Abstract: In order to further improve the efficiency of the population diversity in the implementation process of the Particle Swarm Optimization (PSO), an Adaptive PSO (APSO) algorithm based on diversity feedback was proposed. APSO adopted a new population diversity evaluation strategy which enabled the automatic control of the inertia weight with population diversity in the search process to balance exploration and the exploitation's process. In addition, an elite learning strategy was used in the globally best particle to jump out of local optimal solution. It not only ensured the convergence rate of the algorithm, but also adaptively adjusted the search direction to improve the accuracy of solutions. The simulation results on a set of typical test functions verify the validity of APSO.

Key words: Particle Swarm Optimization (PSO), diversity, entropy, mutation

摘要： 为了进一步提高种群多样性在粒子群优化执行中的效率，提出一种基于多样性反馈的自适应粒子群优化算法(APSO)。APSO采用一种新的种群多样性评价策略，使惯性权值在搜索过程中随多样性自适应性地调整，从而均衡算法的勘探和开发过程。此外，最优粒子采用精英学习策略跳出局部最优区域，从而在保证算法收敛速度的同时能够自适应地调整搜索方向，提高解的精确度。通过一组典型测试函数的仿真结果，验证了APSO的有效性。

关键词: 粒子群优化, 多样性, 熵, 变异