Multiobjective Optimization Algorithm Based on (μ+1) Evolutionary Strategy
Jixiang Zhou · Jisuanji gongcheng · 2003
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.