Search results for: bioprocess optimization

Authors: M. Caramihai, Irina Severin

Abstract:

This paper deals with the design of an intelligent control structure for a bioprocess of Hansenula polymorpha yeast cultivation. The objective of the process control is to produce biomass in a desired physiological state. The work demonstrates that the designed Hybrid Control Techniques (HCT) are able to recognize specific evolution bioprocess trajectories using neural networks trained specifically for this purpose, in order to estimate the model parameters and to adjust the overall bioprocess evolution through an expert system and a fuzzy structure. The design of the control algorithm as well as its tuning through realistic simulations is presented. Taking into consideration the synergism of different paradigms like fuzzy logic, neural network, and symbolic artificial intelligence (AI), in this paper we present a real and fulfilled intelligent control architecture with application in bioprocess control.

Keywords: bioprocess, intelligent control, neural nets, fuzzy structure, hybrid techniques

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Authors: Mihai Caramihai, Dan Vasilescu

Abstract:

The main research objective of the experimental bioprocess analyzed in this paper was to obtain large biomass quantities. The bioprocess is performed in 100 L Bioengineering bioreactor with 42 L cultivation medium made of peptone, meat extract and sodium chloride. The reactor was equipped with pH, temperature, dissolved oxygen, and agitation controllers. The operating parameters were 37 oC, 1.2 atm, 250 rpm and air flow rate of 15 L/min. The main objective of this paper is to present a case study to demonstrate that intelligent control, describing the complexity of the biological process in a qualitative and subjective manner as perceived by human operator, is an efficient control strategy for this kind of bioprocesses. In order to simulate the bioprocess evolution, an intelligent control structure, based on fuzzy logic has been designed. The specific objective is to present a fuzzy control approach, based on human expert’ rules vs. a modeling approach of the cells growth based on bioprocess experimental data. The kinetic modeling may represent only a small number of bioprocesses for overall biosystem behavior while fuzzy control system (FCS) can manipulate incomplete and uncertain information about the process assuring high control performance and provides an alternative solution to non-linear control as it is closer to the real world. Due to the high degree of non-linearity and time variance of bioprocesses, the need of control mechanism arises. BIOSIM, an original developed software package, implements such a control structure. The simulation study has showed that the fuzzy technique is quite appropriate for this non-linear, time-varying system vs. the classical control method based on a priori model.

Keywords: intelligent, control, fuzzy model, bioprocess optimization

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Authors: Vishal Kumar Singh

Abstract:

Bioprocess development is a time-constrained activity aimed at harnessing the full potential of culture performance in an ambience that is not natural to cells. Even with the use of chemically defined media and feeds, a significant amount of time is devoted in identifying the apt operating parameters. In addition, the scale-up of these processes is often accompanied by loss of antibody titer and product quality, which further delays the commercialization of the drug product. In such a scenario, the investigation of this disparity of culture performance is done by further experimentation at a smaller scale that is representative of at-scale production bioreactors. These scale-down model developments are also time-intensive. In this study, a computation fluid dynamics-based multi-objective scaling approach has been illustrated to speed up the process transfer. For the implementation of this approach, a transient multiphase water-air system has been studied in Ansys CFX to visualize the air bubble distribution and volumetric mass transfer coefficient (kLa) profiles, followed by the design of experiment based parametric optimization approach to define the operational space. The proposed approach is completely in silico and requires minimum experimentation, thereby rendering a high throughput to the overall process development.

Keywords: bioprocess development, scale up, scale down, computation fluid dynamics, multi-objective, Ansys CFX, design of experiment

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Authors: María D. Groppa, Andrea Trentini, Myriam Zawoznik, Roxana Bigi, Carlos Nadra, Patricia L. Marconi

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In the present work, a remediation bioprocess based on the use of a local isolate of the microalgae Chlorella vulgaris immobilized in alginate beads is proposed. This process was shown to be effective for the reduction of several chemical and microbial contaminants present in Cildáñez stream, a water course that is part of the Matanza-Riachuelo Basin (Buenos Aires, Argentina). The bioprocess, involving the culture of the microalga in autotrophic conditions in a stirred-tank bioreactor supplied with a marine propeller for 6 days, allowed a significant reduction of Escherichia coli and total coliform numbers (over 95%), as well as of ammoniacal nitrogen (96%), nitrates (86%), nitrites (98%), and total phosphorus (53%) contents. Pb content was also significantly diminished after the bioprocess (95%). Standardized cytotoxicity tests using Allium cepa seeds and Cildáñez water pre- and post-remediation were also performed. Germination rate and mitotic index of onion seeds imbibed in Cildáñez water subjected to the bioprocess was similar to that observed in seeds imbibed in distilled water and significantly superior to that registered when untreated Cildáñez water was used for imbibition. Our results demonstrate the potential of this simple and cost-effective technology to remove urban-water contaminants, offering as an additional advantage the possibility of an easy biomass recovery, which may become a source of alternative energy.

Keywords: bioreactor, bioremediation, Chlorella vulgaris, Matanza-Riachuelo Basin, microalgae

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Authors: J. E. O. Hernandez

Abstract:

In the next 20 years, energy production by burning fuels will increase and so will the atmospheric concentration of CO2 and its well-known threats to life on Earth. The technologies available for capturing CO2 are still dubious and this keeps fostering an interest in bio-inspired approaches. The leading one is the application of carbonic anhydrase (CA) –a superfast biocatalyst able to convert up to one million molecules of CO2 into carbonates in water. However, natural CA underperforms when applied to real smoky CO2 in chimneys and, so far, the efforts to create superior CAs in the lab rely on screening methods running under pristine conditions at the micro level, which are far from resembling those in chimneys. For the evolution of man-made enzymes, selection rather than screening would be ideal but this is challenging because of the need for a suitable artificial environment that is also sustainable for our society. Herein we present the stepwise design and construction of a bioprocess (from bench-scale to semi-pilot) for evolutionary selection experiments. In this bioprocess, reaction and adsorption took place simultaneously at atmospheric pressure in a spray tower. The scrubbing solution was fed countercurrently by reusing municipal pressure and it was mainly prepared with water, carbonic anhydrase and calcium chloride. This bioprocess allowed for the enzymatic carbonation of smoky CO2; the reuse of process water and the recovery of solid carbonates without cooling of smoke, pretreatments, solvent amines and compression of CO2. The average yield of solid carbonates was 0.54 g min-1 or 12-fold the amount produced in serum bottles at lab bench scale. This bioprocess could be used as a tailor-made environment for driving the selection of superior CAs. The bioprocess and its match CA could be sustainably used to reduce global warming by CO2 emissions from exhausts.

Keywords: biological carbon capture and sequestration, carbonic anhydrase, directed evolution, global warming

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Authors: Ghaneshree Moonsamy, Nodumo N. Zulu, Rajesh Lalloo, Suren Singh, Santosh O. Ramchuran

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The abalone industry of South Africa is under severe pressure due to illegal harvesting and poaching of this seafood delicacy. These abalones are harvested excessively; as a result, these animals do not have a chance to replace themselves in their habitats, ensuing in a drastic decrease in natural stocks of abalone. Abalone has an extremely slow growth rate and takes approximately four years to reach a size that is market acceptable; therefore, it was imperative to investigate methods to boost the overall growth rate and immunity of the animal. The University of Cape Town (UCT) began to research, which resulted in the isolation of two microorganisms, a yeast isolate Debaryomyces hansenii and a bacterial isolate Vibrio midae, from the gut of the abalone and characterised them for their probiotic abilities. This work resulted in an internationally competitive concept technology that was patented. The next stage of research was to develop a suitable bioprocess to enable commercial production. Numerous steps were taken to develop an efficient production process for V. midae, one of the isolates found by UCT. The initial stages of research involved the development of a stable and robust inoculum and the optimization of physiological growth parameters such as temperature and pH. A range of temperature and pH conditions were evaluated, and data obtained revealed an optimum growth temperature of 30ᵒC and a pH of 6.5. Once these critical growth parameters were established further media optimization studies were performed. Corn steep liquor (CSL) and high test molasses (HTM) were selected as suitable alternatives to more expensive, conventionally used growth medium additives. The optimization of CSL (6.4 g.l⁻¹) and HTM (24 g.l⁻¹) concentrations in the growth medium resulted in a 180% increase in cell concentration, a 5716-fold increase in cell productivity and a 97.2% decrease in the material cost of production in comparison to conventional growth conditions and parameters used at the onset of the study. In addition, a stable market-ready liquid probiotic product, encompassing the viable but not culturable (VBNC) state of Vibrio midae cells, was developed during the downstream processing aspect of the study. The demonstration of this technology at a full manufacturing scale has further enhanced the attractiveness and commercial feasibility of this production process.

Keywords: probiotics, abalone aquaculture, bioprocess technology, manufacturing scale technology development

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Authors: Boukhiar Aissa, Halladj Fatima, Iguergaziz Nadia, Lamrani yasmina, Benamara Salem

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Determining the alcohol content in alcoholic fermentation bioprocess is of great importance. In fact, it is a key indicator for monitoring this bioprocess. Several methodologies (chemical, spectrophotometric, chromatographic) are used to the determination of this parameter. However, these techniques are very long and they require: rigorous preparations, sometimes dangerous chemical reagents and/or expensive equipment. In the present study, the date juice is used as the substrate of alcoholic fermentation. The extracted juice undergoes an alcoholic fermentation by Saccharomyces cerevisiae. The study of the possible use of refractometry as a sole means for the in situ control of alcoholic fermentation revealed a good correlation (R2=0.98) between initial and final °Brix: °Brixf=0.377×°Brixi. In addition, the relationship between Δ°Brix and alcoholic content of the final product (A,%) has been determined: Δ°Brix/A=1.1. The obtained results allowed us to establish iso-responses abacus, which can be used for the determination of alcohol and residual sugar content, with a mean relative error (MRE) of 5.35%.

Keywords: alcoholic fermentation, date juice, refractometry, residual sugar

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Authors: Elsayed Ahmed Elsayed, Azza Noor El-Deen, Mohamed A. Farid, Mohamed A. Wadaan

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Recently, a great attention has been paid to the use of dietary carbohydrates as prebiotic functional foods. Among the new commercially available products, fructooligosaccharides (FOS), which are microbial produced from sucrose, have attracted special interest due to their valuable properties and, thus, have a great economic potential for the sugar industrial branch. They are non-cariogenic sweeteners of low caloric value, as they are not hydrolyzed by the gastro-intestinal enzymes, promoting selectively the growth of the bifidobacteria in the colon, helping to eliminate the harmful microbial species to human and animal health and preventing colon cancer. FOS has been also found to reduce cholesterol, phospholipids and triglyceride levels in blood. FOS has been mainly produced by microbial fructosyltransferase (FTase) enzymes. The present work outlines bioprocess optimization for different cultivation parameters affecting the production of FTase by Penicillium aurantiogriseum AUMC 5605. The optimization involves both traditional as well as fractional factorial design approaches. Additionally, the production process will be compared under batch and fed-batch conditions. Finally, the optimized process conditions will be applied to 5-L stirred tank bioreactor cultivations.

Keywords: prebiotics, fructooligosaccharides, optimization, cultivation

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Authors: Harsha Thaira, Ritu Raval, Keyur Raval

Abstract:

Melanin has immense applications in the field of agriculture, cosmetics and pharmaceutical industries due to its photo-protective, UV protective and anti- oxidant activities. However, its production is limited to costly chemical methods or harsh extractive methods from hair which ultimately gives poor yields. This makes the cost of melanin very high, to the extent of US Dollar 300 per gram. Some microorganisms are reported to produce melanin under stress conditions. Out of all melanin producing organisms, Pseudomonas stutzeri can grow in sea water and produce melanin under saline stress. The objective of this study was to develop a sea water based bioprocess. Effects of different growth media and process parameters on melanin production using sea water were investigated. The marine bacterial strain Pseudomonas stutzeri HMGM-7(MTCC 11712) was selected and the effect of different media such as Nutrient Broth (NB), Luria Bertini (LB) broth, Bushnell- Haas broth (BHB) and Trypticase Soy broth (TSB) and various medium components were investigated with one factor at a time approach. Parameters like shaking frequency, inoculum age, inoculum size, pH and temperature were also investigated in order to obtain the optimum conditions for maximum melanin production. The highest yield of melanin concentration, 0.306 g/L, was obtained in Trypticase Soy broth at 36 hours. The yield was 1.88 times higher than the melanin obtained before optimization, 0.163 g/L at 36 hours. Studies are underway to optimize medium constituents to further enhance melanin production.

Keywords: melanin, marine, bioprocess, pseudomonas

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Authors: Mitat Uysal

Abstract:

A new met heuristic optimization algorithm called as Migrating Birds Optimization is used for curve fitting by rational cubic Bezier Curves. This requires solving a complicated multivariate optimization problem. In this study, the solution of this optimization problem is achieved by Migrating Birds Optimization algorithm that is a powerful met heuristic nature-inspired algorithm well appropriate for optimization. The results of this study show that the proposed method performs very well and being able to fit the data points to cubic Bezier Curves with a high degree of accuracy.

Keywords: algorithms, Bezier curves, heuristic optimization, migrating birds optimization

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Authors: Kostas Metaxiotis

Abstract:

Portfolio optimization is one of the most important topics in finance. This paper proposes a mean–variance–skewness (MVS) portfolio optimization model. Traditionally, the portfolio optimization problem is solved by using the mean–variance (MV) framework. In this study, we formulate the proposed model as a three-objective optimization problem, where the portfolio's expected return and skewness are maximized whereas the portfolio risk is minimized. For solving the proposed three-objective portfolio optimization model we apply an adapted version of the non-dominated sorting genetic algorithm (NSGAII). Finally, we use a real dataset from FTSE-100 for validating the proposed model.

Keywords: evolutionary algorithms, portfolio optimization, skewness, stock selection

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Authors: Serges Mendomo Meye, Li Guowei, Shen Zhenzhong, Gan Lei, Xu Liqun

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Given the limitations of the original whale optimization algorithm (WAO) in local optimum and low convergence accuracy in truss structure optimization problems, based on the fundamental whale algorithm, an improved whale optimization algorithm (SWAO) based on information entropy is proposed. The information entropy itself is an uncertain measure. It is used to control the range of whale searches in path selection. It can overcome the shortcomings of the basic whale optimization algorithm (WAO) and can improve the global convergence speed of the algorithm. Taking truss structure as the optimization research object, the mathematical model of truss structure optimization is established; the cross-sectional area of truss is taken as the design variable; the objective function is the weight of truss structure; and an improved whale optimization algorithm (SWAO) is used for optimization design, which provides a new idea and means for its application in large and complex engineering structure optimization design.

Keywords: information entropy, structural optimization, truss structure, whale algorithm

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Authors: Ramin Javadzadeh

Abstract:

The particle swarm optimization are Meta heuristic optimization method, which are used for clustering and pattern recognition applications are abundantly. These algorithms in multimodal optimization problems are more efficient than genetic algorithms. A major drawback in these algorithms is their slow convergence to global optimum and their weak stability can be considered in various running of these algorithms. In this paper, improved Particle swarm optimization is introduced for the first time to overcome its problems. The fuzzy cellular automata is used for improving the algorithm efficiently. The credibility of the proposed approach is evaluated by simulations, and it is shown that the proposed approach achieves better results can be achieved compared to the Particle swarm optimization algorithms.

Keywords: cellular automata, cellular learning automata, local search, optimization, particle swarm optimization

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Authors: María L. Jalón, Feargal Brennan

Abstract:

A non-stationary stochastic optimization methodology is applied to an OWC (oscillating water column) to find the design that maximizes the wave energy extraction. Different temporal cycles are considered to represent the long-term variability of the wave climate at the site in the optimization problem. The results of the non-stationary stochastic optimization problem are compared against those obtained by a stationary stochastic optimization problem. The comparative analysis reveals that the proposed non-stationary optimization provides designs with a better fit to reality. However, the stationarity assumption can be adequate when looking at averaged system response.

Keywords: non-stationary stochastic optimization, oscillating water, temporal variability, wave energy

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Authors: Liudmyla Koliechkina, Olena Dvirna

Abstract:

The statement of the multi-objective optimization problem on combinatorial configurations is formulated, and the approach to its solution is proposed. The problem is of interest as a combinatorial optimization one with many criteria, which is a model of many applied tasks. The approach to solving the multi-objective optimization problem on combinatorial configurations consists of two stages; the first is the reduction of the multi-objective problem to the single criterion based on existing multi-objective optimization methods, the second stage solves the directly replaced single criterion combinatorial optimization problem by the horizontal combinatorial method. This approach provides the optimal solution to the multi-objective optimization problem on combinatorial configurations, taking into account additional restrictions for a finite number of steps.

Keywords: discrete set, linear combinatorial optimization, multi-objective optimization, Pareto solutions, partial permutation set, structural graph

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Authors: Boukhiar Aissa, Iguergaziz Nadia, Halladj Fatima, Lamrani Yasmina, Benamara Salem

Abstract:

Determining the alcohol content in alcoholic fermentation bioprocess has a great importance. In fact, it is a key indicator for monitoring this fermentation bioprocess. Several methodologies (chemical, spectrophotometric, chromatographic...) are used to the determination of this parameter. However, these techniques are very long and require: rigorous preparations, sometimes dangerous chemical reagents, and/or expensive equipment. In the present study, the date juice is used as a substrate of alcoholic fermentation. The extracted juice undergoes an alcoholic fermentation by Saccharomyces cerevisiae. The study of the possible use of refractometry as a sole means for the in situ control of this process revealed a good correlation (R2 = 0.98) between initial and final ° Brix: ° Brix f = 0.377× ° Brixi. In addition, we verified the relationship between the variation in final and initial ° Brix (Δ ° Brix) and alcoholic rate produced (A exp): CΔ° Brix / A exp = 1.1. This allows the tracing of abacus isoresponses that permit to determine the alcoholic and residual sugar rates with a mean relative error (MRE) of 5.35%.

Keywords: refractometry, alcohol, residual sugar, fermentation, brix, date, juice

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Authors: R. M. Rizk-Allah

Abstract:

This paper presents co-evolutionary fruit fly optimization algorithm based on firefly algorithm (CFOA-FA) for solving unconstrained optimization problems. The proposed algorithm integrates the merits of fruit fly optimization algorithm (FOA), firefly algorithm (FA) and elite strategy to refine the performance of classical FOA. Moreover, co-evolutionary mechanism is performed by applying FA procedures to ensure the diversity of the swarm. Finally, the proposed algorithm CFOA- FA is tested on several benchmark problems from the usual literature and the numerical results have demonstrated the superiority of the proposed algorithm for finding the global optimal solution.

Keywords: firefly algorithm, fruit fly optimization algorithm, unconstrained optimization problems

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Authors: Pham Van Nha, Le Cam Binh

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Particle swarm optimization (PSO) is a population-based stochastic optimization algorithm. PSO was inspired by the natural behavior of birds and fish in migration and foraging for food. PSO is considered as a multidisciplinary optimization model that can be applied in various optimization problems. PSO’s ideas are simple and easy to understand but PSO is only applied in simple model problems. We think that in order to expand the applicability of PSO in complex problems, PSO should be described more explicitly in the form of a mathematical model. In this paper, we represent PSO in a mathematical model and apply in the multivariate data classification. First, PSOs general mathematical model (MPSO) is analyzed as a universal optimization model. Then, Model of Optimal Centroids (MOC) is proposed for the multivariate data classification. Experiments were conducted on some benchmark data sets to prove the effectiveness of MOC compared with several proposed schemes.

Keywords: analysis of optimization, artificial intelligence based optimization, optimization for learning and data analysis, global optimization

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Authors: Sait Ali Uymaz, Gülay Tezel

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This paper presents the comparison results on the performance of the Cuckoo Search (CS) algorithm for constrained optimization problems. For constraint handling, CS algorithm uses penalty method. CS algorithm is tested on thirteen well-known test problems and the results obtained are compared to Particle Swarm Optimization (PSO) algorithm. Mean, best, median and worst values were employed for the analyses of performance.

Keywords: cuckoo search, particle swarm optimization, constrained optimization problems, penalty method

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Authors: Takahiro Hino, Michiharu Maeda

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Particle swarm optimization (PSO) is an optimization approach that achieves the social model of bird flocking and fish schooling. PSO works in continuous space and can solve continuous optimization problem with high quality. Set-based particle swarm optimization (SPSO) functions in discrete space by using a set. SPSO can solve combinatorial optimization problem with high quality and is successful to apply to the large-scale problem. In this paper, we present an algorithm of SPSO with status memory to decide the position based on the previous position for solving traveling salesman problem (TSP). In order to show the effectiveness of our approach. We examine SPSOSM for TSP compared to the existing algorithms.

Keywords: combinatorial optimization problems, particle swarm optimization, set-based particle swarm optimization, traveling salesman problem

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Authors: D. Li

Abstract:

Optical thin films are used in a wide variety of optical components and there are many software tools programmed for advancing multilayer thin film design. The available software packages for designing the thin film structure may not provide optimum designs. Normally, almost all current software programs obtain their final designs either from optimizing a starting guess or by technique, which may or may not involve a pseudorandom process, that give different answers every time, depending upon the initial conditions. With the increasing power of personal computers, functional methods in optimization and synthesis of optical multilayer systems have been developed such as DGL Optimization, Simulated Annealing, Genetic Algorithms, Needle Optimization, Inductive Optimization and Flip-Flop Optimization. Among these, DGL Optimization has proved its efficiency in optical thin film designs. The application of the DGL optimization technique to the design of optical coating is presented. A DGL optimization technique is provided, and its main features are discussed. Guidelines on the application of the DGL optimization technique to various types of design problems are given. The innovative global optimization strategies used in a software tool, OnlyFilm, to optimize multilayer thin film designs through different filter designs are outlined. OnlyFilm is a powerful, versatile, and user-friendly thin film software on the market, which combines optimization and synthesis design capabilities with powerful analytical tools for optical thin film designers. It is also the only thin film design software that offers a true global optimization function.

Keywords: optical coatings, optimization, design software, thin film design

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Authors: O. Mohamed Amine, A. Khireddine

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Telecoms operators are always looking to meet their share of the other customers, they try to gain optimum utilization of the deployed equipment and network optimization has become essential. This project consists of optimizing UMTS network, and the study area is an urban area situated in the center of Algiers. It was initially questions to become familiar with the different communication systems (3G) and the optimization technique, its main components, and its fundamental characteristics radios were introduced.

Keywords: UMTS, UTRAN, WCDMA, optimization

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Authors: Wu Qinglong, Zhou Qicai, Xiong Xiaolei, Zhang Richeng

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In order to achieve the layout and size optimization of the web members of tower crane boom, a truss topology and cross section size optimization method based on continuum is proposed considering three typical working conditions. Firstly, the optimization model is established by replacing web members with web plates. And the web plates are divided into several sub-domains so that periodic soft kill option (SKO) method can be carried out for topology optimization of the slender boom. After getting the optimized topology of web plates, the optimized layout of web members is formed through extracting the principal stress distribution. Finally, using the web member radius as design variable, the boom compliance as objective and the material volume of the boom as constraint, the cross section size optimization mathematical model is established. The size optimization criterion is deduced from the mathematical model by Lagrange multiplier method and Kuhn-Tucker condition. By comparing the original boom with the optimal boom, it is identified that this optimization method can effectively lighten the boom and improve its performance.

Keywords: tower crane boom, topology optimization, size optimization, periodic, SKO, optimization criterion

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Authors: Mengxiao Li, Johnson Zhang

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Currently, topology optimization technique is widely used to define the layout design of structures that are presented as truss-like topologies. However, due to the difficulty in combining optimization technique with more realistic material models where their nonlinear properties should be considered, the achieved optimized topologies are commonly unable to apply straight towards the practical design problems. This study presented an optimization procedure of composite structures where different elastic stiffness, yield criteria, and hardening models are assumed for the candidate materials. From the results, it can be concluded that a more explicit modeling has the significant influence on the resulting topologies. Also, the isotropic or kinematic hardening is important for elastoplastic structural optimization design. The capability of the proposed optimization procedure is shown through several cases.

Keywords: topology optimization, material composition, nonlinear modeling, hardening rules

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Authors: Akintayo E. Akinsunmade

Abstract:

The digestion optimization algorithm is a novel biological-inspired metaheuristic method for solving complex optimization problems. The algorithm development was inspired by studying the human digestive system. The algorithm mimics the process of food ingestion, breakdown, absorption, and elimination to effectively and efficiently search for optimal solutions. This algorithm was tested for optimal solutions on seven different types of optimization benchmark functions. The algorithm produced optimal solutions with standard errors, which were compared with the exact solution of the test functions.

Keywords: bio-inspired algorithm, benchmark optimization functions, digestive system in human, algorithm development

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Authors: S. Adhirai, R. P. Mahapatra, Paramjit Singh

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Optimization is an important tool in making decisions and in analysing physical systems. In mathematical terms, an optimization problem is the problem of finding the best solution from among the set of all feasible solutions. The paper discusses the Whale Optimization Algorithm (WOA), and its applications in different fields. The algorithm is tested using MATLAB because of its unique and powerful features. The benchmark functions used in WOA algorithm are grouped as: unimodal (F1-F7), multimodal (F8-F13), and fixed-dimension multimodal (F14-F23). Out of these benchmark functions, we show the experimental results for F7, F11, and F19 for different number of iterations. The search space and objective space for the selected function are drawn, and finally, the best solution as well as the best optimal value of the objective function found by WOA is presented. The algorithmic results demonstrate that the WOA performs better than the state-of-the-art meta-heuristic and conventional algorithms.

Keywords: optimization, optimal value, objective function, optimization problems, meta-heuristic optimization algorithms, Whale Optimization Algorithm, implementation, MATLAB

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Authors: Mazura Mokhtar, Adibah Shuib, Daud Mohamad

Abstract:

Portfolio optimization problem has received a lot of attention from both researchers and practitioners over the last six decades. This paper provides an overview of the current state of research in portfolio optimization with the support of mathematical programming techniques. On top of that, this paper also surveys the solution algorithms for solving portfolio optimization models classifying them according to their nature in heuristic and exact methods. To serve these purposes, 40 related articles appearing in the international journal from 2003 to 2013 have been gathered and analyzed. Based on the literature review, it has been observed that stochastic programming and goal programming constitute the highest number of mathematical programming techniques employed to tackle the portfolio optimization problem. It is hoped that the paper can meet the needs of researchers and practitioners for easy references of portfolio optimization.

Keywords: portfolio optimization, mathematical programming, multi-objective programming, solution approaches

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Authors: Elham Kazemi

Abstract:

Quadratic Assignment Problem (QAP) is one the combinatorial optimization problems about which research has been done in many companies for allocating some facilities to some locations. The issue of particular importance in this process is the costs of this allocation and the attempt in this problem is to minimize this group of costs. Since the QAP’s are from NP-hard problem, they cannot be solved by exact solution methods. Cuckoo Optimization Algorithm is a Meta-heuristicmethod which has higher capability to find the global optimal points. It is an algorithm which is basically raised to search a continuous space. The Quadratic Assignment Problem is the issue which can be solved in the discrete space, thus the standard arithmetic operators of Cuckoo Optimization Algorithm need to be redefined on the discrete space in order to apply the Cuckoo Optimization Algorithm on the discrete searching space. This paper represents the way of discretizing the Cuckoo optimization algorithm for solving the quadratic assignment problem.

Keywords: Quadratic Assignment Problem (QAP), Discrete Cuckoo Optimization Algorithm (DCOA), meta-heuristic algorithms, optimization algorithms

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Authors: Felipe Haro, Soheila Antar

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This paper introduces a custom optimization experiment developed in AnyLogic, based on genetic algorithms, designed to ensure reliable optimization results by incorporating Montecarlo simulations and achieving a specified confidence level. To validate the custom experiment, we compared its performance with AnyLogic's built-in OptQuest optimization method across three distinct problems. Statistical analyses, including Welch's t-test, were conducted to assess the differences in performance. The results demonstrate that while the custom experiment shows advantages in certain scenarios, both methods perform comparably in others, confirming the custom approach as a reliable and effective tool for optimization under uncertainty.

Keywords: optimization, confidence intervals, Montecarlo simulation, optQuest, AnyLogic

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Authors: Sourabh Joshi, Tarun Sharma, Anurag Sharma

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Ant Colony Optimization is heuristic Algorithm which has been proven a successful technique applied on number of combinatorial optimization problems. Two variants of Ant Colony Optimization algorithm named Ant System and Max-Min Ant System are implemented in MATLAB to solve travelling Salesman Problem and the results are compared. In, this paper both systems are analyzed by solving the some Travelling Salesman Problem and depict which system solve the problem better in term of cost and time.

Keywords: Ant Colony Optimization, Travelling Salesman Problem, Ant System, Max-Min Ant System

Procedia PDF Downloads 483