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提出一种三态协调搜索多目标粒子群优化算法.该算法提出的三态指导粒子选择策略可以很好地协调算法的局部和全局搜索能力,且算法改进了传统的外部档案保存机制,同时引入3种突变因子,使获得的非劣解具有更好的分散性.通过对标准测试函数的求解,并与其他经典多目标优化算法比较,表明了新算法在收敛性和多样性方面均有较大的优越性.最后分析了区域划分系数对所提出算法性能的影响.
A three-state coordinated search multi-objective particle swarm optimization algorithm is proposed.The tri-state directed particle selection strategy proposed by the algorithm can well coordinate the local and global search capabilities of the algorithm and the algorithm improves the traditional external file saving mechanism while introducing Three kinds of mutation factors make the obtained non-inferior solutions have better dispersibility.By solving the standard test function and comparing with other classical multi-objective optimization algorithms, it shows that the new algorithm has more convergence and diversity The superiority of the algorithm.Finally, the influence of the regional division coefficient on the performance of the proposed algorithm is analyzed.