Extremal optimization algorithm with adaptive constants dealing techniques for constrained optimization problems
Jie Chen, Guo‐Qiang Zeng, Kang‐Di Lu, Wen-Wen Peng, Zhengjiang Zhang, Yuxing Dai · 2014
Extremal optimization (EO) has been successfully applied to a variety of combinatorial optimization problems. However, the applications of EO to constrained optimization problems are relatively rare. This paper proposes a novel EO algorithm with adaptive constraints dealing techniques called EO-ACD for constrained optimization problems. The basic idea behind EO-ACD is the combination of real-coded EO and adaptive dealing technique of constraints. The experimental results on 11 benchmark test functions have shown that the proposed EO-ACD is competitive or even better than the existing evolutionary algorithms such as population-based EO (PEO), stochastic ranking (SR) algorithm, simple multimembered evolution strategy (SMES) and genetic algorithm with two-phase genetic framework.