多目标优化的演化算法

Evolutionary Algorithms of Multi-Objective Optimization Problems

近年来 ,多目标优化问题求解已成为演化计算的一个重要研究方向 ,而基于Pareto最优概念的多目标演化算法则是当前演化计算的研究热点 .多目标演化算法的研究目标是使算法种群快速收敛并均匀分布于问题的非劣最优域 .该文在比较与分析多目标优化的演化算法发展的历史基础上 ,介绍基于Pareto最优概念的多目标演化算法中的一些主要技术与理论结果 ,并具体以多目标遗传算法为代表 ,详细介绍了基于偏好的个体排序、适应值赋值以及共享函数与小生境等技术 .此外 。

Multi-objective optimization (MOO) becomes an important research area of evolutionary computations in recent years, and the current research work focuses on the Pareto optimal-based MOO evolutionary approaches. The evolutionary MOO techniques are used to find the non-dominated set of solutions and distribute them uniformly in the Pareto front. After comparing and analyzing the developing history of evolutionary MOO techniques, this paper takes the multi-objective genetic algorithm as an example and introduces the main techniques and theoretical results for the Pareto optimal-based evolutionary approaches, mainly focusing on the preference based-individual ordering, fitness assignment, fitness sharing and niche size setting etc.. In addition, some problems that deserve further studying are also addressed.