Search results for: fruit fly optimization algorithm
6509 Optimization of Passive Vibration Damping of Space Structures
Authors: Emad Askar, Eldesoky Elsoaly, Mohamed Kamel, Hisham Kamel
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The objective of this article is to improve the passive vibration damping of solar array (SA) used in space structures, by the effective application of numerical optimization. A case study of a SA is used for demonstration. A finite element (FE) model was created and verified by experimental testing. Optimization was then conducted by implementing the FE model with the genetic algorithm, to find the optimal placement of aluminum circular patches, to suppress the first two bending mode shapes. The results were verified using experimental testing. Finally, a parametric study was conducted using the FE model where patch locations, material type, and shape were varied one at a time, and the results were compared with the optimal ones. The results clearly show that through the proper application of FE modeling and numerical optimization, passive vibration damping of space structures has been successfully achieved.Keywords: damping optimization, genetic algorithm optimization, passive vibration damping, solar array vibration damping
Procedia PDF Downloads 4426508 Inverse Mapping of Weld Bead Geometry in Shielded Metal Arc-Welding: Genetic Algorithm Approach
Authors: D. S. Nagesh, G. L. Datta
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In the field of welding, various studies had been made by some of the previous investigators to predict as well as optimize weld bead geometric descriptors. Modeling of weld bead shape is important for predicting the quality of welds. In most of the cases, design of experiments technique to postulate multiple linear regression equations have been used. Nowadays, Genetic Algorithm (GA) an intelligent information treatment system with the characteristics of treating complex relationships as seen in welding processes used as a tool for inverse mapping/optimization of the process is attempted.Keywords: smaw, genetic algorithm, bead geometry, optimization/inverse mapping
Procedia PDF Downloads 4476507 Genetic Algorithm Approach for Inverse Mapping of Weld Bead Geometry in Shielded Metal Arc-Welding
Authors: D. S. Nagesh, G. L. Datta
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In the field of welding, various studies had been made by some of the previous investigators to predict as well as optimize weld bead geometric descriptors. Modeling of weld bead shape is important for predicting the quality of welds. In most of the cases design of experiments technique to postulate multiple linear regression equations have been used. Nowadays Genetic Algorithm (GA) an intelligent information treatment system with the characteristics of treating complex relationships as seen in welding processes used as a tool for inverse mapping/optimization of the process is attempted.Keywords: SMAW, genetic algorithm, bead geometry, optimization/inverse mapping
Procedia PDF Downloads 4176506 A Multi-Population DE with Adaptive Mutation and Local Search for Global Optimization
Authors: Zhoucheng Bao, Haiyan Zhu, Tingting Pang, Zuling Wang
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This paper proposes a multi-population DE with adaptive mutation and local search for global optimization, named AMMADE. In order to better coordinate the cooperation between the populations and the rational use of resources. In AMMADE, the population is divided based on the Euclidean distance sorting method at each generation to appropriately coordinate the cooperation between subpopulations and the usage of resources, such that the best-performed subpopulation will get more computing resources in the next generation. Further, an adaptive local search strategy is employed on the best-performed subpopulation to achieve a balanced search. The proposed algorithm has been tested by solving optimization problems taken from CEC2014 benchmark problems. Experimental results show that our algorithm can achieve a competitive or better than related methods. The results also confirm the significance of devised strategies in the proposed algorithm.Keywords: differential evolution, multi-mutation strategies, memetic algorithm, adaptive local search
Procedia PDF Downloads 1486505 An Improved Discrete Version of Teaching–Learning-Based Optimization for Supply Chain Network Design
Authors: Ehsan Yadegari
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While there are several metaheuristics and exact approaches to solving the Supply Chain Network Design (SCND) problem, there still remains an unfilled gap in using the Teaching-Learning-Based Optimization (TLBO) algorithm. The algorithm has demonstrated desirable results with problems with complicated combinational optimization. The present study introduces a Discrete Self-Study TLBO (DSS-TLBO) with priority-based solution representation that can solve a supply chain network configuration model to lower the total expenses of establishing facilities and the flow of materials. The network features four layers, namely suppliers, plants, distribution centers (DCs), and customer zones. It is designed to meet the customer’s demand through transporting the material between layers of network and providing facilities in the best economic Potential locations. To have a higher quality of the solution and increase the speed of TLBO, a distinct operator was introduced that ensures self-adaptation (self-study) in the algorithm based on the four types of local search. In addition, while TLBO is used in continuous solution representation and priority-based solution representation is discrete, a few modifications were added to the algorithm to remove the solutions that are infeasible. As shown by the results of experiments, the superiority of DSS-TLBO compared to pure TLBO, genetic algorithm (GA) and firefly Algorithm (FA) was established.Keywords: supply chain network design, teaching–learning-based optimization, improved metaheuristics, discrete solution representation
Procedia PDF Downloads 456504 Solving Flowshop Scheduling Problems with Ant Colony Optimization Heuristic
Authors: Arshad Mehmood Ch, Riaz Ahmad, Imran Ali Ch, Waqas Durrani
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This study deals with the application of Ant Colony Optimization (ACO) approach to solve no-wait flowshop scheduling problem (NW-FSSP). ACO algorithm so developed has been coded on Matlab computer application. The paper covers detailed steps to apply ACO and focuses on judging the strength of ACO in relation to other solution techniques previously applied to solve no-wait flowshop problem. The general purpose approach was able to find reasonably accurate solutions for almost all the problems under consideration and was able to handle a fairly large spectrum of problems with far reduced CPU effort. Careful scrutiny of the results reveals that the algorithm presented results better than other approaches like Genetic algorithm and Tabu Search heuristics etc; earlier applied to solve NW-FSSP data sets.Keywords: no-wait, flowshop, scheduling, ant colony optimization (ACO), makespan
Procedia PDF Downloads 4276503 Study of the Effect of Inclusion of TiO2 in Active Flux on Submerged Arc Welding of Low Carbon Mild Steel Plate and Parametric Optimization of the Process by Using DEA Based Bat Algorithm
Authors: Sheetal Kumar Parwar, J. Deb Barma, A. Majumder
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Submerged arc welding is a very complex process. It is a very efficient and high performance welding process. In this present study an attempt have been done to reduce the welding distortion by increased amount of oxide flux through TiO2 in submerged arc welding process. Care has been taken to avoid the excessiveness of the adding agent for attainment of significant results. Data Envelopment Analysis (DEA) based BAT algorithm is used for the parametric optimization purpose in which DEA Data Envelopment Analysis is used to convert multi response parameters into a single response parameter. The present study also helps to know the effectiveness of the addition of TiO2 in active flux during submerged arc welding process.Keywords: BAT algorithm, design of experiment, optimization, submerged arc welding
Procedia PDF Downloads 6306502 Optimal Design of Concrete Shells by Modified Particle Community Algorithm Using Spinless Curves
Authors: Reza Abbasi, Ahmad Hamidi Benam
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Shell structures have many geometrical variables that modify some of these parameters to improve the mechanical behavior of the shell. On the other hand, the behavior of such structures depends on their geometry rather than on mass. Optimization techniques are useful in finding the geometrical shape of shell structures to improve mechanical behavior, especially to prevent or reduce bending anchors. The overall objective of this research is to optimize the shape of concrete shells using the thickness and height parameters along the reference curve and the overall shape of this curve. To implement the proposed scheme, the geometry of the structure was formulated using nonlinear curves. Shell optimization was performed under equivalent static loading conditions using the modified bird community algorithm. The results of this optimization show that without disrupting the initial design and with slight changes in the shell geometry, the structural behavior is significantly improved.Keywords: concrete shells, shape optimization, spinless curves, modified particle community algorithm
Procedia PDF Downloads 2266501 Particle Filter State Estimation Algorithm Based on Improved Artificial Bee Colony Algorithm
Authors: Guangyuan Zhao, Nan Huang, Xuesong Han, Xu Huang
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In order to solve the problem of sample dilution in the traditional particle filter algorithm and achieve accurate state estimation in a nonlinear system, a particle filter method based on an improved artificial bee colony (ABC) algorithm was proposed. The algorithm simulated the process of bee foraging and optimization and made the high likelihood region of the backward probability of particles moving to improve the rationality of particle distribution. The opposition-based learning (OBL) strategy is introduced to optimize the initial population of the artificial bee colony algorithm. The convergence factor is introduced into the neighborhood search strategy to limit the search range and improve the convergence speed. Finally, the crossover and mutation operations of the genetic algorithm are introduced into the search mechanism of the following bee, which makes the algorithm jump out of the local extreme value quickly and continue to search the global extreme value to improve its optimization ability. The simulation results show that the improved method can improve the estimation accuracy of particle filters, ensure the diversity of particles, and improve the rationality of particle distribution.Keywords: particle filter, impoverishment, state estimation, artificial bee colony algorithm
Procedia PDF Downloads 1386500 Optimization of Steel Moment Frame Structures Using Genetic Algorithm
Authors: Mohammad Befkin, Alireza Momtaz
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Structural design is the challenging aspect of every project due to limitations in dimensions, functionality of the structure, and more importantly, the allocated budget for construction. This research study aims to investigate the optimized design for three steel moment frame buildings with different number of stories using genetic algorithm code. The number and length of spans, and height of each floor were constant in all three buildings. The design of structures are carried out according to AISC code within the provisions of plastic design with allowable stress values. Genetic code for optimization is produced using MATLAB program, while buildings modeled in Opensees program and connected to the MATLAB code to perform iterations in optimization steps. In the end designs resulted from genetic algorithm code were compared with the analysis of buildings in ETABS program. The results demonstrated that suggested structural elements by the code utilize their full capacity, indicating the desirable efficiency of produced code.Keywords: genetic algorithm, structural analysis, steel moment frame, structural design
Procedia PDF Downloads 1116499 A Weighted Sum Particle Swarm Approach (WPSO) Combined with a Novel Feasibility-Based Ranking Strategy for Constrained Multi-Objective Optimization of Compact Heat Exchangers
Authors: Milad Yousefi, Moslem Yousefi, Ricarpo Poley, Amer Nordin Darus
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Design optimization of heat exchangers is a very complicated task that has been traditionally carried out based on a trial-and-error procedure. To overcome the difficulties of the conventional design approaches especially when a large number of variables, constraints and objectives are involved, a new method based on a well-stablished evolutionary algorithm, particle swarm optimization (PSO), weighted sum approach and a novel constraint handling strategy is presented in this study. Since, the conventional constraint handling strategies are not effective and easy-to-implement in multi-objective algorithms, a novel feasibility-based ranking strategy is introduced which is both extremely user-friendly and effective. A case study from industry has been investigated to illustrate the performance of the presented approach. The results show that the proposed algorithm can find the near pareto-optimal with higher accuracy when it is compared to conventional non-dominated sorting genetic algorithm II (NSGA-II). Moreover, the difficulties of a trial-and-error process for setting the penalty parameters is solved in this algorithm.Keywords: Heat exchanger, Multi-objective optimization, Particle swarm optimization, NSGA-II Constraints handling.
Procedia PDF Downloads 5516498 Multi-Objective Random Drift Particle Swarm Optimization Algorithm Based on RDPSO and Crowding Distance Sorting
Authors: Yiqiong Yuan, Jun Sun, Dongmei Zhou, Jianan Sun
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In this paper, we presented a Multi-Objective Random Drift Particle Swarm Optimization algorithm (MORDPSO-CD) based on RDPSO and crowding distance sorting to improve the convergence and distribution with less computation cost. MORDPSO-CD makes the most of RDPSO to approach the true Pareto optimal solutions fast. We adopt the crowding distance sorting technique to update and maintain the archived optimal solutions. Introducing the crowding distance technique into MORDPSO can make the leader particles find the true Pareto solution ultimately. The simulation results reveal that the proposed algorithm has better convergence and distributionKeywords: multi-objective optimization, random drift particle swarm optimization, crowding distance sorting, pareto optimal solution
Procedia PDF Downloads 2516497 Modified Bat Algorithm for Economic Load Dispatch Problem
Authors: Daljinder Singh, J.S.Dhillon, Balraj Singh
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According to no free lunch theorem, a single search technique cannot perform best in all conditions. Optimization method can be attractive choice to solve optimization problem that may have exclusive advantages like robust and reliable performance, global search capability, little information requirement, ease of implementation, parallelism, no requirement of differentiable and continuous objective function. In order to synergize between exploration and exploitation and to further enhance the performance of Bat algorithm, the paper proposed a modified bat algorithm that adds additional search procedure based on bat’s previous experience. The proposed algorithm is used for solving the economic load dispatch (ELD) problem. The practical constraint such valve-point loading along with power balance constraints and generator limit are undertaken. To take care of power demand constraint variable elimination method is exploited. The proposed algorithm is tested on various ELD problems. The results obtained show that the proposed algorithm is capable of performing better in majority of ELD problems considered and is at par with existing algorithms for some of problems.Keywords: bat algorithm, economic load dispatch, penalty method, variable elimination method
Procedia PDF Downloads 4566496 Classification Rule Discovery by Using Parallel Ant Colony Optimization
Authors: Waseem Shahzad, Ayesha Tahir Khan, Hamid Hussain Awan
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Ant-Miner algorithm that lies under ACO algorithms is used to extract knowledge from data in the form of rules. A variant of Ant-Miner algorithm named as cAnt-MinerPB is used to generate list of rules using pittsburgh approach in order to maintain the rule interaction among the rules that are generated. In this paper, we propose a parallel Ant MinerPB in which Ant colony optimization algorithm runs parallel. In this technique, a data set is divided vertically (i-e attributes) into different subsets. These subsets are created based on the correlation among attributes using Mutual Information (MI). It generates rules in a parallel manner and then merged to form a final list of rules. The results have shown that the proposed technique achieved higher accuracy when compared with original cAnt-MinerPB and also the execution time has also reduced.Keywords: ant colony optimization, parallel Ant-MinerPB, vertical partitioning, classification rule discovery
Procedia PDF Downloads 2856495 Optimization Analysis of a Concentric Tube Heat Exchanger with Field Synergy Principle
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The paper investigates the optimization analysis to the heat exchanger design, mainly with response surface method and genetic algorithm to explore the relationship between optimal fluid flow velocity and temperature of the heat exchanger using field synergy principle. First, finite volume method is proposed to calculate the flow temperature and flow rate distribution for numerical analysis. We identify the most suitable simulation equations by response surface methodology. Furthermore, a genetic algorithm approach is applied to optimize the relationship between fluid flow velocity and flow temperature of the heat exchanger. The results show that the field synergy angle plays vital role in the performance of a true heat exchanger.Keywords: optimization analysis, field synergy, heat exchanger, genetic algorithm
Procedia PDF Downloads 3026494 Enhancing the Dynamic Performance of Grid-Tied Inverters Using Manta Ray Foraging Algorithm
Authors: H. E. Keshta, A. A. Ali
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Three phase grid-tied inverters are widely employed in micro-grids (MGs) as interphase between DC and AC systems. These inverters are usually controlled through standard decoupled d–q vector control strategy based on proportional integral (PI) controllers. Recently, advanced meta-heuristic optimization techniques have been used instead of deterministic methods to obtain optimum PI controller parameters. This paper provides a comparative study between the performance of the global Porcellio Scaber algorithm (GPSA) based PI controller and Manta Ray foraging optimization (MRFO) based PI controller.Keywords: micro-grids, optimization techniques, grid-tied inverter control, PI controller
Procedia PDF Downloads 1276493 Improved Multi–Objective Firefly Algorithms to Find Optimal Golomb Ruler Sequences for Optimal Golomb Ruler Channel Allocation
Authors: Shonak Bansal, Prince Jain, Arun Kumar Singh, Neena Gupta
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Recently nature–inspired algorithms have widespread use throughout the tough and time consuming multi–objective scientific and engineering design optimization problems. In this paper, we present extended forms of firefly algorithm to find optimal Golomb ruler (OGR) sequences. The OGRs have their one of the major application as unequally spaced channel–allocation algorithm in optical wavelength division multiplexing (WDM) systems in order to minimize the adverse four–wave mixing (FWM) crosstalk effect. The simulation results conclude that the proposed optimization algorithm has superior performance compared to the existing conventional computing and nature–inspired optimization algorithms to find OGRs in terms of ruler length, total optical channel bandwidth and computation time.Keywords: channel allocation, conventional computing, four–wave mixing, nature–inspired algorithm, optimal Golomb ruler, lévy flight distribution, optimization, improved multi–objective firefly algorithms, Pareto optimal
Procedia PDF Downloads 3116492 Examining the Performance of Three Multiobjective Evolutionary Algorithms Based on Benchmarking Problems
Authors: Konstantinos Metaxiotis, Konstantinos Liagkouras
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The objective of this study is to examine the performance of three well-known multiobjective evolutionary algorithms for solving optimization problems. The first algorithm is the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), the second one is the Strength Pareto Evolutionary Algorithm 2 (SPEA-2), and the third one is the Multiobjective Evolutionary Algorithms based on decomposition (MOEA/D). The examined multiobjective algorithms are analyzed and tested on the ZDT set of test functions by three performance metrics. The results indicate that the NSGA-II performs better than the other two algorithms based on three performance metrics.Keywords: MOEAs, multiobjective optimization, ZDT test functions, evolutionary algorithms
Procedia PDF Downloads 4626491 Nelder-Mead Parametric Optimization of Elastic Metamaterials with Artificial Neural Network Surrogate Model
Authors: Jiaqi Dong, Qing-Hua Qin, Yi Xiao
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Some of the most fundamental challenges of elastic metamaterials (EMMs) optimization can be attributed to the high consumption of computational power resulted from finite element analysis (FEA) simulations that render the optimization process inefficient. Furthermore, due to the inherent mesh dependence of FEA, minuscule geometry features, which often emerge during the later stages of optimization, induce very fine elements, resulting in enormously high time consumption, particularly when repetitive solutions are needed for computing the objective function. In this study, a surrogate modelling algorithm is developed to reduce computational time in structural optimization of EMMs. The surrogate model is constructed based on a multilayer feedforward artificial neural network (ANN) architecture, trained with prepopulated eigenfrequency data prepopulated from FEA simulation and optimized through regime selection with genetic algorithm (GA) to improve its accuracy in predicting the location and width of the primary elastic band gap. With the optimized ANN surrogate at the core, a Nelder-Mead (NM) algorithm is established and its performance inspected in comparison to the FEA solution. The ANNNM model shows remarkable accuracy in predicting the band gap width and a reduction of time consumption by 47%.Keywords: artificial neural network, machine learning, mechanical metamaterials, Nelder-Mead optimization
Procedia PDF Downloads 1236490 Advanced Hybrid Particle Swarm Optimization for Congestion and Power Loss Reduction in Distribution Networks with High Distributed Generation Penetration through Network Reconfiguration
Authors: C. Iraklis, G. Evmiridis, A. Iraklis
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Renewable energy sources and distributed power generation units already have an important role in electrical power generation. A mixture of different technologies penetrating the electrical grid, adds complexity in the management of distribution networks. High penetration of distributed power generation units creates node over-voltages, huge power losses, unreliable power management, reverse power flow and congestion. This paper presents an optimization algorithm capable of reducing congestion and power losses, both described as a function of weighted sum. Two factors that describe congestion are being proposed. An upgraded selective particle swarm optimization algorithm (SPSO) is used as a solution tool focusing on the technique of network reconfiguration. The upgraded SPSO algorithm is achieved with the addition of a heuristic algorithm specializing in reduction of power losses, with several scenarios being tested. Results show significant improvement in minimization of losses and congestion while achieving very small calculation times.Keywords: congestion, distribution networks, loss reduction, particle swarm optimization, smart grid
Procedia PDF Downloads 4396489 Improvement of the Robust Proportional–Integral–Derivative (PID) Controller Parameters for Controlling the Frequency in the Intelligent Multi-Zone System at the Present of Wind Generation Using the Seeker Optimization Algorithm
Authors: Roya Ahmadi Ahangar, Hamid Madadyari
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The seeker optimization algorithm (SOA) is increasingly gaining popularity among the researchers society due to its effectiveness in solving some real-world optimization problems. This paper provides the load-frequency control method based on the SOA for removing oscillations in the power system. A three-zone power system includes a thermal zone, a hydraulic zone and a wind zone equipped with robust proportional-integral-differential (PID) controllers. The result of simulation indicates that load-frequency changes in the wind zone for the multi-zone system are damped in a short period of time. Meanwhile, in the oscillation period, the oscillations amplitude is not significant. The result of simulation emphasizes that the PID controller designed using the seeker optimization algorithm has a robust function and a better performance for oscillations damping compared to the traditional PID controller. The proposed controller’s performance has been compared to the performance of PID controller regulated with Particle Swarm Optimization (PSO) and. Genetic Algorithm (GA) and Artificial Bee Colony (ABC) algorithms in order to show the superior capability of the proposed SOA in regulating the PID controller. The simulation results emphasize the better performance of the optimized PID controller based on SOA compared to the PID controller optimized with PSO, GA and ABC algorithms.Keywords: load-frequency control, multi zone, robust PID controller, wind generation
Procedia PDF Downloads 2946488 Description of a New Fruit Fly Species within Genus Bactrocera Macquart (Diptera: Tephritidae: Dacinae) Detected in Pakistan
Authors: Muhammad Riaz, Muhammad Sarwar
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As a result of broad trapping program for the collection of fruit flies fauna of Pakistan, adults or larvae samples of fruit flies were collected from different localities. After sampling, to characterize fruit fly fauna involved, the collected samples were brought to the laboratory for their species identification. In this study, based on extensive literature records, the presence of one fruit fly species Bactrocera abbasi (Diptera: Tephritidae: Dacinae) belonging to genus Bactrocera Macquar was recognized for the first time. This new species is described and illustrated on the basis of morphological characters, supported by data on its ecology and geographic distribution. Information is also given on host plant and location of type specimen, distinguish remarks and diagnosis are as well included.Keywords: diptera, tephritidae, bactrocera, new species, taxonomy, Pakistan
Procedia PDF Downloads 3456487 Influence of Optimization Method on Parameters Identification of Hyperelastic Models
Authors: Bale Baidi Blaise, Gilles Marckmann, Liman Kaoye, Talaka Dya, Moustapha Bachirou, Gambo Betchewe, Tibi Beda
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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
Procedia PDF Downloads 1626486 Supplementation of Fig Fruit (Ficus carica linn.) Extract in Extender on Sperm Motility and Viability of Native Chicken Semen after Cooling
Authors: N. Isnaini, S. Wahjuningsih
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Fig fruit is the fruit of a tropical plant with content of flavanoids, vitamins A, C, and E which are antioxidants that effectively prevent and neutralize free radicals. This study was conducted to evaluate the supplementation of fig fruit extract in a physiological NaCl-based diluent on sperm motility and viability of native chicken semen after cooling. Semen was collected from 4 male mature chocks using massage method. Fresh semen evaluated for colour, pH, volume, concentration, mass motility, individual motility, life sperm and sperm abnormality. Semen was diluted with physiological NaCl-based extender supplemented with different levels of fig fruit extract (0, 10, 20 and 30 %) v/v with the ratio of 1 semen: 4 diluter. Semen used had mass motility of 2+ and motility of 70%. Immediately after dilution semen was stored in 3-5 °C and sperm motility and viability percentage were observed at 0, 12 and 24 h. The obtained data were analyze with Analysis of Variant (ANOVA) and Least Significant Difference were determined. The experiment was designed using completely random design (4 treatments and 10 replications). The results showed that the level of fig fruit extract had very significant effect (P < 0,01) on sperm motility and viability percentage in 0, 12 and 24 h of cooling. It can be concluded that the best fig fruit extract level for resulting optimal sperm motility and viability was 10%.Keywords: chock, antioxidant, fig fruit extract, sperm
Procedia PDF Downloads 2986485 Bio-Oil Production and Chromatographic Characterization from the Pyrolysis of Oil Palm Empty Fruit Bunches
Authors: Arif Ferdiyanto, Fajar Hamida, Arif Hidayat
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Oil palm empty fruit bunches, derived biomass available in Indonesia, is one of the potential biomass to produce biofuels like bio-oil due to its abundant supply and favorable physicochemical characteristics. An interesting alternative of utilising the oil palm empty fruit bunches is in the production of bio-oil by pyrolysis. Pyrolysis of oil palm empty fruit bunches to bio-oil is being considered for national energy security and environmental advantages. The aim of this study was to produce bio-oil by pyrolysis of oil palm empty fruit bunches at various temperature and observe its detailed chemical composition. The biomass was submitted to a pyrolysis in a batch reactor. Experiments were carried out at a temperature range of 450–600°C and heating rate range of 10-20°C/min. The yield of bio-oil was found to be maximum at the temperature of 600°C. The bio-oils detailed compositions were investigated using FTIR and GC-MS. The bio-char produced as a co-product can be a potential soil amendment with multiple benefits including soil fertility and for solid fuel applications that also contributes to the preservation of the environment. The present investigation suggests the suitability of oil palm empty fruit bunches as a potential feedstock for exploitation of energy and biomaterials through pyrolysis process.Keywords: bio-oil, oil palm empty fruit bunches, pyrolysis, renewable energy
Procedia PDF Downloads 3336484 A High-Level Co-Evolutionary Hybrid Algorithm for the Multi-Objective Job Shop Scheduling Problem
Authors: Aydin Teymourifar, Gurkan Ozturk
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In this paper, a hybrid distributed algorithm has been suggested for the multi-objective job shop scheduling problem. Many new approaches are used at design steps of the distributed algorithm. Co-evolutionary structure of the algorithm and competition between different communicated hybrid algorithms, which are executed simultaneously, causes to efficient search. Using several machines for distributing the algorithms, at the iteration and solution levels, increases computational speed. The proposed algorithm is able to find the Pareto solutions of the big problems in shorter time than other algorithm in the literature. Apache Spark and Hadoop platforms have been used for the distribution of the algorithm. The suggested algorithm and implementations have been compared with results of the successful algorithms in the literature. Results prove the efficiency and high speed of the algorithm.Keywords: distributed algorithms, Apache Spark, Hadoop, job shop scheduling, multi-objective optimization
Procedia PDF Downloads 3586483 Trajectory Optimization of Re-Entry Vehicle Using Evolutionary Algorithm
Authors: Muhammad Umar Kiani, Muhammad Shahbaz
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Performance of any vehicle can be predicted by its design/modeling and optimization. Design optimization leads to efficient performance. Followed by horizontal launch, the air launch re-entry vehicle undergoes a launch maneuver by introducing a carefully selected angle of attack profile. This angle of attack profile is the basic element to complete a specified mission. Flight program of said vehicle is optimized under the constraints of the maximum allowed angle of attack, lateral and axial loads and with the objective of reaching maximum altitude. The main focus of this study is the endo-atmospheric phase of the ascent trajectory. A three degrees of freedom trajectory model is simulated in MATLAB. The optimization process uses evolutionary algorithm, because of its robustness and efficient capacity to explore the design space in search of the global optimum. Evolutionary Algorithm based trajectory optimization also offers the added benefit of being a generalized method that may work with continuous, discontinuous, linear, and non-linear performance matrix. It also eliminates the requirement of a starting solution. Optimization is particularly beneficial to achieve maximum advantage without increasing the computational cost and affecting the output of the system. For the case of launch vehicles we are immensely anxious to achieve maximum performance and efficiency under different constraints. In a launch vehicle, flight program means the prescribed variation of vehicle pitching angle during the flight which has substantial influence reachable altitude and accuracy of orbit insertion and aerodynamic loading. Results reveal that the angle of attack profile significantly affects the performance of the vehicle.Keywords: endo-atmospheric, evolutionary algorithm, efficient performance, optimization process
Procedia PDF Downloads 4006482 Elephant Herding Optimization for Service Selection in QoS-Aware Web Service Composition
Authors: Samia Sadouki Chibani, Abdelkamel Tari
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Web service composition combines available services to provide new functionality. Given the number of available services with similar functionalities and different non functional aspects (QoS), the problem of finding a QoS-optimal web service composition is considered as an optimization problem belonging to NP-hard class. Thus, an optimal solution cannot be found by exact algorithms within a reasonable time. In this paper, a meta-heuristic bio-inspired is presented to address the QoS aware web service composition; it is based on Elephant Herding Optimization (EHO) algorithm, which is inspired by the herding behavior of elephant group. EHO is characterized by a process of dividing and combining the population to sub populations (clan); this process allows the exchange of information between local searches to move toward a global optimum. However, with Applying others evolutionary algorithms the problem of early stagnancy in a local optimum cannot be avoided. Compared with PSO, the results of experimental evaluation show that our proposition significantly outperforms the existing algorithm with better performance of the fitness value and a fast convergence.Keywords: bio-inspired algorithms, elephant herding optimization, QoS optimization, web service composition
Procedia PDF Downloads 3246481 Effect of Aminoethoxyvinylglycine on Ceasing in Sweet Orange
Authors: Zahoor Hussain
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Creasing is a physiological disorder of rind in sweet orange [Citrus sinensis (L.) Osbeck] fruit and causes serious economic losses in various countries of the world. The reversible inhibitor of ethylene, aminoethoxyvinylglycine (AVG) with the effects of different concentrations (0, 20, 40 and 60 mgL⁻¹) AVG with 0.05% ‘Tween 20’ as a surfactant applied at the fruit set, the golf ball or at the colour break stage on controlling creasing, rheological properties of fruit and rind as well as fruit quality in of Washington Navel and Lane Late sweet orange was investigated. Creasing was substantially reduced and fruit quality was improved with the exogenous application of AVG depending upon its concentration and stage of application in both cultivars. The spray application of AVG (60 mgL⁻¹) at the golf ball stage was effective in reducing creasing (27.86% and 24.29%) compared to the control (52.14 and 51.53%) in cv. Washington Navel during 2011 and 2012, respectively. Whilst, in cv. Lane Late lowest creasing was observed When AVG was applied at fruit set stage (22.86%) compared to the control (51.43%) during 2012. In cv. Washington Navel, AVG treatment (60 mgL⁻¹) was more effective to increase the fruit firmness (318.97 N) and rind hardness (25.94 N) when applied at fruit set stage. However, rind tensile strength was higher, when AVG was applied at the golf ball stage (54.13 N). In cv. Lane Late, the rind harness (28.61 N), rind tensile strength (78.82 N) was also higher when AVG was sprayed at fruit set stage. Whilst, the fruit compression force (369.68 N) was higher when AVG was applied at the golf ball stage. Similarly, the treatment AVG (60 mgL⁻¹) was more effective in improving fruit weight (281.00 and 298.50 g) and fruit diameter (87.30 and 82.69 mm), rind thickness (5.56 and 5.38 mm) and total sugars (15.27 mg.100ml⁻¹) when AVG was applied at the fruit golf ball stage in cv. Washington Navel and Lane Late, respectively. Similarly, rind harness (25.94 and 28.61 N), total antioxidants (45.30 and 46.48 mM trolox 100ml⁻¹), total sugars (13.64 and 15.27 mg.100ml⁻¹), citric acid (1.66 and 1.32 mg100ml⁻¹), malic acid (0.36 and 0.63 mg.100ml⁻¹) and succinic acid (0.35 and 0.38 mg100ml⁻¹) were also higher, when AVG was applied at the fruit set stage in both cultivars. In conclusion, the exogenous applications of AVG substantially reduces the creasing incidence, improves rheological properties of fruit and rind as well as fruit quality in Washington Navel and Lane Late sweet orange fruit.Keywords: AVG, creasing, ethylene inhibitor, sweet orange
Procedia PDF Downloads 1536480 ACO-TS: an ACO-based Algorithm for Optimizing Cloud Task Scheduling
Authors: Fahad Y. Al-dawish
Abstract:
The current trend by a large number of organizations and individuals to use cloud computing. Many consider it a significant shift in the field of computing. Cloud computing are distributed and parallel systems consisting of a collection of interconnected physical and virtual machines. With increasing request and profit of cloud computing infrastructure, diverse computing processes can be executed on cloud environment. Many organizations and individuals around the world depend on the cloud computing environments infrastructure to carry their applications, platform, and infrastructure. One of the major and essential issues in this environment related to allocating incoming tasks to suitable virtual machine (cloud task scheduling). Cloud task scheduling is classified as optimization problem, and there are several meta-heuristic algorithms have been anticipated to solve and optimize this problem. Good task scheduler should execute its scheduling technique on altering environment and the types of incoming task set. In this research project a cloud task scheduling methodology based on ant colony optimization ACO algorithm, we call it ACO-TS Ant Colony Optimization for Task Scheduling has been proposed and compared with different scheduling algorithms (Random, First Come First Serve FCFS, and Fastest Processor to the Largest Task First FPLTF). Ant Colony Optimization (ACO) is random optimization search method that will be used for assigning incoming tasks to available virtual machines VMs. The main role of proposed algorithm is to minimizing the makespan of certain tasks set and maximizing resource utilization by balance the load among virtual machines. The proposed scheduling algorithm was evaluated by using Cloudsim toolkit framework. Finally after analyzing and evaluating the performance of experimental results we find that the proposed algorithm ACO-TS perform better than Random, FCFS, and FPLTF algorithms in each of the makespaan and resource utilization.Keywords: cloud Task scheduling, ant colony optimization (ACO), cloudsim, cloud computing
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