计算机工程
計算機工程
계산궤공정
COMPUTER ENGINEERING
2014年
7期
156-161
,共6页
云遗传算法%粒子群优化算法%双种群混合算法%自调整惯性权值策略%信息交流机制%云变异操作
雲遺傳算法%粒子群優化算法%雙種群混閤算法%自調整慣性權值策略%信息交流機製%雲變異操作
운유전산법%입자군우화산법%쌍충군혼합산법%자조정관성권치책략%신식교류궤제%운변이조작
Cloud Genetic Algorithm(CGA)%Particle Swarm Optimization(PSO) algorithm%dual populations hybrid algorithm%self-adjusting inertia weight strategy%information exchange mechanism%cloud mutation operation
针对粒子群算法(PSO)收敛速度慢、求解精度不高以及易陷入局部最优的缺点，结合云遗传算法(CGA)和粒子群优化算法，提出一种新型的双种群混合算法(CGA-PSO)。将整个种群平均分成2个子群，分别采用云遗传算法和加入自调整惯性权值策略的粒子群优化算法完成进化。通过引入一种新型的信息交流机制：两子群子代间信息交流以及子代与父代间信息交流，共享最优个体，淘汰最劣个体，实现共同进化，适时对粒子群适应度较差的个体进行云变异操作，该操作是基于云模型的随机性和稳定性，利用全局最优位置和最劣位置实现对部分粒子位置的变异过程。对5个经典测试函数进行测试，并与CGA和PSO算法及其优化算法进行比较，结果表明，CGA-PSO算法具有较高的搜索效率、求解精度和较快的收敛速度，鲁棒性也较强。
針對粒子群算法(PSO)收斂速度慢、求解精度不高以及易陷入跼部最優的缺點，結閤雲遺傳算法(CGA)和粒子群優化算法，提齣一種新型的雙種群混閤算法(CGA-PSO)。將整箇種群平均分成2箇子群，分彆採用雲遺傳算法和加入自調整慣性權值策略的粒子群優化算法完成進化。通過引入一種新型的信息交流機製：兩子群子代間信息交流以及子代與父代間信息交流，共享最優箇體，淘汰最劣箇體，實現共同進化，適時對粒子群適應度較差的箇體進行雲變異操作，該操作是基于雲模型的隨機性和穩定性，利用全跼最優位置和最劣位置實現對部分粒子位置的變異過程。對5箇經典測試函數進行測試，併與CGA和PSO算法及其優化算法進行比較，結果錶明，CGA-PSO算法具有較高的搜索效率、求解精度和較快的收斂速度，魯棒性也較彊。
침대입자군산법(PSO)수렴속도만、구해정도불고이급역함입국부최우적결점，결합운유전산법(CGA)화입자군우화산법，제출일충신형적쌍충군혼합산법(CGA-PSO)。장정개충군평균분성2개자군，분별채용운유전산법화가입자조정관성권치책략적입자군우화산법완성진화。통과인입일충신형적신식교류궤제：량자군자대간신식교류이급자대여부대간신식교류，공향최우개체，도태최렬개체，실현공동진화，괄시대입자군괄응도교차적개체진행운변이조작，해조작시기우운모형적수궤성화은정성，이용전국최우위치화최렬위치실현대부분입자위치적변이과정。대5개경전측시함수진행측시，병여CGA화PSO산법급기우화산법진행비교，결과표명，CGA-PSO산법구유교고적수색효솔、구해정도화교쾌적수렴속도，로봉성야교강。
Considering the problem including slow convergence rates, low solving precisions and easy to trap in local optimum of Particle Swarm Optimization(PSO) algorithm, a novel dual population hybrid algorithm named CGA-PSO is presented, which is based on Cloud Genetic Algorithm(CGA) and PSO algorithm. In this algorithm, the whole population is divided into two equal populations. CGA and PSO with self-adjusting inertia weight strategy are used in the process of evolution of two populations. Two populations share the best individual and eliminate the worst individual by exchanging information between the two groups of offspring as well as offspring and parent to complete the evolution, and a timely cloud mutation operation is given on poor fitness of individuals. Cloud mutation operation is based on stable tendency and randomness property of cloud model. The global best position and the global worst position are used to complete mutation on the part of the particle’s position. By testing five classical functions and comparing CGA-PSO with CGA, PSO and their optimization algorithms, the results show that the proposed algorithm has higher search efficiency, accuracy and rapid convergence speed, and stronger robustness.
