基于金字塔结构的群智能演化策略求解混合整数规划问题

混合整数非线性规划问题（mixed-integer nonlinear programming，MINLP） 广泛应用于科学及工程系统设计，传统的群智能算法在求解混合整数规划问题时，未能很好地解决种群内部个体或者种群之间开采与探索、竞争与协作的矛盾。为了解决这两个矛盾及更高效率地寻优，提出一种基于金字塔结构的群智能演化策略（swarm intelligent evolution strategy based on pyramid structure）的PES算法来求解混合整数规划问题。PES算法中明确的分工机制能够平衡全局与局部搜索的能力，晋升机制解决了种群间竞争与协作的矛盾。利用标准测试函数进行仿真，对比改进的粒子群算法（CLSPSO、CLSPSO2）及改进的差分进化算法（ridDE、ridDE2）的结果，发现PES算法在成功率与精度方面具有优势，也体现了PES算法的有效性。

Swarm intelligent evolution strategy based on pyramid structure for solving mixed integer programming problems

MINLP is widely used in scientific and engineering system designs. The traditional swarm intelligent algorithm fails to solve the contradiction between mining and exploration, competition and cooperation among individuals or populations in the population when solving mixed integer programming problems. In order to solve these two contradictions and optimize more efficiently, this paper proposed a PES algorithm due to swarm intelligent evolution strategy based on pyramid structure to solve the mixed integer programming problems. PES algorithm had a clear division of labor mechanism and promotion mechanism. A clear division of labor could balance global and local search capabilities. The promotion mechanism resolved the contradictions between competition and collaboration among populations. It used standard test functions to simulate. The experimental results show that success rate and accuracy of the PES algorithm are better and verify effectiveness of the PES algorithm by comparing the results of the improved particle swarm optimization algorithm(CLSPSO, CLSPSO2) and the improved differential evolution algorithm(ridDE, ridDE2).