摘要
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-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.
出处
《计算机工程与应用》
CSCD
北大核心
2003年第10期79-82,共4页
Computer Engineering and Applications
基金
国家自然科学基金(编号:60133010
60073043
70071042)
关键词
演化算法
多父体杂交
适应值共享
目标函数
最优解
多目标优化问题
Evolutionary Computation,Multi-Objective optimization,Pareto -optimal set,Multi-parent-crossover,Fitness Sharing
