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constraint-handling in genetic algorithms through the use of dominance-based tournament selection

ADVANCED ENGINEERING INFORMATICS 2002年第3期16卷 193-203页

作者： Coello, CAC Montes, EM CINVESTAV IPN Dept Ingn Electr Secc Computac Mexico City 07300 DF Mexico

In this paper, we propose a dominance-based selection scheme to incorporate constraints into the fitness function of a genetic algorithm used for global optimization. The approach does not require the use of a penalty function and, unlike traditional evolutionary multiobjective optimization techniques, it does not require niching (or any other similar approach) to maintain diversity in the population. We validated the algorithm using several test functions taken from the specialized literature on evolutionary optimization. The results obtained indicate that the approach is a viable alternative to the traditional penalty function, mainly in engineering optimization problems. (C) 2002 Elsevier Science Ltd. All rights reserved.

关键词： genetic algorithms constraint-handling multiobjective optimization self-adaptation evolutionary optimization numerical optimization

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constraint-handling using an evolutionary multiobjective optimization technique

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CIVIL ENGINEERING AND ENVIRONMENTAL SYSTEMS 2000年第4期17卷 319-346页

作者： Coello, CAC Lab Nacl Informat Avanzada Xalapa 91090 Veracruz Mexico

In this paper, we introduce the concept of non-dominance (commonly used in multiobjective optimization) as a way to incorporate constraints into the fitness function of a genetic algorithm. Each individual is assigned a rank based on its degree of dominance over the rest of the population. Feasible individuals are always ranked higher than infeasible ones, and the degree of constraint violation determines the rank among infeasible individuals. The proposed technique does not require fine tuning of factors like the traditional penalty function and uses a self-adaptation mechanism that avoids the traditional empirical adjustment of the main genetic operators (i.e., crossover and mutation).

关键词： genetic algorithms constraint-handling multiobjective optimization self-adaptation evolutionary optimization numerical optimization

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constraint-handling through multi-objective optimization: The hydrophobic-polar model for protein structure prediction

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COMPUTERS & OPERATIONS RESEARCH 2015年 53卷 128-153页

作者： Garza-Fabre, Mario Rodriguez-Tello, Eduardo Toscano-Pulido, Gregorio CINVESTAV Tamaulipas Informat Technol Lab Tamaulipas 87130 Mexico

In the multi-objective approach to constraint-handling, a constrained problem is transformed into an unconstrained one by defining additional optimization criteria to account for the problem constraints. In this paper, this approach is explored in the context of the hydrophobic-polar model, a simplified yet challenging representation of the protein structure prediction problem. Although focused on such a particular case of study, this research work is intended to contribute to the general understanding of the multi-objective constraint-handling strategy. First, a detailed analysis was conducted to investigate the extent to which this strategy impacts on the characteristics of the fitness landscape. As a result, it was found that an important fraction of the infeasibility translates into neutrality. This neutrality defines potentially shorter paths to move through the landscape, which can also be exploited to escape from local optima. By studying different mechanisms, the second part of this work highlights the relevance of introducing a proper search bias when handling constraints by multi-objective optimization. Finally, the suitability of the multi-objective approach was further evaluated in terms of its ability to effectively guide the search process. This strategy significantly improved the performance of the considered search algorithms when compared with respect to commonly adopted techniques from the literature. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： constraint-handling Evolutionary multi-objective optimization Fitness landscape analysis Search bias Protein structure prediction Hydrophobic-polar model

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constraint-handling in nature-inspired numerical optimization: Past, present and future

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SWARM AND EVOLUTIONARY COMPUTATION 2011年第4期1卷 173-194页

作者： Mezura-Montes, Efren Coello Coello, Carlos A. Lab Nacl Informat Avanzada LANIA AC Rebsamen 80 Xalapa 91000 Veracruz Mexico Ctr Invest & Estudios Avanzados IPN CINVESTAV Dept Computac Evolutionary Computat Grp Mexicali 07360 Baja California Mexico

In their original versions, nature-inspired search algorithms such as evolutionary algorithms and those based on swarm intelligence, lack a mechanism to deal with the constraints of a numerical optimization problem. Nowadays, however, there exists a considerable amount of research devoted to design techniques for handling constraints within a nature-inspired algorithm. This paper presents an analysis of the most relevant types of constraint-handling techniques that have been adopted with nature-inspired algorithms. From them, the most popular approaches are analyzed in more detail. For each of them, some representative instantiations are further discussed. In the last part of the paper, some of the future trends in the area, which have been only scarcely explored, are briefly discussed and then the conclusions of this paper are presented. (C) 2011 Elsevier B.V. All rights reserved.

关键词： constraint-handling Differential evolution Evolution strategies Particle swarm optimization Genetic algorithms Evolutionary programming

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constraint-handling with Support Vector Decoders 5

Constraint-Handling with Support Vector Decoders

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5th International Conference on Agents and Artificial Intelligence (ICAART)

作者： Bremer, Jeorg Sonnenschein, Michael Carl von Ossietzky Univ Oldenburg D-26129 Oldenburg Germany

ISBN: (纸本)9783662444405;9783662444399

A comparably new application for support vector machines is their use for meta-modeling the feasible region in constrained optimization problems. Applications have already been developed to optimization problems from the smart grid domain. Still, the problem of a standardized integration of such models into (evolutionary) optimization algorithms was as yet unsolved. We present a new decoder approach that constructs a mapping from the unit hyper cube to the feasible region from the learned support vector model. Thus, constrained problems are transferred into unconstrained ones by space mapping for easier search. We present result from artificial test cases as well as simulation results from smart grid use cases for real power planning scenarios.

关键词： Smart grid constraint-handling Decoder constraint modeling SVDD

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constraint-handling techniques used with evolutionary algorithms 07

Constraint-handling techniques used with evolutionary algori...

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9th Annual Genetic and Evolutionary Computation Conference, GECCO 2007

作者： Coello, Coello, Carlos A Depto. de Computación Av. IPN No. 2508 Mexico D. F. 07360 Mexico

No abstract available

ISBN: (纸本)159593698X

No abstract available

关键词： constraint-handling evolutionary algorithms optimization

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Machining Parameters Optimization of Multi-Pass Face Milling Using a Chaotic Imperialist Competitive Algorithm with an Efficient constraint-handling Mechanism

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Computer Modeling in Engineering & Sciences 2018年第9期116卷 365-389页

作者： Yang Yang College of Engineering Huazhong Agricultural UniversityKey Laboratory of Agricultural Equipment in Mid-lower Yangtze RiverMinistry of Agriculture430070WuhanChina

The selection of machining parameters directly affects the production time,quality,cost,and other process performance measures for multi-pass *** of machining parameters is of great ***,it is a nonlinear constrained optimization problem,which is very difficult to obtain satisfactory solutions by traditional optimization methods.A new optimization technique combined chaotic operator and imperialist competitive algorithm(ICA)is proposed to solve this *** ICA simulates the competition between the *** is a population-based meta-heuristic algorithm for unconstrained optimization *** development operator based on chaotic sequence is introduced to improve the local search of ICA,while constraints handling mechanism is introduced and an imperialist-colony transformation policy is *** improved ICA is called chaotic imperialist competitive algorithm(CICA).A case study of optimizing machining parameters for multi-pass face milling operations is presented to verify the effectiveness of the proposed *** case is to optimize parameters such as speed,feed,and depth of cut in each pass have yielded a minimum total product ion *** depth of cut of optimal strategy obtained by CICA are 4 mm,3 mm,1 mm for rough cutting pass 1,rough cutting pass 1 and finish cutting pass,*** cost for each pass are$0.5366 US,$0.4473 US and$0.3738 *** optimal solution of CICA for various strategies with at=8 mm is$1.3576 *** results obtained with the proposed schemes are better than those of previous *** shows the superior performance of CICA in solving such ***,optimization of cutting strategy when the width of workpiece no smaller than the diameter of cutter is *** can be drawn that larger tool diameter and row spacing should be chosen to increase cutting efficiency.

关键词： Chaotic imperialist competitive algorithm constraint-handling mechanism multi-pass face milling machining parameters optimization cutting strategy

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Particle swarm optimization for solving engineering problems: A new constraint-handling mechanism

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2013年第4期26卷 1263-1273页

作者： Mazhoud, Issam Hadj-Hamou, Khaled Bigeon, Jean Joyeux, Patrice UJF Grenoble 1 Grenoble INP CNRS G SCOP UMR 5272 F-38031 Grenoble France

This paper addresses constrained and optimal engineering problems solved using an adapted particle swarm optimization (PSO) algorithm. In fact, a specific constraint-handling mechanism is presented. It consists of a closeness evaluation of the solutions to the feasible region. The total constraints violation is introduced as an objective function to minimize. Interval arithmetic is used to normalize the total violations. The resulting objective problem is solved using a simple lexicographic method. The new algorithm is called CVI-PSO for constraint violation with interval arithmetic PSO. The paper provides numerous experimental results based on a well-known benchmark and comparisons with previously reported results. Finally, a case study of the optimal design of an electrical actuator with several model reformulations is detailed. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Preliminary design Global optimization Particular swarm optimization constraint-handling Interval arithmetic Model reformulation

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Feasibility preserving constraint-handling strategies for real parameter evolutionary optimization

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2015年第3期62卷 851-890页

作者： Padhye, Nikhil Mittal, Pulkit Deb, Kalyanmoy MIT Dept Mech Engn Cambridge MA 02139 USA Indian Inst Technol Dept Elect Engn Kanpur 208016 Uttar Pradesh India Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA

Evolutionary algorithms (EAs) are being routinely applied for a variety of optimization tasks, and real-parameter optimization in the presence of constraints is one such important area. During constrained optimization EAs often create solutions that fall outside the feasible region;hence a viable constraint-handling strategy is needed. This paper focuses on the class of constraint-handling strategies that repair infeasible solutions by bringing them back into the search space and explicitly preserve feasibility of the solutions. Several existing constraint-handling strategies are studied, and two new single parameter constraint-handling methodologies based on parent-centric and inverse parabolic probability (IP) distribution are proposed. The existing and newly proposed constraint-handling methods are first studied with PSO, DE, GAs, and simulation results on four scalable test-problems under different location settings of the optimum are presented. The newly proposed constraint-handling methods exhibit robustness in terms of performance and also succeed on search spaces comprising up-to variables while locating the optimum within an error of . The working principle of the IP based methods is also demonstrated on (i) some generic constrained optimization problems, and (ii) a classic 'Weld' problem from structural design and mechanics. The successful performance of the proposed methods clearly exhibits their efficacy as a generic constrained-handling strategy for a wide range of applications.

关键词： constraint-handling Nonlinear and constrained optimization Particle swarm optimization Real-parameter genetic algorithms Differential evolution

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Adaptive boundary constraint-handling scheme for constrained optimization

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SOFT COMPUTING 2019年第17期23卷 8247-8280页

作者： Juarez-Castillo, Efren Acosta-Mesa, Hector-Gabriel Mezura-Montes, Efren Univ Veracruz Artificial Intelligence Res Ctr Sebastian Camacho 5 Ctr Xalapa 91000 Veracruz Mexico Technol Univ Huasteca Hidalguense Huejutla Chalahuiyapa S-N Huejutla 43000 Hidalgo Mexico

Nature-inspired optimization algorithms are meta-heuristics that mimic nature for solving optimization problems. Many optimization problems are constrained and have a bounded search space from which some solution vectors leave when the variation operators are applied. Therefore, the use of boundary constraint-handling methods (BCHM) is necessary in order to repair the invalid vectors. This paper presents an adaptive scheme to handling boundary constraints in constrained numerical optimization problems. The proposed adaptive scheme operates in two stages: At the first one, when there are still no feasible solutions, a BCHM that benefits the exploration of the search space is employed, and in the second stage, one of several BCHMs, according to their associated probabilities, is selected. The methods' probabilities are updated every learning period so that the methods that generate the best repaired solutions will have a greater chance of being selected. The proposed scheme has been tested within two nature-inspired optimization algorithms: Particle Swarm Optimization and Differential Evolution employing their canonical version as well as one state-of-the-art version specialized in constrained optimization. A set of sixty single-objective constrained real-parameter optimization problems are solved. The results show that this adaptive scheme has a major impact on the algorithm's performance, and it is able to promote better final results mainly within high-dimensional problems.

关键词： Boundary constraint-handling Differential Evolution Particle Swarm Optimization Adaptive

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