An evolutionary algorithm based on stochastic weighted learning for constrained optimization

Authors:
Jun Ye;Xiande Liu;Lu Han
Affiliations:
Optics Engineering Dept., Huazhong Univ. of Science & Tech., Hubei, P.R. China;Optics Engineering Dept., Huazhong Univ. of Science & Tech., Hubei, P.R. China;Electronics & Info. Engineering Dept., Huazhong Univ. of Science & Tech., Hubei, P.R. China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Year:
2005

Citing 3
Cited 0

Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization

Evolutionary Computation
Evolutionary algorithms for constrained parameter optimization problems

Evolutionary Computation
Stochastic ranking for constrained evolutionary optimization

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we propose an evolutionary algorithm based on a single operator called stochastic weighted learning, i.e., each individual will learn from other individuals specified with stochastic weight coefficients in each generation, for constrained optimization. For handling equality and inequality constraints, the proposed algorithm introduces a learning rate adapting technique combined with a fitness comparison schema. Experiment results on a set of benchmark problems show the efficiency of the algorithm.