CAJ | 학술논문

针对综合学习粒子群算法后期收敛速度慢、一旦所有粒子陷入局部最优，则无法跳出等缺陷，提出免疫综合学习粒子群优化（ICLPSO）算法。ICLPSO算法引入人工免疫系统中的克隆选择机制，利用克隆复制、高频变异、克隆选择等操作，增加种群的多样性，提高算法的收敛速度，利用柯西分布较宽的两翼分布特性进行精英粒子学习以进一步增强粒子逃离局部极值及多峰函数优化问题全局寻优能力。针对标准测试函数的仿真结果表明，与其他改进粒子群算法相比，ICLPSO算法收敛速度快，求解精度更高。
침대종합학습입자군산법후기수렴속도만、일단소유입자함입국부최우，칙무법도출등결함，제출면역종합학습입자군우화（ICLPSO）산법。ICLPSO산법인입인공면역계통중적극륭선택궤제，이용극륭복제、고빈변이、극륭선택등조작，증가충군적다양성，제고산법적수렴속도，이용가서분포교관적량익분포특성진행정영입자학습이진일보증강입자도리국부겁치급다봉함수우화문제전국심우능력。침대표준측시함수적방진결과표명，여기타개진입자군산법상비，ICLPSO산법수렴속도쾌，구해정도경고。
Convergence of the comprehensive learning particle swarm optimization(CLPSO)algorithm is relatively slow at the late stage of evolution.Once all particles trapped in local optimum,the algorithm can not jump out of the local optimum.This paper proposed immune comprehensive learning particle swarm optimization(ICLPSO)algorithms.The algorithm introduced clonal se-lection mechanism in artificial immune system.Using of clonal copy,hypermutation and clonal selection,it increased the diversi-ty of the population,improved the convergence rate and enhanced the ability of escape from the local optimum and multi-mode op-timization ability of global optimization.Using the elitist learning strategy,the ability to escape from local optimia is further en-hanced.Experiments on several benchmark functions verify the effective of the proposed algorithm.