CAJ | 학술논문

受多种群并行寻优机制的启发,提出了一种基于熵模型的动态粒子群优化算法( entropy dynamic multi-PSO,EDM-PSO)用于处理动态优化问题。将解空间划分为多个子空间,在每个子空间中利用熵模型增加种群多样性,多种群并行搜索,利用多点环境检测机制检测环境变化。对动态多峰benchmark优化问题进行了数值实验,并与其他几种动态优化算法进行了比较,结果表明：EDM-PSO算法对于处理动态优化问题具有优势。
수다충군병행심우궤제적계발,제출료일충기우적모형적동태입자군우화산법( entropy dynamic multi-PSO,EDM-PSO)용우처리동태우화문제。장해공간화분위다개자공간,재매개자공간중이용적모형증가충군다양성,다충군병행수색,이용다점배경검측궤제검측배경변화。대동태다봉benchmark우화문제진행료수치실험,병여기타궤충동태우화산법진행료비교,결과표명：EDM-PSO산법대우처리동태우화문제구유우세。
Inspired by the multi-population parallel optimization mechanism, this paper proposes an Entropy-based Dynamic Multi-population Particle Swarm Optimization ( EDM-PSO) algorithm which can be utilized to deal with dynamic optimization problems. The solution space was divided into multiple sub-spaces, in which the entropy models were utilized in each sub-space to increase the diversity of populations. Additionally, the multi-population parallel searching mechanism and multi-point detection mechanism were also implemented to seek the optimal solution and to detect ambient environmental changes respectively. Finally, a comparison between EDM-PSO and several other dynamical optimization algorithms in terms of the errors ( standard deviation ) when addressing a moving peaks function benchmark problem was made, resulting in that the EDM-PSO algorithm can be more beneficial to solving dynamic problems.