基于(μ+1)演化策略的多目标优化算法

Multiobjective Optimization Algorithm Based on (μ+1) Evolutionary Strategy

使用(μ+1)演化策略求解多目标优化问题，利用群体中个体间的距离定义拥挤密度函数以衡量群体中个体的密集程度，个体适应值定义为个体的Pareto强度值和拥挤密度值之和。通过对测试函数的实验，验证了算法的可行性和有效性，该算法具有简单、稳健等特点。

This paper proposes (μ+1) evolutionary strategy for mutiobjective optimization problems. It uses crowding density to maintain a good spread of solution in the population. Sorting the distances between one point and other points in the population, the individuals crowding density is defined as the sum of the nearest and the second distances. Then it defines the fitness of the individual by combining Pareto strength and crowding density. Test results on several benchmark functions show that the approach is a simple, robust and effective method.