Proceedings Article10.1145/1543834.1543967
Stochastic ranking based differential evolution algorithm for constrained optimization problem
Ruochen Liu,Yong Li,Wei Zhang,Licheng Jiao +3 more
- 12 Jun 2009
- pp 887-890
8
TL;DR: The experiment results show that the proposed algorithm is capable of improving the search performance significantly in convergent speed and precision with respect to four other algorithms such as Evolutionary Algorithm based on Homomorphic Maps and Evolutionary Strategies based on Stochastic Ranking.
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Abstract: Based on differential evolution and stochastic ranking strategy, a new differential evolution algorithm for constrained optimization problem is proposed in this paper. The proposed algorithm reserves sub-optimal solutions in the process of population evolution, which effectively enhances the diversity of the population. The experiment results on 13 well-known benchmark problems show that the proposed algorithm is capable of improving the search performance significantly in convergent speed and precision with respect to four other algorithms such as Evolutionary Algorithm based on Homomorphous Maps (EAHM), Artificial Immune Response Constrained Evolutionary Strategy (AIRCES), Constraint Handling Differential Evolution (CHDE), and Evolutionary Strategies based on Stochastic Ranking (ESSR).
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References
Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art
TL;DR: A comprehensive survey of the most popular constraint-handling techniques currently used with evolutionary algorithms, including approaches that go from simple variations of a penalty function, to others, more sophisticated, that are biologically inspired on emulations of the immune system, culture or ant colonies.
2.1K
Stochastic ranking for constrained evolutionary optimization
Thomas Philip Runarsson,Xin Yao +1 more
TL;DR: A novel approach to balance objective and penalty functions stochastically, i.e., stochastic ranking, is introduced, and a new view on penalty function methods in terms of the dominance of penalty and objective functions is presented.
Variational methods for the solution of problems of equilibrium and vibrations
TL;DR: The equivalence between boundary value problems of partial differential equations on the one hand and problems of the calculus of variations on the other hand has been a central point in analysis as mentioned in this paper.
Evolutionary algorithms for constrained parameter optimization problems
TL;DR: Difficulty connected with solving the general nonlinear programming problem is discussed; several approaches that have emerged in the evolutionary computation community are surveyed; and a set of 11 interesting test cases are provided that may serve as a handy reference for future methods.