一种新的差分进化约束优化算法

doi:10.3969/j.issn.1001-2400.2011.01.008

Abstract

Abstract:

Most existing differential evolution algorithms for the Constrained Optimization Problem(COP) use the penalty function method to handle constrains, which depends strongly on the penalty parameter. So, this paper transforms the COP into two-objective multi-objective optimization by taking constraints as an objective function. Based on the concept of Pareto, the grades of individuals in population are prescribed so as to determine their selection probability in the process of “survival of the fittest”. In addition, when the algorithm gets into a local optimum, an infeasible solution replacing mechanism is also given to improve the search capability. The results of the 13 Standard tests show that compared to the Evolutionary Algorithm based on Homomorphous Maps (EAHM), Constraint Handling Differential Evolution (CHDE), Evolutionary Strategies based on Stochastic Ranking (ESSR) and Artificial Immune Response Constrained Evolutionary Strategy (AIRCES), the proposed algorithm has certain advantages in convergence speed and solution accuracy.

Key words: differential evolution algorithm, constrained optimization, multi-objective optimization

LIU Ruochen;JIAO Licheng;LEI Qifeng;FANG Lingfen. New differential evolution constrained optimization algorithm[J].J4, 2011, 38(1): 47-53.

References

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New differential evolution constrained optimization algorithm

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