基于多父体杂交的多目标演化优化算法

多目标优化问题是演化计算领域的一个新热点。提出了一种求解Pareto最优解集的新算法,它既能较快地收敛,又能有效保持种群的多样性。新算法引入了“约束占优”的概念;采用多父体杂交算子(一种多父体非凸线性组合算子),最小淘汰压力策略(每次只淘汰群体中的一个最差个体),以及适应值共享的niche技术,这样既保证了近似解集对Pareto前沿的逼近,又保持了解集分布的均匀性。对一些代表性的BenchMark问题(包括凸的与非凸的、连续的与间断的、带约束的与不带约束的各种问题)数值试验都取得了很好的结果。

Multi-Parent-Crossover Based Evolutionary Multi-Objective Optimization

Multi-Objective optimization is a new focus of EC research.This paper puts forward a new algorithm,which can not only converge quickly,but also keep diversity among population efficiently,in order to find the Pareto-optimal set.This new algorithm replaces the worst individual with a newly-created one by″multi-parent crossover″.so that the population could approach the Pareto-optimal solutions in the end.At the same time ,this new algorithm adopts niching and fitness-sharing techniques to keep the population in a good distribution.The numerical experiments show that the algorithm is rather effective in solving some Benchmarks.No matter whether the Pareto front of problems is convex or non-convex,continuous or discontinuous,and the problems with constraints or not,the program turns out to do well.