Search results for: teaching-learning based optimization

Authors: Djamel Remache, Serge Dos Santos, Michael Cliez, Michel Gratton, Patrick Chabrand, Jean-Marie Rossi, Jean-Louis Milan

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Skin tissue is an inhomogeneous and anisotropic material. Uniaxial tensile testing is one of the primary testing techniques for the mechanical characterization of skin at large scales. In order to predict the mechanical behavior of materials, the direct or inverse analytical approaches are often used. However, in case of an inhomogeneous and anisotropic material as skin tissue, analytical approaches are not able to provide solutions. The numerical simulation is thus necessary. In this work, the uniaxial tensile test and the FEM (finite element method) based inverse method were used to identify the anisotropic mechanical properties of porcine skin tissue. The uniaxial tensile experiments were performed using Instron 8800 tensile machine®. The uniaxial tensile test was simulated with FEM, and then the inverse optimization approach (or the inverse calibration) was used for the identification of mechanical properties of the samples. Experimentally results were compared to finite element solutions. The results showed that the finite element model predictions of the mechanical behavior of the tested skin samples were well correlated with experimental results.

Keywords: mechanical skin tissue behavior, uniaxial tensile test, finite element analysis, inverse optimization approach

Procedia PDF Downloads 408

Authors: Taha Anjamrooz, Sareh Rajabi, Salwa Bheiry

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Selection between the available projects in bidding processes for the contractor is one of the essential areas to concentrate on. It is important for the contractor to choose the right projects within its portfolio during the tendering stage based on certain criteria. It should align the bidding process with its origination strategies and goals as a screening process to have the right portfolio pool to start with. Secondly, it should set the proper framework and use a suitable technique in order to optimize its selection process for concertation purpose and higher efforts during the tender stage with goals of success and winning. In this research paper, a two steps framework proposed to increase the efficiency of the contractor’s bidding process and the winning chance of getting the new projects awarded. In this framework, initially, all the projects pass through the first stage screening process, in which the portfolio basket will be evaluated and adjusted in accordance with the organization strategies to the reduced version of the portfolio pool, which is in line with organization activities. In the second stage, the contractor uses linear programming to optimize the portfolio pool based on available resources such as manpower, light equipment, heavy equipment, financial capability, return on investment, and success rate of winning the bid. Therefore, this optimization model will assist the contractor in utilizing its internal resource to its maximum and increase its winning chance for the new project considering past experience with clients, built-relation between two parties, and complexity in the exertion of the projects. The objective of this research will be to increase the contractor's winning chance in the bidding process based on the success rate and expected return on investment.

Keywords: bidding process, internal resources, optimization, contracting portfolio management

Procedia PDF Downloads 142

Authors: Prakash Kattire, Rahul Pathare, Nilesh Tawde

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A canopy is an overhead roof structure generally used at the entrance of a building to provide shelter from rain and sun and may also be used for decorative purposes. In this paper, the canopy structure to cover the conveyor line has been studied. Existing most of the canopy structures are made of steel and glass, which makes a heavier structure, so the purpose of this study is to weight and cost optimization of the canopy. To achieve this goal, the materials of construction considered are Polyvinyl chloride (PVC) natural composite, Fiber Reinforced Plastic (FRP), and Structural steel Fe250. Designing and modeling were done in Solid works, whereas Altair Inspire software was used for the optimization of the structure. Through this study, it was found that there is a total 10% weight reduction in the structure with sufficient reserve for structural strength.

Keywords: canopy, composite, FRP, PVC

Procedia PDF Downloads 147

Authors: Lydia Novozhilova, Vladimir Urazhdin

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An optimization problem for reinforced thin-walled vessels under uniform external pressure is considered. The conventional approaches to optimization generally start with pre-defined geometric parameters of the vessels, and then employ analytic or numeric calculations and/or experimental testing to verify functionality, such as stability under the projected conditions. The proposed approach consists of two steps. First, the feasibility domain will be identified in the multidimensional parameter space. Every point in the feasibility domain defines a design satisfying both geometric and functional constraints. Second, an objective function defined in this domain is formulated and optimized. The broader applicability of the suggested methodology is maximized by implementing the Support Vector Machines (SVM) classification algorithm of machine learning for identification of the feasible design region. Training data for SVM classifier is obtained using the Simulation package of SOLIDWORKS®. Based on the data, the SVM algorithm produces a curvilinear boundary separating admissible and not admissible sets of design parameters with maximal margins. Then optimization of the vessel parameters in the feasibility domain is performed using the standard algorithms for the constrained optimization. As an example, optimization of a ring-stiffened closed cylindrical thin-walled vessel with semi-spherical caps under high external pressure is implemented. As a functional constraint, von Mises stress criterion is used but any other stability constraint admitting mathematical formulation can be incorporated into the proposed approach. Suggested methodology has a good potential for reducing design time for finding optimal parameters of thin-walled vessels under uniform external pressure.

Keywords: design parameters, feasibility domain, von Mises stress criterion, Support Vector Machine (SVM) classifier

Procedia PDF Downloads 327

Authors: Sai Siddharth S., Prasanna Kumar G. M., Alagarsamy R.

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This research introduces an alternative approach to urban road maintenance by utilizing Blended Wing Body (BWB) design and Vertical Takeoff and Landing (VTOL) drones. The integration of this aerospace innovation, combining blended wing efficiency with VTOL maneuverability, aims to optimize fuel consumption and explore versatile applications in solving urban problems. A few problems are discussed along with optimization of the design and comparative study with other drone configurations.

Keywords: design optimization, CFD, CAD, VTOL, blended wing body

Procedia PDF Downloads 97

Authors: Meziane Rachid, Boufala Seddik, Hamzi Amar, Amara Mohamed

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Today’s energy industry seeks maximum benefit with maximum reliability. In order to achieve this goal, design engineers depend on reliability optimization techniques. This work uses a harmony search algorithm (HS) meta-heuristic optimization method to solve the problem of wind-Solar-Gas power systems design optimization. We consider the case where redundant electrical components are chosen to achieve a desirable level of reliability. The electrical power components of the system are characterized by their cost, capacity and reliability. The reliability is considered in this work as the ability to satisfy the consumer demand which is represented as a piecewise cumulative load curve. This definition of the reliability index is widely used for power systems. The proposed meta-heuristic seeks for the optimal design of series-parallel power systems in which a multiple choice of wind generators, transformers and lines are allowed from a list of product available in the market. Our approach has the advantage to allow electrical power components with different parameters to be allocated in electrical power systems. To allow fast reliability estimation, a universal moment generating function (UMGF) method is applied. A computer program has been developed to implement the UMGF and the HS algorithm. An illustrative example is presented.

Keywords: reliability optimization, harmony search optimization (HSA), universal generating function (UMGF)

Procedia PDF Downloads 576

Authors: Hany Mohamed Abdu, Mohamed Hassan Gadallah, El-Giushi Mokhtar, Yehia Mahmoud Ismail

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Laser beam cutting is one nontraditional machining process. This paper optimizes the parameters of Laser beam cutting machining parameters of Stainless steel (316L) by considering the effect of input parameters viz. power, oxygen pressure, frequency and cutting speed. Statistical design of experiments are carried in three different levels and process responses such as 'Average kerf taper (Ta)' and 'Surface Roughness (Ra)' are measured accordingly. A quadratic mathematical model (RSM) for each of the responses is developed as a function of the process parameters. Responses predicted by the models (as per Taguchi’s L27 OA) are employed to search for an optimal parametric combination to achieve desired yield of the process. RSM models are developed for mean responses, S/N ratio, and standard deviation of responses. Optimization models are formulated as single objective problem subject to process constraints. Models are formulated based on Analysis of Variance (ANOVA) using MATLAB environment. Optimum solutions are compared with Taguchi Methodology results.

Keywords: optimization, laser cutting, robust design, kerf width, Taguchi method, RSM and DOE

Procedia PDF Downloads 620

Authors: Rizqi W. Romadhon, Nur Arifan

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Halal Tourism is a part of tourism industry which is based on Islamic Principle and addressed to the Muslim tourist. The potency of halal tourism is very broad to be developed, because the growth of Muslim populations is rapidly increasing. Indonesia is one of the biggest countries with Majority of its population is Muslim, therefore human resources and natural resources have very good potential to be part of the Halal tourism industry. But the fact is Indonesia can not optimize the potential of human resources and natural resources as well as neighboring countries carried out. This paper will discuss the reasons of the importance of developing Halal tourism, and the factors influencing the success of developing halal tourism in Indonesia, and also the optimization strategies which can be adopted by the government so that the Halal tourism industry in Indonesia has a sustainable competitive advantage. The existence of this research is expected to government, tourism agents and others can optimize the potency of Indonesia’s Human resources and natural resources for developing Halal tourism industry in Indonesia.

Keywords: halal tourism, Islamic principle, optimization, sustainable competitive advantage

Procedia PDF Downloads 384

Authors: Paffrath Meinhard, Zhou Yayun, Guo Yiqing, Ertl Harald

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Winding geometry design is an important part of power transformer electrical design. Conventionally, the winding geometry is designed manually, which is a time-consuming job because it involves many iteration steps in order to meet all cost, manufacturing and electrical requirements. Here a method is presented which automatically generates the winding geometry for given user parameters and allows the user to interactively set and change parameters. To achieve this goal, the winding problem is transferred to a mixed integer nonlinear optimization problem. The relevant geometrical design parameters are defined as optimization variables. The cost and other requirements are modeled as constraints. For the solution, a stochastic ant colony optimization algorithm is applied. It is well-known, that an optimizer can get stuck in a local minimum. For the winding problem, we present efficient strategies to come out of local minima, furthermore a reduced variable search range helps to accelerate the solution process. Numerical examples show that the optimization result is delivered within seconds such that the user can interactively change the variable search area and constraints to improve the design.

Keywords: ant colony optimization, mixed integer nonlinear programming, power transformer, winding design

Procedia PDF Downloads 380

Authors: Sultan Noman Qasem

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This paper presents an evolutionary algorithm for solving multi-objective optimization problems-based artificial neural network (ANN). The multi-objective evolutionary algorithm used in this study is genetic algorithm while ANN used is radial basis function network (RBFN). The proposed algorithm named memetic elitist Pareto non-dominated sorting genetic algorithm-based RBFNN (MEPGAN). The proposed algorithm is implemented on medical diseases problems. The experimental results indicate that the proposed algorithm is viable, and provides an effective means to design multi-objective RBFNs with good generalization capability and compact network structure. This study shows that MEPGAN generates RBFNs coming with an appropriate balance between accuracy and simplicity, comparing to the other algorithms found in literature.

Keywords: radial basis function network, hybrid learning, multi-objective optimization, genetic algorithm

Procedia PDF Downloads 563

Authors: Laidi Maamar, Achwak Madani, Abdellah El Ahdj Abdellah

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The aim of this work is to use Support Vector regression (SVR) combined with dragonfly, firefly, Bee Colony and particle swarm Optimization algorithm to predict global solar radiation on horizontal surfaces in some cities in Algeria. Combining these optimization algorithms with SVR aims principally to enhance accuracy by fine-tuning the parameters, speeding up the convergence of the SVR model, and exploring a larger search space efficiently; these parameters are the regularization parameter (C), kernel parameters, and epsilon parameter. By doing so, the aim is to improve the generalization and predictive accuracy of the SVR model. Overall, the aim is to leverage the strengths of both SVR and optimization algorithms to create a more powerful and effective regression model for various cities and under different climate conditions. Results demonstrate close agreement between predicted and measured data in terms of different metrics. In summary, SVM has proven to be a valuable tool in modeling global solar radiation, offering accurate predictions and demonstrating versatility when combined with other algorithms or used in hybrid forecasting models.

Keywords: support vector regression (SVR), optimization algorithms, global solar radiation prediction, hybrid forecasting models

Procedia PDF Downloads 35

Authors: Mourad Hrizi

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In this paper, we study the inverse problem of reconstructing an interior interface D appearing in the elliptic partial differential equation: Δu+χ(D)u=0 from the knowledge of the boundary measurements. This problem arises from a semiconductor transistor model. We propose a new shape reconstruction procedure that is based on the Kohn-Vogelius formulation and the topological sensitivity method. The inverse problem is formulated as a topology optimization one. A topological sensitivity analysis is derived from a function. The unknown subdomain D is reconstructed using a level-set curve of the topological gradient. Finally, we give several examples to show the viability of our proposed method.

Keywords: inverse problem, topological optimization, topological gradient, Kohn-Vogelius formulation

Procedia PDF Downloads 244

Authors: Yue Hu, Xilu Zhao, Takao Yamaguchi, Manabu Sasajima, Yoshio Koike, Akira Hara

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This study tried optimization of design parameter of a cone loudspeaker unit as an example of the high flexibility of the products design. We developed an acoustic analysis software program that considers the impact of damping caused by air viscosity. In sound reproduction, it is difficult to each design the parameter of the loudspeaker. To overcome the limitation of the design problem in practice, this paper proposes a new an acoustic analysis algorithm to optimize design the parameter of the loudspeaker. The material character of cone paper and the loudspeaker edge was the design parameter, and the vibration displacement of the cone paper was the objective function. The results of the analysis were compared with the predicted value. They had high accuracy to the predicted value. These results suggest that, though the parameter design is difficult by experience and intuition, it can be performed comparatively easily using the optimization design by the developed acoustic analysis software.

Keywords: air viscosity, loudspeaker, cone paper, edge, optimization

Procedia PDF Downloads 401

Authors: Thomas Weber, Nina Strobel, Thomas Kohne, Eberhard Abele

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In context of the energy transition in Germany, the importance of so-called virtual power plants in the energy supply continues to increase. The progressive dismantling of the large power plants and the ongoing construction of many new decentralized plants result in great potential for optimization through synergies between the individual plants. These potentials can be exploited by mathematical optimization algorithms to calculate the optimal application planning of decentralized power and heat generators and storage systems. This also includes linear or linear mixed integer optimization. In this paper, procedures for reducing the number of decision variables to be calculated are explained and validated. On the one hand, this includes combining n similar installation types into one aggregated unit. This aggregated unit is described by the same constraints and target function terms as a single plant. This reduces the number of decision variables per time step and the complexity of the problem to be solved by a factor of n. The exact operating mode of the individual plants can then be calculated in a second optimization in such a way that the output of the individual plants corresponds to the calculated output of the aggregated unit. Another way to reduce the number of decision variables in an optimization problem is to reduce the number of time steps to be calculated. This is useful if a high temporal resolution is not necessary for all time steps. For example, the volatility or the forecast quality of environmental parameters may justify a high or low temporal resolution of the optimization. Both approaches are examined for the resulting calculation time as well as for optimality. Several optimization models for virtual power plants (combined heat and power plants, heat storage, power storage, gas turbine) with different numbers of plants are used as a reference for the investigation of both processes with regard to calculation duration and optimality.

Keywords: CHP, Energy 4.0, energy storage, MILP, optimization, virtual power plant

Procedia PDF Downloads 178

Authors: Alae Lamtai, Said Elkoun, Hniya Kharmoudi, Mathieu Robert, Carl Diez

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This study applies Taguchi's design of experiment methodology and grey relational analysis (GRA) for multi objective optimization of an industrial recycling line. This last is composed mainly of a mono and twin-screw extruder and a filtration system. Experiments were performed according to L₁₆ standard orthogonal array based on five process parameters, namely: mono screw design, screw speed of the mono and twin-screw extruder, melt pump pressure, and filter mesh size. The objective of this optimization is to improve the durability of the Polyethylene (PE) blend by decreasing the loss of Stress Crack resistance (SCR) using Notched Crack Ligament Stress (NCLS) test and Unnotched Crack Ligament Stress (UCLS) in parallel with increasing the gain of Izod impact strength of the Polyethylene (PE) blend before and after recycling. Based on Grey Relational Analysis (GRA), the optimal setting of process parameters was identified, and the results indicated that the mono-screw design and screw speed of both mono and twin-screw extruder impact significantly the mechanical properties of recycled Polyethylene (PE) blend.

Keywords: Taguchi, recycling line, polyethylene, stress crack resistance, Izod impact strength, grey relational analysis

Procedia PDF Downloads 83

Authors: Alireza Nikbakht Shahbazi, Emadeddin Shirali

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Since optimization issues of water resources are complicated due to the variety of decision making criteria and objective functions, it is sometimes impossible to resolve them through regular optimization methods or, it is time or money consuming. Therefore, the use of modern tools and methods is inevitable in resolving such problems. An accurate and essential utilization policy has to be determined in order to use natural resources such as water reservoirs optimally. Water reservoir programming studies aim to determine the final cultivated land area based on predefined agricultural models and water requirements. Dam utilization rule curve is also provided in such studies. The basic information applied in water reservoir programming studies generally include meteorological, hydrological, agricultural and water reservoir related data, and the geometric characteristics of the reservoir. The system of Dez dam water resources was simulated applying the basic information in order to determine the capability of its reservoir to provide the objectives of the performed plan. As a meta-exploratory method, genetic algorithm was applied in order to provide utilization rule curves (intersecting the reservoir volume). MATLAB software was used in order to resolve the foresaid model. Rule curves were firstly obtained through genetic algorithm. Then the significance of using rule curves and the decrease in decision making variables in the system was determined through system simulation and comparing the results with optimization results (Standard Operating Procedure). One of the most essential issues in optimization of a complicated water resource system is the increasing number of variables. Therefore a lot of time is required to find an optimum answer and in some cases, no desirable result is obtained. In this research, intersecting the reservoir volume has been applied as a modern model in order to reduce the number of variables. Water reservoir programming studies has been performed based on basic information, general hypotheses and standards and applying monthly simulation technique for a statistical period of 30 years. Results indicated that application of rule curve prevents the extreme shortages and decrease the monthly shortages.

Keywords: optimization, rule curve, genetic algorithm method, Dez dam reservoir

Procedia PDF Downloads 265

Authors: H. Nikzad, S. Yoshitomi

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In this paper, the optimal seismic design of reinforced concrete shear wall-frame building structures was done using structural optimization. The optimal section sizes were generated through structural optimization based on linear static analysis conforming to American Concrete Institute building design code (ACI 318-14). An analytical procedure was followed to validate the accuracy of the proposed method by comparing stresses on structural members through output files of MATLAB and ETABS. In order to consider the difference of stresses in structural elements by ETABS and MATLAB, and to avoid over-stress members by ETABS, a stress constraint ratio of MATLAB to ETABS was modified and introduced for the most critical load combinations and structural members. Moreover, seismic design of the structure was done following the International Building Code (IBC 2012), American Concrete Institute Building Code (ACI 318-14) and American Society of Civil Engineering (ASCE 7-10) standards. Typical reinforcement requirements for the structural wall, beam and column were discussed and presented using ETABS structural analysis software. The placement and detailing of reinforcement of structural members were also explained and discussed. The outcomes of this study show that the modification of section sizes play a vital role in finding an optimal combination of practical section sizes. In contrast, the optimization problem with size constraints has a higher cost than that of without size constraints. Moreover, the comparison of optimization problem with that of ETABS program shown to be satisfactory and governed ACI 318-14 building design code criteria.

Keywords: structural optimization, seismic design, linear static analysis, etabs, matlab, rc shear wall-frame structures

Procedia PDF Downloads 173

Authors: Oğuzhan Hasançebi, Saeid Kazemzadeh Azad

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Keywords: structural design optimization, optimal sizing, metaheuristics, self-adaptive step-size search, steel trusses, steel frames

Procedia PDF Downloads 375

Authors: R. Rajabpour, N. Talebbeydokhti, M. H. Ahmadi

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Carefully scheduling the operations of pumps can be resulted to significant energy savings. Schedules can be defined either implicit, in terms of other elements of the network such as tank levels, or explicit by specifying the time during which each pump is on/off. In this study, two new explicit representations based on time-controlled triggers were analyzed, where the maximum number of pump switches was established beforehand, and the schedule may contain fewer switches than the maximum. The optimal operation of pumping stations was determined using a Jumping Particle Swarm Optimization (JPSO) algorithm to achieve the minimum energy cost. The model integrates JPSO optimizer and EPANET hydraulic network solver. The optimal pump operation schedule of VanZyl water distribution system was determined using the proposed model and compared with those from Genetic and Ant Colony algorithms. The results indicate that the proposed model utilizing the JPSP algorithm outperformed the others and is a versatile management model for the operation of real-world water distribution system.

Keywords: JPSO, operation, optimization, water distribution system

Procedia PDF Downloads 245

Authors: Yuhan Lin, Rohan Kekatpure, Cassidy Yeung

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In Digital Marketing, robust quantification of View-through attribution (VTA) is necessary for evaluating channel effectiveness. VTA occurs when a product purchase is aided by an Ad but without an explicit click (e.g. a TV ad). A lack of a tracking mechanism makes VTA estimation challenging. Most prevalent VTA estimation techniques rely on post-purchase in-product user surveys. User surveys enable the calculation of channel multipliers, which are the ratio of the view-attributed to the click-attributed purchases of each marketing channel. Channel multipliers thus provide a way to estimate the unknown VTA for a channel from its known click attribution. In this work, we use Convex Optimization to compute channel multipliers in a way that enables a mathematical encoding of the expected channel behavior. Large fluctuations in channel attributions often result from overfitting the calculations to user surveys. Casting channel attribution as a Convex Optimization problem allows an introduction of constraints that limit such fluctuations. The result of our study is a distribution of channel multipliers across the entire marketing funnel, with important implications for marketing spend optimization. Our technique can be broadly applied to estimate Ad effectiveness in a privacy-centric world that increasingly limits user tracking.

Keywords: digital marketing, survey analysis, operational research, convex optimization, channel attribution

Procedia PDF Downloads 199

Authors: Abdullah Ali H. Ahmadini, Firoz Ahmad, Intekhab Alam

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Geometric programming problem (GPP) is a well-known non-linear optimization problem having a wide range of applications in many engineering problems. The structure of GPP is quite dynamic and easily fit to the various decision-making processes. The aim of this paper is to highlight the bounded solution method for GPP with special reference to variation among right-hand side parameters. Thus this paper is taken the advantage of two-level mathematical programming problems and determines the solution of the objective function in a specified interval called lower and upper bounds. The beauty of the proposed bounded solution method is that it does not require sensitivity analyses of the obtained optimal solution. The value of the objective function is directly calculated under varying parameters. To show the validity and applicability of the proposed method, a numerical example is presented. The system reliability optimization problem is also illustrated and found that the value of the objective function lies between the range of lower and upper bounds, respectively. At last, conclusions and future research are depicted based on the discussed work.

Keywords: varying parameters, geometric programming problem, bounded solution method, system reliability optimization

Procedia PDF Downloads 133

Authors: Chung To Kong, Siu Ming Yiu

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Rubik's cube was invented in 1974. Since then, speedcubers all over the world try their best to break the world record again and again. The newest record is 3.47 seconds. There are many factors that affect the timing, including turns per second (tps), algorithm, finger trick, hardware of the cube. In this paper, the lower bound of the cube solving time will be discussed using convex optimization. Extended analysis of the world records will be used to understand how to improve the timing. With the understanding of each part of the solving step, the paper suggests a list of speed improvement techniques. Based on the analysis of the world record, there is a high possibility that the 3 seconds mark will be broken soon.

Keywords: Rubik's Cube, speed, finger trick, optimization

Procedia PDF Downloads 207

Authors: Francesca Schiavello

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This work aims to create a hybrid algorithm, combining Quantum Approximate Optimization Algorithm (QAOA) with an Evolutionary Algorithm (EA) in the place of traditional gradient based optimization processes. QAOA’s were first introduced in 2014, where, at the time, their algorithm performed better than the traditional best known classical algorithm for Max-cut graphs. Whilst classical algorithms have improved since then and have returned to being faster and more efficient, this was a huge milestone for quantum computing, and their work is often used as a benchmarking tool and a foundational tool to explore variants of QAOA’s. This, alongside with other famous algorithms like Grover’s or Shor’s, highlights to the world the potential that quantum computing holds. It also presents the reality of a real quantum advantage where, if the hardware continues to improve, this could constitute a revolutionary era. Given that the hardware is not there yet, many scientists are working on the software side of things in the hopes of future progress. Some of the major limitations holding back quantum computing are the quality of qubits and the noisy interference they generate in creating solutions, the barren plateaus that effectively hinder the optimization search in the latent space, and the availability of number of qubits limiting the scale of the problem that can be solved. These three issues are intertwined and are part of the motivation for using EAs in this work. Firstly, EAs are not based on gradient or linear optimization methods for the search in the latent space, and because of their freedom from gradients, they should suffer less from barren plateaus. Secondly, given that this algorithm performs a search in the solution space through a population of solutions, it can also be parallelized to speed up the search and optimization problem. The evaluation of the cost function, like in many other algorithms, is notoriously slow, and the ability to parallelize it can drastically improve the competitiveness of QAOA’s with respect to purely classical algorithms. Thirdly, because of the nature and structure of EA’s, solutions can be carried forward in time, making them more robust to noise and uncertainty. Preliminary results show that the EA algorithm attached to QAOA can perform on par with the traditional QAOA with a Cobyla optimizer, which is a linear based method, and in some instances, it can even create a better Max-Cut. Whilst the final objective of the work is to create an algorithm that can consistently beat the original QAOA, or its variants, due to either speedups or quality of the solution, this initial result is promising and show the potential of EAs in this field. Further tests need to be performed on an array of different graphs with the parallelization aspect of the work commencing in October 2023 and tests on real hardware scheduled for early 2024.

Keywords: evolutionary algorithm, max cut, parallel simulation, quantum optimization

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Authors: Mohamed Mousa

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Sacristies in the supply of freshwater, strict regulations on discharging wastewater and the support to encourage sustainable development by water minimization techniques leads to raise the interest of water reusing, regeneration, and recycling. Water is considered a vital element in chemical industries. In this study, an optimization model will be developed to determine the optimal design of refinery’s water network system via source interceptor sink that involves several network alternatives, then a Mixed-Integer Non-Linear programming (MINLP) was used to obtain the optimal network superstructure based on flowrates, the concentration of contaminants, etc. The main objective of the model is to reduce the fixed cost of piping installation interconnections, reducing the operating cots of all streams within the refiner’s water network, and minimize the concentration of pollutants to comply with the environmental regulations. A real case study for one of the Egyptian refineries was studied by GAMS / BARON global optimization platform, and the water network had been retrofitted and optimized, leading to saving around 195 m³/ hr. of freshwater with a total reduction reaches to 26 %.

Keywords: freshwater minimization, modelling, GAMS, BARON, water network design, wastewater reudction

Procedia PDF Downloads 232

Authors: Zahid Ullah, Atlas Khan

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This abstract focuses on the advancements in mathematical modeling and optimization techniques that play a crucial role in enhancing the efficiency, reliability, and performance of these systems. In this era of rapidly evolving technology, mathematical modeling and optimization offer powerful tools to tackle the complex challenges faced by control, signal processing, and energy systems. This abstract presents the latest research and developments in mathematical methodologies, encompassing areas such as control theory, system identification, signal processing algorithms, and energy optimization. The abstract highlights the interdisciplinary nature of mathematical modeling and optimization, showcasing their applications in a wide range of domains, including power systems, communication networks, industrial automation, and renewable energy. It explores key mathematical techniques, such as linear and nonlinear programming, convex optimization, stochastic modeling, and numerical algorithms, that enable the design, analysis, and optimization of complex control and signal processing systems. Furthermore, the abstract emphasizes the importance of addressing real-world challenges in control, signal processing, and energy systems through innovative mathematical approaches. It discusses the integration of mathematical models with data-driven approaches, machine learning, and artificial intelligence to enhance system performance, adaptability, and decision-making capabilities. The abstract also underscores the significance of bridging the gap between theoretical advancements and practical applications. It recognizes the need for practical implementation of mathematical models and optimization algorithms in real-world systems, considering factors such as scalability, computational efficiency, and robustness. In summary, this abstract showcases the advancements in mathematical modeling and optimization techniques for control, signal processing, and energy systems. It highlights the interdisciplinary nature of these techniques, their applications across various domains, and their potential to address real-world challenges. The abstract emphasizes the importance of practical implementation and integration with emerging technologies to drive innovation and improve the performance of control, signal processing, and energy.

Keywords: mathematical modeling, optimization, control systems, signal processing, energy systems, interdisciplinary applications, system identification, numerical algorithms

Procedia PDF Downloads 112

Authors: Sukant Mehra, Parth Gupta, Varun Arora, Sarvoday Singh, Amit Kohli

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The study was emphasised on dry sliding wear behavior of AISI 1042 steel. Dry sliding wear tests were performed using pin-on-disk apparatus under normal loads of 5, 7.5 and 10 kgf and at speeds 600, 750 and 900 rpm. Response surface methodology (RSM) was utilized for finding optimal values of process parameter and experiment was based on rotatable, central composite design (CCD). It was found that the wear followed linear pattern with the load and rpm. The obtained optimal process parameters have been predicted and verified by confirmation experiments.

Keywords: central composite design (CCD), optimization, response surface methodology (RSM), wear

Procedia PDF Downloads 577

Authors: Seyed Esmail Seyedi Bariran, Khairul Salleh Mohamed Sahari

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Apparent quality defects such as warpage, shrinkage, weld line, etc. are such an irresistible phenomenon in mass production of auto plastic appearance parts. These frequently occurred manufacturing defects should be satisfied concurrently so as to achieve a final product with acceptable quality standards. Determining the significant control factors that simultaneously affect multiple quality characteristics can significantly improve the optimization results by eliminating the deviating effect of the so-called ineffective outliers. Hence, a robust quantitative approach needs to be developed upon which major control factors and their level can be effectively determined to help improve the reliability of the optimal processing parameter design. Hence, the primary objective of current study was to develop a systematic methodology for selection of significant control factors (SCF) relevant to multiple quality optimization of auto plastic appearance part. Auto bumper was used as a specimen with the most identical quality and production characteristics to APAP group. A preliminary failure modes and effect analysis (FMEA) was conducted to nominate a database of pseudo significant significant control factors prior to the optimization phase. Later, CAE simulation Moldflow analysis was implemented to manipulate four rampant plastic injection quality defects concerned with APAP group including warpage deflection, volumetric shrinkage, sink mark and weld line. Furthermore, a step-backward elimination searching method (SESME) has been developed for systematic pre-optimization selection of SCF based on hierarchical orthogonal array design and priority-based one-way analysis of variance (ANOVA). The development of robust parameter design in the second phase was based on DOE module powered by Minitab v.16 statistical software. Based on the F-test (F 0.05, 2, 14) one-way ANOVA results, it was concluded that for warpage deflection, material mixture percentage was the most significant control factor yielding a 58.34% of contribution while for the other three quality defects, melt temperature was the most significant control factor with a 25.32%, 84.25%, and 34.57% contribution for sin mark, shrinkage and weld line strength control. Also, the results on the he least significant control factors meaningfully revealed injection fill time as the least significant factor for both warpage and sink mark with respective 1.69% and 6.12% contribution. On the other hand, for shrinkage and weld line defects, the least significant control factors were holding pressure and mold temperature with a 0.23% and 4.05% overall contribution accordingly.

Keywords: plastic injection moulding, quality optimization, FMEA, ANOVA, SESME, APAP

Procedia PDF Downloads 349

Authors: Bale Baidi Blaise, Gilles Marckmann, Liman Kaoye, Talaka Dya, Moustapha Bachirou, Gambo Betchewe, Tibi Beda

Abstract:

This work highlights the capabilities of particles swarm optimization (PSO) method to identify parameters of hyperelastic models. The study compares this method with Genetic Algorithm (GA) method, Least Squares (LS) method, Pattern Search Algorithm (PSA) method, Beda-Chevalier (BC) method and the Levenberg-Marquardt (LM) method. Four classic hyperelastic models are used to test the different methods through parameters identification. Then, the study compares the ability of these models to reproduce experimental Treloar data in simple tension, biaxial tension and pure shear.

Keywords: particle swarm optimization, identification, hyperelastic, model

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Authors: Mostafa Mjahed

Abstract:

Tilt Rotor UAVs (Unmanned Aerial Vehicles) are naturally unstable and difficult to maneuver. The purpose of this paper is to design controllers for the stabilization and trajectory tracking of this type of UAV. To this end, artificial intelligence methods have been exploited. First, the dynamics of this UAV was modeled using the Lagrange-Euler method. The conventional method based on Proportional, Integral and Derivative (PID) control was applied by decoupling the different flight modes. To improve stability and trajectory tracking of the Tilt Rotor, the fuzzy approach and the technique of multilayer neural networks (NN) has been used. Thus, Fuzzy Proportional Integral and Derivative (FPID) and Neural Network-based Proportional Integral and Derivative controllers (NNPID) have been developed. The meta-heuristic approach based on Particle Swarm Optimization (PSO) method allowed adjusting the setting parameters of NNPID controller, giving us an improved NNPID-PSO controller. Simulation results under the Matlab environment show the efficiency of the approaches adopted. Besides, the Tilt Rotor UAV has become stable and follows different types of trajectories with acceptable precision. The Fuzzy, NN and NN-PSO-based approaches demonstrated their robustness because the presence of the disturbances did not alter the stability or the trajectory tracking of the Tilt Rotor UAV.

Keywords: neural network, fuzzy logic, PSO, PID, trajectory tracking, tilt-rotor UAV

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Authors: Marius Gavrilescu, Sabina-Adriana Floria, Florin Leon, Silvia Curteanu, Costel Anton

Abstract:

Natural sciences provide a wide range of experimental data whose related problems require study and modeling beyond the capabilities of conventional methodologies. Such problems have solution spaces whose complexity and high dimensionality require correspondingly complex regression methods for proper characterization. In this context, we propose an optimization method which consists in a hybrid dual optimizer setup: a global optimizer based on a modified variant of the popular Imperialist Competitive Algorithm (ICA), and a local optimizer based on a gradient descent approach. The ICA is modified such that intermediate solution populations are more quickly and efficiently pruned of low-fitness individuals by appropriately altering the assimilation, revolution and competition phases, which, combined with an initialization strategy based on low-discrepancy sampling, allows for a more effective exploration of the corresponding solution space. Subsequently, gradient-based optimization is used locally to seek the optimal solution in the neighborhoods of the solutions found through the modified ICA. We use this combined approach to find the optimal configuration and weights of a fully-connected neural network, resulting in regression models used to characterize the process of obtained bricks using silicon-based materials. Installations in the raw ceramics industry, i.e., bricks, are characterized by significant energy consumption and large quantities of emissions. Thus, the purpose of our approach is to determine by simulation the working conditions, including the manufacturing mix recipe with the addition of different materials, to minimize the emissions represented by CO and CH4. Our approach determines regression models which perform significantly better than those found using the traditional ICA for the aforementioned problem, resulting in better convergence and a substantially lower error.

Keywords: optimization, biologically inspired algorithm, regression models, bricks, emissions

Procedia PDF Downloads 82