论文信息 - An Evolutionary Algorithm Based on Stochastic Weighted Learning for Constrained Optimization

An Evolutionary Algorithm Based on Stochastic Weighted Learning for Constrained Optimization

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.

Lu Han | Xiande Liu | Jun Ye

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