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在多目标优化问题求解上,粒子群优化算法存在所得最优解集精度不足、分布不够均匀的缺点,针对上述问题,提出了一种多种群分阶段的多目标粒子群优化算法.算法对外部档案个体采取多种算子进行处理以提高解集的收敛精度,引入简化粒子群优化模型使算法更适应多目标优化问题的求解,通过分阶段选取领导个体以及分阶段采取不同策略对非支配解集进行维护以维持解分布均匀性的同时提高收敛速度,重点改善高维多目标优化问题的解集分布均匀性.实验结果表明,改进算法所得的非支配解集具有更好的分布均匀性和收敛精度.
In solving multi-objective optimization problems, Particle Swarm Optimization (PSO) has the shortcomings that the optimal solution set is not enough and its distribution is not uniform enough. A multi-objective and multi-objective particle swarm optimization algorithm is proposed to solve the above problems. The file individual adopts a variety of operators to improve the convergence precision of the solution set. The simplified particle swarm optimization model is introduced to make the algorithm more suitable for solving multi-objective optimization problems. By selecting leaders in stages and adopting different strategies in stages, Set to maintain to maintain the uniformity of solution distribution and improve the convergence speed, and focus on improving the solution set distribution uniformity of high-dimensional multi-objective optimization problems.The experimental results show that the improved algorithm has better distribution uniformity and Convergence accuracy.