33 مقاله ISI با موضوع الگوریتم ژنتیک Genetic Algorithm

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33 مقاله ISI با موضوع الگوریتم ژنتیک

Genetic Algorithm Articles

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MODELING AND MULTI-OBJECTIVE OPTIMIZATION OF FORWARD-CURVED

MODELING AND MULTI-OBJECTIVE OPTIMIZATION OF FORWARD-CURVEDBLADE CENTRIFUGAL FANS USING CFD AND NEURAL NETWORKSAbolfazl Khalkhali, Mehdi Farajpoor, Hamed Safikhani2011
ABSTRACTIn the present study, multi-objective optimization of Forward-Curved (FC) blade centrifugal fans is performed in three steps. In the first step, Head rise (HR) and the Head loss (HL) in a set of FC centrifugal fan is 
numerically investigated using commercial software NUMECA. Two meta-models based on the evolved group method of data handling (GMDH) type neural networks are obtained, in the second step, for modeling of HR 
and HL with respect to geometrical design variables. Finally, using the obtained polynomial neural networks, multiobjective genetic algorithms are used for Pareto based optimization of FC centrifugal fans considering two 
conflicting objectives, HR and HL.  Keywords: forward-curved blade centrifugal fan; multi-objective optimization; CFD; GMDH; genetic algorithms
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MULTIOBJECTIVE OPTIMIZATION OF LOW-THRUST TRAJECTORIES USING A GENETIC ALGORITHM HYBRID

Matthew A. Vavrina* and Kathleen C. Howell†In low-thrust, gravity-assist trajectory design, two objectives are often equally important: maximization of final spacecraft mass and minimization of time-of-flight. Generally, these objectives are coupled and competing. 
Designing the trajectory that is best-suited for a mission typically requires a compromise between the objectives. However, optimizing even a single objective in the complex design space of low-thrust, gravity-assist 
trajectories is difficult. The technique in this development hybridizes a multiobjective genetic algorithm (NSGA-II) and an efficient, calculus-based direct method (GALLOP). The hybrid algorithm capitalizes on the benefits 
of both methods to generate a representation of the Pareto front of near-globally optimal solutions.

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Applications of Genetic Algorithm for Solving Multi-Objective Optimization Problems in Chemical Engineering

By Abhijit Tarafder, Ajay K. Ray and Santosh K. Gupta"The search is always on for more powerful algorithms for multiobjective optimization, with inspiration coming from diverse areas of human endeavour. One very recent example is the use of the jumping gene adaptation 
in multi-objective optimization"

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A multi-objective optimization approach to groundwater management using genetic algorithm

T. A. Saafan1, S. H. Moharram1, M. I. Gad2 and S. KhalafAllah1*-Department of Irrigation and Hydraulics, Faculty of Engineering, Mansoura University, Egypt.-Hydrology Division, Desert Research Center, Cairo, Egypt.2011Management of groundwater resources is very important for regions where freshwater supply is naturally limited. Long-term planning of groundwater usage requires method-based new decision support tools. These tools 
must be able to predict the change in the groundwater storage with sufficient accuracy, and must allow exploring management scenarios with respect to different criteria such as sustainability and cost. So, a multi-objective 
optimization algorithm is used for groundwater management problem. In this paper, a genetic algorithm with two additional techniques, Pareto optimality ranking and fitness sharing, is applied to simultaneously maximize 
the pumping rate and minimize pumping cost. The methodology proposed has more Pareto optimal solutions. However, it is desirable to get, and to find the ones scattered uniformly over the Pareto frontier in order to 
provide a variety of compromise solutions to help the decision maker. A groundwater resources management model in which performed through a combined simulation-optimization model is used. This multiobjective 
genetic algorithm (MOGA) of optimization combines the modular three-dimensional finitedifference (MODFLOW) and genetic algorithm (GA). MOGA model is applied in El-Farafra oasis, Egypt to develop the maximum 
pumping rate and minimum operation cost as well as the prediction of the future changes in both pumping rate and pumping operation cost. It also makes a feasible solution in groundwater management. Finally, a 
compromise solution is presented from a set of Pareto optimal solutions.
Key words: Groundwater management, multi-objective optimization, genetic algorithm, Farafra oasis, Egypt
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Multi-objective optimization using genetic algorithms: A tutorial 

Abdullah Konaka,
, David W. Coitb, Alice E. SmithcaInformation Sciences and Technology, Penn State Berks, USA-Department of Industrial and Systems Engineering, Rutgers University-Department of Industrial and Systems Engineering, Auburn University2006
AbstractMulti-objective formulations are realistic models for many complex engineering optimization problems. In many real-life problems, objectives under consideration conflict with each other, and optimizing a particular 
solution with respect to a single objective can result in unacceptable results with respect to the other objectives. A reasonable solution to a multi-objective problem is to investigate a set of solutions, each of which satisfies 
the objectives at an acceptable level without being dominated by any other solution. In this paper, an overview and tutorial is presented describing genetic algorithms (GA) developed specifically for problems with multipleobjectives. They differ primarily from traditional GA by using specialized fitness functions and introducing methods to promote solution diversity

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A Cellular Genetic Algorithm for Multiobjective Optimization

A. J. Nebro, J. J. Durillo, F. Luna, B. Dorronsoro, and E. Alba
Abstract. This paper introduces a new cellular genetic algorithm for solving multiobjective contin- uous optimization problems. Our approach is characterized by using an external archive to store non-dominated solutions 
and a feedback mechanism in which solutions from this archive randomly replaces existing individuals in the population after each iteration. The result is a simple and elitist algorithm called MOCell. Our proposal has been 
evaluated with both constrained and unconstrained problems and compared against NSGA-II and SPEA2, two state-of-the-art evolutionary multiob- jective optimizers. For the used benchmark, preliminary experiments 
indicate that MOCell obtains competitive results in terms of convergence, and it clearly outperforms the other two compared al- gorithms concerning the diversity of solutions along the Pareto front.
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Financial Multi Objective Optimization, A Genetic Algorithm Approach
AbstractThere are a wide range of financial optimization problems that involve multiple objectives at the same time. In spite of several methods to overcome this situation and tofind the best solution, this paper propose an effective and usable method by considering a genetic algorithm approach. Of course the proposed method can be used by any multipleobjective programming, but here we focus on financial area. Keywords: Finance, Multiple Objective Problems, Genetic Algorithm.
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Multi-objective optimization by genetic algorithms in H∞/LPV control of semi-active suspensionA. L. Do, O

Sename, L. Dugard and B. Soualmi
Abstract In semi-active suspension control, comfort and road holding are two essential but conflicting performance objectives. In a previous work, the authors proposed an LPV formulation for semi-active suspension control of a 
realistic nonlinear suspension model where the nonlinearities (i.e the bi-viscous and the hysteresis) were taken into account; an H1/LPV controller to handle the comfort and road holding was also designed. The present 
paper aims at improving the method of Do et al. (2010) by using Genetic Algorithms (GAs) to select the optimal weighting functions for the H1/LPV synthesis. First, a general procedure for the optimization of the 
weighting functions for the H1/LPV synthesis is proposed and then applied to the semi-active suspension control. Thanks to GAs, the comfort and road holding are handled using a single high level parameter and 
illustrated via the Pareto optimality. The simulation results performed on a nonlinear vehicle model emphasize the efficiency of the method. 
Keywords: Genetic algorithms, H1 control, LPV systems, semi-active suspensions

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Genetic Algorithms for Multiobjective Optimization:Formulation, Discussion and GeneralizationCarlos

M. Fonsecay and Peter J. Flemingz
AbstractThe paper describes a rank-based tness assignment method for Multiple Objective Genetic Algorithms (MOGAs). Conventional niche formation methods are extended to this class of multimodal problems and theory forsetting the niche size is presented. The tness assignment method is then modied to allow direct intervention of an external decision maker (DM). Finally, the MOGA is generalised further: the genetic algorithm is seenas the optimizing element of a multiobjective optimization loop, which also comprises the DM. It is the interaction between the two that leads to the determination of a satisfactory solution to the problem. Illustrative 
results of how the DM can interact with the genetic algorithm are presented. They also show the ability of the MOGA to uniformly sample regions of the trade-o surface. 
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Multiobjective Optimization using Fuzzy Genetic Algorithms

Pong Kuan Peng and Muhamad Suzuri Hitam
IntroductionGenetic algorithms (GAs) is a well established method that could provide solution either single objective optimization or multi-objective optimization problems. However, GA is also known to suffer from excessively slow or 
premature convergence before providing an accurate solution due to the its internal algorithm design; not utilizing a priori knowledge and not exploiting local search information [1]. Practitioners are also experiencing 
tedious process of fine tuning the GA before it converged to satisfying solution. For example, fine tuning the probability of crossover, probability of mutation, setting the number of generation, etc. before the GA finally 
settles to an accurate solution. These factors have led many researchers trying to find way to improve GA to convergence.
Keywords: Multiobjective optimization, fuzzy Logic and genetic algorithms.
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A non-dominated sorting hybrid algorithm for multi-objective optimization of engineering problems

Hossein Ghiasi*, Damiano Pasini and Larry Lessard2010
Among numerous multi-objective optimization algorithms, the Elitist non-dominated sorting genetic algorithm (NSGA-II) is one of the most popular methods due to its simplicity, effectiveness and minimum involvement 
of the user. This article develops a multi-objective variation of the Nelder-Mead simplex method and combines it with NSGA-II in order to improve the quality and spread of the solutions. The proposed hybrid algorithm, 
called non-dominated sorting hybrid algorithm (NSHA), is compared with NSGA-II on several constrained and unconstrained test problems. The higher convergence rate and the wider spread of solutions obtained with 
NSHA make this algorithm a good candidate for engineering problems that require time-consuming simulation and analysis. To demonstrate this fact, NSHA is applied to the design of a carbon fibre bicycle stem 
simultaneously optimized for strength, weight and processing time. 
Keywords: multi-objective optimization; genetic algorithm; non-dominated sorting; hybrid algorithm
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Structural Topology Optimization using Multi-objective Genetic Algorithm with Constructive Solid Geometry Representation

Faez Ahmed, Kalyanmoy Deb, Bishakh Bhattacharya2012
AbstractThis paper proposes a constructive solid geometry based approach (named CG-TOM) for structural topology optimization. The use of evolutionary algorithms for topology optimisation is considered ineective due to a 
considerably large number of topological design variables. To alleviate this problem, a novel representation scheme is proposed, which encodes the topology using position of few joints and width of segments connecting 
them. Union of overlapping rectangular primitives is calculated using constructive solid geometry technique to obtain the structural topology and ne triangular mesh is used to improve the accuracy of nite element 
compliance approximation. A valid topology in the design domain is ensured by representing the topology as a connected simple graph of nodes. A graph repair operator is applied to ensure a physically meaningful 
connected structure. The algorithm is integrated with single and multi-objective genetic algorithm and its performance is compared with those of other methods like SIMP. The trade-o between compliance and material 
availability is explored in the multi-objective analysis and common design principles for optimized solutions are discussed. The proposed method is generic and can be extended to any two or three-dimensional topology 
optimization problem by utilizing diferent shape primitives.
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Multi-objective optimization using evolutionary algorithms for qualitative and quantitative control of urban runoffS.

Oraei Zare1, B. Saghafian1, A. Shamsai2, and S. Nazif32012
AbstractUrban development and affects the quantity and quality of urban floods. Generally, flood management include planning and management activities to reduce the harmful effects of floods on people, environment and economy is in a region. In recent years, 5 a concept called Best Management Practices (BMPs) has been widely used for urban flood control from both quality and quantity aspects. In this paper, three objective functionsrelating to the quality of runoff (including BOD5 and TSS parameters), the quantity of runoff (including runoff volume produced at each sub-basin) and expenses (including construction and maintenance costs of BMPs) were employed in the optimization 10 algorithm aimed at finding optimal solution MOPSO and NSGAII optimization methods were coupled with the SWMM urban runoff simulation model. In the proposed structure for NSGAII algorithm, a continuous structure and intermediate crossover was used because they perform better for improving the optimization model efficiency. To compare the performance of the two optimization algorithms, 15 a number of statistical indicators were computed for the last generation of solutions. Comparing the pareto solution resulted from each of the optimization algorithms indicated that the NSGAII solutions was more optimal. Moreover, the standard deviation of solutions in the last generation had no significant differences in comparison with MOPSO
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Multiobjective Optimization of Co-Clustering Ensembles Francesco Gullo

2012
AbstractCo-clustering is a machine learning task where the goal is to simultaneously develop clusters of the data and of their respective features. We address the use of coclustering ensembles to establish a consensus co-clusteringover the data. As is obvious from its name, co-clustering is naturally multiobjective. Previous work tackled the problem using both rudimentary multiobjective optimization and expectation maximization, then later agradient ascent approach which outperformed both of them. In this paper we develop a new preference-based multiobjective optimization algorithm to compete with the gradient ascent approach. Unlike this gradient ascent algorithm, our approach once again tackles the coclustering problem with multiple heuristics, but also applies the gradient ascent algorithm’s joint heuristic as a preference selection procedure. As a result, we are able to significantly outperform the gradient ascent algorithm on feature clustering and on problems with smaller datasets.
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MULTI-OBJECTIVE GENETIC ALGORITHM USING CLASS-BASED ELITIST APPROACHP.

Maragathavalli 1 and S. Kanmani 2

ABSTRACTAn approach named Class-Based Elitist Genetic Algorithm is presented in this paper. The test data is being generated for object-oriented programs using evolutionary techniques. A class-based algorithm derived from class control-flow graph (CCFG) is used for testing object-oriented software. Evolutionary techniques have been used for solving most of the software engineering problems. Evolutionary testing technique which is based on theory of evolution like reproduction, mutation, recombination, and selection is used to generate test cases in CBEGA. Evolutionary Algorithm applies these techniques repeatedly to a set of individuals called population to obtain optimal solution in a global search space with minimum search time. Multi-objective optimization involves optimizing a number of objectives simultaneously like time, cost and fault detection capability. The objectives considered here for optimization are maximum path coverage, minimum test suite size, and minimum execution time. For experiments, the data are taken from Siemens’ test suite which shows better path coverage results like 96% in CBEGA and are compared with simple GA which gives only 88% path coverage for a set of sample java classes. 
KEYWORDSMulti-objective Genetic Algorithm, Search Time, CBEGA, Test-suite Reduction, Path Coverage, Elitism
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Multi-objective parametric optimization of powder mixed electro-discharge machining using response surface methodology and non-dominated sorting genetic algorithm

SOUMYAKANT PADHEE1, NIHARRANJAN NAYAK2,S K PANDA2, P R DHAL2 and S S MAHAPATRA2,∗
Abstract Powder mixed electro-discharge machining (EDM) is being widely used in modern metal working industry for producing complex cavities in dies and moulds which are otherwise difficult to create by conventional machining route. It has been experimentally demonstrated that the presence of suspended particle in dielectric fluid significantly increases the surface finish and machining efficiency of EDM process. Concentration of powder (silicon) in the dielectric fluid, pulse on time, duty cycle, and peak current are taken as independent variables on which the machining performance was analysed in terms of material removal rate (MRR) and surface roughness (SR). Experiments have been conducted on an EZNC fuzzy logic Die Sinking EDM machine manufactured by Electronica Machine Tools Ltd. India. A copper electrode having diameter of 25 mm is used to cut EN 31 steel for one hour in each trial. Response surface methodology (RSM) is adopted to study the effect of independent variables on responses and develop predictive models. It is desired to obtain optimal parameter setting that aims at decreasing surface roughness along with larger material removal rate. Since the responses are conflicting in nature, it is difficult to obtain a single combination of cutting parameters satisfying both the objectives in any one solution. Therefore, it is essential to explore the optimization landscape to generate the set of dominant solutions. Non-sorted genetic algorithm (NSGA) has been adopted to optimize the responses such that a set of mutually dominant solutions are found over a wide range of machining parameters.
Keywords. Powder mixed EDM; surface roughness; material removal rate; non-sorted genetic algorithm; response surface methodology.

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Parallelizing Multi-objective Evolutionary Genetic AlgorithmsG. N. Shinde Member IAENG

, Sudhir B. Jagtap and Subhendu Kumar Pani
Abstract: In this paper a hybrid parallel multi-objective genetic algorithm is proposed for solving 0/1 knapsack problem. Multiobjective problems with non-convex and discrete Pareto front can take enormous computation time to converge to the true Pareto front. Hence, the classical multi-objective genetic algorithms (MOGAs) (i.e., non- Parallel MOGAs) may fail to solve such intractable problem in a reasonable amount of time. The proposed hybrid model will combine the best attribute of island and Jakobovic master slave models. We conduct an extensive experimental study in a multi-core system by varying the different size of processors and the result is compared with basic parallel model i.e., master-slave model which is used to parallelize NSGA-II. The experimental results confirm that the hybrid model is showing a clear edge over master-slave model interms of processing time and approximation to the true Pareto front. Index Terms: Multi-Objective Genetic Algorithm, Parallel Processing Techniques, NSGA-II, 0/1 Knapsack Problem, Trigger Model, Cone Separation Model, Island Model
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A Functional Framework for Solving Multi-objective Optimization Problems using Genetic AlgorithmsDaan van Beek
ABSTRACTBy studying single-objective genetic algorithms and multi-objective genetic algorithms this paper determines the functions needed to update existing single-objective genetic algorithms programmed in functional languages 
in order to make them applicable to multi-objective problems. By performing a literature study knowledge about genetic algorithms and their special multi-objective versions was collected and existing single-objective 
genetic algorithm frameworks where examined for missing functionality. Using this knowledge a way was derived to update single-objective genetic algorithm frameworks. With the results of this paper one is able to 
update a single-objective framework in order to make it applicable to multi-objective problems

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A NON-ELITIST MULTI OBJECTIVE GENETIC ALGORITHM FOR AXIAL COMPRESSOR STAGE OPTIMIZATIONG

.CHAITANYA

Abstract:A non-dominated sorted Genetic Algorithm approach proposed by Goldberg and later refined by Srinivas & Deb has been implemented with appropriate sharing function value for stage optimization of axial compressor. The objectives for the multi objective problem are Stage Efficiency, Inlet stage specific Area & Stall margin Coefficient. Jin Shik Lim and Myung kyoon Chung performed the optimization axial compressor stage taking two objective functions, stage efficiency and stage weight into consideration and by taking eight design variables. In the present approach the problem is modeled as a three objective function problem with five design variables. NSGA technique is implemented and Results were analyzed for non dominated solution fronts among the three objective functions and sensitivity of design variables on objective functions has been studied.
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Multiobjective Simulation Optimisation in Software Project Management

Mercedes Ruiz

ABSTRACT:

Background: Traditionally, simulation has been used by project managers in optimising decision making. However, current simulation packages only include simulation optimisation which consider a single objective (or multiple objectives combined into a single fitness function). Although useful, such single optimisation approaches do not seem to be enough in a field such as software project management where the optimisation of several conflicting objectives is a frequent task.• Aim: This paper aims to describe an approach that consists of using multiobjective optimisation techniques via simulation in order to help software project managers find the best values for initial team size and schedule estimates for a given project so that cost, time and productivity are optimised. • Method: Using a System Dynamics (SD) simulation model of a software project, the sensitivity of the output variables regarding productivity, cost and schedule using different initial team size and schedule estimationsis determined. The generated data is combined with a well-known multiobjective optimisation algorithm, called NSGA-II, to find optimal solutions for the output variables, i.e., development time, cost andproductivity. • Results: The NSGA-II algorithm was able to quickly converge to a set of optimal solutions (Pareto front)Part of this work was carried out while visiting Oxford Brookes University composed of multiple and conflicting variables from a medium size software project simulation model.• Conclusions: Multiobjective optimisation and SD simulation modeling are complementary techniques that can generate the Pareto front needed by project managersfor decision making. Furthermore, visual representations of such solutions in two or three dimensions are intuitive and can help project managers in their decision making process.
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-Sustainable land use optimization using Boundary-based Fast Genetic Algorithm 

Kai Cao a,b,c,⇑, Bo Huang a, Shaowen Wangc,d, Hui Lin e

2011

a b s t r a c tUnder the notion of sustainable development, a heuristic method named as the Boundary-based Fast Genetic Algorithm (BFGA) is developed to search for optimal solutions to a land use allocation problem with multiple objectives and constraints. Plans are obtained based on the trade-off among economic benefit, environmental and ecological benefit, social equity including Gross Domestic Product (GDP), conversion cost, geological suitability, ecological suitability, accessibility, Not In My Back Yard (NIMBY) influence, compactness, and compatibility. These objectives and constraints are formulated into a Multi-objective Optimization of Land Use (MOLU) model based on a reference point method (i.e. goal programming). This paper demonstrates that the BFGA is effective by offering the possibility of searching over tens of thousands of plans for trade-off sets of non-dominated plans. This paper presents an application of the model to the Tongzhou Newtown in Beijing, China. The results clearly evince the potential of the model in a planning support process by generating suggested near-optimal planning scenarios considering multi-objectives with different preferences.
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Prospect Indicator Based Evolutionary Algorithm for Multiobjective Optimization ProblemsPruet Boonma

Abstract:This paper proposes and evaluates an evolutionary multiobjective optimization algorithm (EMOA) that uses a new quality indicator, called the prospect indicator, for parent selection and environmental selection operators. The prospect indicator measures the potential of each individual to reproduce offspring that dominate itself and spread out in the objective space. The prospect indicator allows the proposed EMOA, PIBEA(Prospect Indicator Based Evolutionary Algorithm), to (1) maintain sufficient selection pressure, even in high dimensional MOPs, thereby improving convergence velocity toward the Pareto front, and (2) diversify individuals, even in high dimensional MOPs, thereby distributing individuals uniformly in the objective space. Experimental results show that PIBEA effectively performs its operators in high dimensional problems and outperforms three existing well-known EMOAs, NSGA-II, SPEA2 and AbYSS, in terms of convergence velocity, diversity of individuals, coverage of the Pareto front and performance stability
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GANETXL: A DSS GENERATOR FOR MULTIOBJECTIVE OPTIMISATION OF SPREADSHEET-BASED MODELSD

.A. Savić1, J. Bicik and M.S. Morley

AbstractWater management practice has benefited from the development of model-driven Decision Support Systems (DSS), and in particular those that combine simulation with single or multiple-objective optimisation tools. However, there are many performance, acceptance and adoption problems with these decision support tools caused mainly by misunderstandings between the communities of system developers and users. This paper presents a general-purpose decision-support system generator, GANetXL, for developing specific applications that require multiobjective optimisation of spreadsheet-based models. The system is developed as an Excel add-in that provides easy access to evolutionary multiobjective optimisation algorithms to non-specialists by incorporating an intuitive interactive graphical user interface that allows easy creation of specific decision-support application. GANetXL’s utility is demonstrated on two examples from water engineering practice, a simple water supply reservoir operation model with two objectives and a large combinatorial optimisation problem of pump scheduling in water distribution systems. The two examples show how GANetXL goes a long way toward closing the gap between the achievements in optimisation technology and the successful use of DSS in practice.
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Multi-Objective Optimization for the m-PDPTW: Aggregation Method With Use of Genetic Algorithm and Lower BoundsI.

Harbaoui Dridi, R. Kammarti, M. Ksouri, P. BorneImen Harbaoui Dridi

Abstract: The PDPTW is an optimization vehicles routing problem which must meet requests for transport between suppliers and customers in purpose to satisfy precedence, capacity and time constraints. We present, in this paper,a genetic algorithm for multi-objective optimization of a multi pickup and delivery problem with time windows (m-PDPTW), based on aggregation method and lower bounds. We propose in this sense a brief literature review of the PDPTW, present our approach to give a satisfying solution to the m-PDPTW minimizing the compromise between total travel cost and total tardiness time. Keywords: PDPTW, multi-objective, aggregation method, lower bounds.
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Comparing a Coevolutionary Genetic Algorithm for Multiobjective Optimization

Jason D. Lohn1, William F. Kraus2, Gary L. Haith3
Abstract: We present results from a study comparing a recently developed coevolutionary genetic algorithm (CGA) against a set of evolutionary algorithms using a suite of multiobjective optimization benchmarks. The CGA embodies competitive coevolution and employs a simple, straightforward target population representation and tness calculation based on developmental theory of learning. Because of these properties, setting up the additional population is trivial making implementation no more di- cult than using a standard GA. Empirical results using a suite of two-objective test functions indicate that this CGA performs well at nding solutions on convex, nonconvex, discrete, and deceptive Pareto-optimal fronts, while giving respectable results on a nonuniform optimization. On a multimodal Pareto front, the CGA yields poor coverage  across the Pareto front, yet nds a solution that dominates all the solutions produced by the eight other algorithms.
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The Fuzzy Genetic Strategy for Multiobjective Optimization

Krzysztof Pytel
Abstract:This paper presents the idea of fuzzy controlling of evolution in the genetic algorithm (GA) for multiobjective optimization. The genetic algorithm uses the Fuzzy Logic Controller (FLC), which manages the process of selection of individuals to the parents’ pool and mutation of their genes. The FLC modifies the probability of selection and mutation of individuals’ genes, so algorithms possess improved convergence and maintenance of suitable genetic variety of individuals. We accepted the wellknown LOTZ problem as a benchmark for experiments. In the experiments we investigated the operating time and the number of fitness function calls needed to finish optimization. We compared results of the elementary algorithms and the modified algorithm with the modification of probability of selection and mutation of individuals. Some good results have been obtainedduring the experiments.
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A Survey of Different Genetic Algorithms for Multi-Objective Optimization

S. SivaSathya1 and N. Aravindhu2

ABSTRACTMulti-Objective problems are realistic models for many complex engineering optimization problems. Different types of genetic algorithms provide best solution for solving these problems. They differ from traditional GA by using specialized fitness functions and by providing new methods to promote solution diversity. In this paper an overview of multi-objective problems is presented first. It also provides a comparative study on different types of genetic algorithms that are available for solving multi-objective problems. The merits and demerits of each algorithm is identified
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The Fuzzy Genetic System for Multiobjective Optimization

Krzysztof Pytel
Abstract:The article presents the idea of a hybrid system for multiobjective optimization. The system consists of the genetic algorithm and the fuzzy logic driver. The genetic algorithm realizes the process of multiobjective optimization. The fuzzy logic driver uses data aggregated by the genetic algorithm and controls the process of evolution by modifying the probability ofselection of individuals to the parental pool. The controlling of the evolution process makes it possible to choose the preferred area with pareto-optimal solution. In experiments we investigated the ability of the proposed system to search solutions in a given area of the search space. We compared the results of the standard genetic algorithm and the proposed system. The experiments showed that the proposed system is able to control the process of evolution toward pareto-optimal solutions in the given area of searching.

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Solving multiobjective optimization under bounds by genetic algorithms

Anon Sukstrienwong
Abstract For complex engineering optimizing problems, several problems are required to be controlled within the specific interval in which something can operate or act efficiently. Most researchers minimize the objective vector 
into a single objective and interested in the set known as Pareto optimal solution. However, in this paper is concerned with the application of genetic algorithm to solve multi-objective problems in which some objectives are requested to be balanced within its objective bounds. The proposed approach called genetic algorithms for objective boundary (GAsOB scheme) for searching the possible solutions for the particular multiobjectives problems. The elite technique is employed to enhance the efficiency of the algorithm. The experimental results have compared with the results derived by a linear search technique and traditional genetic algorithms through the search space. From the experimental results, GAsOB scheme generates the solution efficiently with customization of the number of eras and immigration rate. Keywords:Optimization, genetic algorithms, objective boundary, simulation, multiobjective optimization.
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Multiobjective evolutionary algorithms: A survey of the state of the art

Aimin Zhoua,∗, Bo-Yang Qub, Hui Li c, Shi

-Zheng Zhaob, Ponnuthurai Nagaratnam Suganthan b,Qingfu Zhangd2011a b s t r a c tA multiobjective optimization problem involves several conflicting objectives and has a set of Pareto optimal solutions. By evolving a population of solutions, multiobjective evolutionary algorithms (MOEAs)  are able to approximate the Pareto optimal set in a single run. MOEAs have attracted a lot of research effort during the last 20 years, and they are still one of the hottest research areas in the field of evolutionary computation. This paper surveys the development of MOEAs primarily during the last eight years. It covers algorithmic frameworks such as decomposition-based MOEAs (MOEA/Ds), memetic MOEAs, coevolutionary MOEAs, selection and offspring reproduction operators, MOEAs with specific search methods, MOEAs for multimodal problems, constraint handling and MOEAs, computationally expensive multiobjective optimization problems (MOPs), dynamic MOPs, noisy MOPs, combinatorial and discrete MOPs, benchmark problems, performance indicators, and applications. In addition, some future research issues are also presented
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Aerodynamic Multi-Objective Optimization Using Parallel Genetic Algorithm

G MANIKANDAN M ANANDA RAO1Abstract: Shape optimization of airfoil for the aerodynamic analysis of a low speed and low Reynolds number unmanned aerial vehicle wing is performed using parallel Genetic Algorithm. NACA 2412 chambered airfoil is 
chosen as zero generation airfoil. Real number coding is implemented for inputting seed value. Four modification operators are applied in this design space search method. The design space genes are control points of 
airfoil. Multiple fitness functions are utilized. Genetic Algorithm optimized airfoil profiles are used for the fabrication of composite material wing and are tested in the subsonic wind tunnel. The aerodynamic characteristics 
gleaned from experimental analysis are compared with base line airfoil and genetic algorithm optimized airfoil.Keywords: Parallel Genetic Algorithm; Cambered Aerofoil; Fitness Function; Composite Material; Wind Tunnel; Aerodynamic characteristics.
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 Spatial multi-objective land use optimization: extensions to the nondominated sorting genetic algorithm-II
Kai Cao a b , Michael Batty c , Bo Huang b , Yan Liu d , Le Yu e &2011A spatial multi-objective land use optimization model defined by the acronym ‘NSGA-II-MOLU’ or the ‘non-dominated sorting genetic algorithm-II for multi-objective optimization of land use’ is proposed for searching for optimal land use scenarios which embrace multiple objectives and constraints extracted from the requirements of users, as well as providing support to the land use planning process. In this application, we took the MOLU model which was initially developed to integrate multiple objectives and coupled this with a revised version of the genetic algorithm NSGA-II which is based on specific crossover and mutation operators. The resulting NSGA-II-MOLU model is able to offer the possibility of efficiently searching over tens of thousands of solutions for trade-off sets which define non-dominated plans on the classical Pareto frontier. In this application, we chose the example of Tongzhou New Town, China, to demonstrate how the model could be employed to meet three conflicting objectives based on minimizing conversion costs, maximizing accessibility, and maximizing compatibilities between land uses. Our case study clearly shows the ability of the model to generate diversified land use planning scenarios which form the core of a land use planning support system. It also demonstrates the potential of the model to consider more complicated spatial objectives and variables with open-ended characteristics. The breakthroughs in spatial optimization that this model provides lead directly to otherproperties of the process in which further efficiencies in the process of optimization, more vivid visualizations, and more interactive planning support are possible. These form directions for future research. 
Keywords: spatial land use optimization; NSGA-II-MOLU; planning support systems;  land use planning; multi-objective optimization; Tongzhou New Town, China