Constrained multi-objective optimization with hybrid differential evolution and alpha constrained domination technique
基于混合差分进化和alpha约束支配处理的多目标优化算法

To solve the constrained multi-objective optimization problems, we present a hybrid differential evolution algorithm with alpha constrained domination technique. In this approach, the constraint level, which measures how well an individual satisfies the constraints, is incorporated with the domination principle to solve multi-objective problems. At the early stage, the constraint level is relaxed in order to utilize the useful information carried by some infeasible individuals, so this relaxation increases the diversity of the population. At the later stage, the constraint level is tightened to make the evolution process searching for the feasible area. At the same time, a new dynamic simplex crossover operator is incorporated into differential evolution to improve the abilities of exploration and exploitation. The proposed algorithm is tested on 6 typical benchmarks and compared with other algorithms. Comparison results indicate that the proposed algorithm has advantages in converging to Pareto front and maintaining the evenly-distributed optima along the Pareto front.