一种基于反向学习的约束差分进化算法

差分进化算法是一种结构简单、易用且鲁棒性强的全局搜索启发式优化算法,它可以结合约束处理技术来解决约束优化问题.机器学习在进化算法中,经常可以引导种群的进化,而且被广泛地应用于无约束的差分进化算法中,但对于约束差分进化算法却很少有应用.针对这一情况,提出了一种基于反向学习的约束差分进化算法框架.该算法框架采用基于反向学习的机器学习方法,提高约束差分进化算法的多样性和加速全局收敛速度.最后把该算法框架植入了两个著名的约束差分进化算法:(μ+λ)-CDE和ECHT,并采用CEC 2010的18个Benchmark函数进行了实验评估,实验结果表明:与(μ+λ)-CDE和ECHT相比,植入后的算法具有更强的全局搜索能力、更快的收敛速度和更高的收敛精度.

Constrained D ifferentiaI EvoIution Using Opposition-Based Learning

Differential evolution is a global heuristic algorithm,which is simple,easy-to-use and robust in practice. Combining with the constraint-handling techniques,it can solve constrained optimization problems.Machine learning often guides population to evolve in the evolution computation,and is widely applied to unconstrained differential evolution algo-rithm.However,machine learning is rarely applied to constrained differential evolution algorithm,so this paper proposed a constrained differential evolution algorithm framework using opposition-based learning.The algorithm can improve the diver-sity and convergence of differential evolution.At last,the proposed algorithm framework is applied to two popular constrain-ed differential evolution variants,that is (μ+λ)-CDE and ECHT-DE.And 18 benchmark functions presented in CEC 2010 are chosen as the test suite,experimental results show that comparing with (μ+λ)-CDE and ECHT-DE,our algorithms are able to improve global search ability,convergence speed and accuracy in the majority of test cases.