论文部分内容阅读
针对微粒群优化算法的早熟停滞缺陷问题,提出了一种基于种群年龄模型的动态粒子数微粒群优化算法.该算法建立了生物种群年龄模型,将每个粒子划分为不同的年龄段.动态地依据种群环境和个体信息有效地控制种群的粒子数规模;设计了较优粒子的生殖策略和较差粒子的死亡策略,增加群体的多样性和减少冗余计算量.以保证算法获得最优性能.将此算法与其他改进算法进行比较,仿真测试结果表明,新算法具有较高的全局搜索成功率和效率.计算量显著降低.优化精度显著提高,能够有效地避免算法陷入局部停滞的缺点.
Aiming at the problem of precocious stagnation in Particle Swarm Optimization (PSO) algorithm, a dynamic Particle Swarm Optimization (PSO) algorithm based on the population age model is proposed. The algorithm builds a biological population age model and divides each particle into different age groups. According to the population environment and individual information, population size of population is effectively controlled. Reproductive strategy of optimal particle and death strategy of poorer particle are designed to increase the diversity of population and reduce the amount of redundant computation, so as to ensure the optimal performance of the algorithm Compared with other improved algorithms, the simulation results show that the new algorithm has high global success rate and efficiency, significantly reduces the computational cost, significantly improves the optimization accuracy, and can effectively avoid the shortcomings of the algorithm into local stagnation.