Feasibility preserving constraint-handling strategies for real parameter evolutionary optimization

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Argitaratua izan da:Computational Optimization and Applications vol. 62, no. 3 (Dec 2015), p. 851
Egile nagusia: Padhye, Nikhil
Beste egile batzuk: Mittal, Pulkit, Deb, Kalyanmoy
Argitaratua:
Springer Nature B.V.
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Sarrera elektronikoa:Citation/Abstract
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100 1 |a Padhye, Nikhil 
245 1 |a Feasibility preserving constraint-handling strategies for real parameter evolutionary optimization 
260 |b Springer Nature B.V.  |c Dec 2015 
513 |a Journal Article 
520 3 |a (ProQuest: ... denotes formulae and/or non-USASCII text omitted; see image) 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. 
653 |a Optimization 
653 |a Genetic algorithms 
653 |a Differential scanning calorimetry 
653 |a Computer science 
653 |a Analysis 
653 |a Studies 
653 |a Variables 
653 |a Computer engineering 
653 |a Linear programming 
653 |a Methods 
653 |a Values 
653 |a Mutation 
653 |a Feasibility 
700 1 |a Mittal, Pulkit 
700 1 |a Deb, Kalyanmoy 
773 0 |t Computational Optimization and Applications  |g vol. 62, no. 3 (Dec 2015), p. 851 
786 0 |d ProQuest  |t ABI/INFORM Global 
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