【摘要】 为了提高多目标微粒群优化算法处理多目标优化问题的性能,降低计算复杂度,改善算法的收敛性,提出了一种基于人类社交行为的多目标动态微粒群优化算法。考虑到粒子寻优过程受到环境中精英粒子与平庸粒子的影响,分别对自身产生推力与阻力作用,并引入局部跳出策略,使算法具有很强的全局搜索能力和较好的鲁棒性能。通过典型的多目标优化函数对算法进行了测试验证,结果表明提出的多目标算法具有较快的收敛速度和较强的跳出局部最优能力,性能优越,可供许多领域优化问题求解借鉴。更多还原

【Abstract】 In order to improve the processing performance of the multi-objective optimization problem,reduce the computational complexity and improve the convergence of the algorithm,a multi-objective particle swarm optimization algorithm based on a human social behavior was proposed.The strategies such as promotion/resistance factor and the local jump strategy are introduced in proposed algorithm,to make the algorithm have strong global search ability and good robust performance.Some typical multi-objective optimization functions were tested to verify the algorithm.The results show that the proposed algorithm has superior performance of fast convergence speed and strong ability to jump out of local optimum,so it can be used for many fields.更多还原