CAJ | 학술논문

为解决传统粒子群优化算法易出现早熟的不足，提出了精英反向学习策略，引入精英粒子，采用反向学习生成其反向解，扩大搜索区域的范围，可增强算法的全局勘探能力。同时，为避免最优粒子陷入局部最优而导致整个群体出现搜索停滞，提出了差分演化变异策略，采用差分演化算法搜索最优粒子的邻域空间，可增强算法的局部开采能力。在14个测试函数上将本文算法与多种知名的PSO算法进行对比，实验结果表明本文算法在解的精度与收敛速度上更优。
위해결전통입자군우화산법역출현조숙적불족，제출료정영반향학습책략，인입정영입자，채용반향학습생성기반향해，확대수색구역적범위，가증강산법적전국감탐능력。동시，위피면최우입자함입국부최우이도치정개군체출현수색정체，제출료차분연화변이책략，채용차분연화산법수색최우입자적린역공간，가증강산법적국부개채능력。재14개측시함수상장본문산법여다충지명적PSO산법진행대비，실험결과표명본문산법재해적정도여수렴속도상경우。
Traditional particle swarm optimization (PSO)algorithm tends to suffer from premature convergence ;we proposed an elite opposition-based learning strategy in which elite particles are introduced to generate their opposite solutions by opposition-based learning .This mechanism can expand the search area and is helpful to enhance the global explorative ability of PSO .Mean-while ,a differential evolutionary mutation strategy is presented to avoid the best particle being trapped into local optima ,since this may cause search stagnation of the whole swarm .This strategy adopts differential evolution algorithm to search for the neighbor-hoods of the global best particle and is helpful to enhance the exploitation ability of PSO .We compared our algorithm with some state-of-the-art PSOs on 14 benchmarks ,the results show that our algorithm obtains better solution accuracy and quicker convergence speed .