计算机工程
計算機工程
계산궤공정
COMPUTER ENGINEERING
2014年
11期
200-204
,共5页
粒子群优化%动态环境%优化问题%双子群协同%对称位移映射%差异进化
粒子群優化%動態環境%優化問題%雙子群協同%對稱位移映射%差異進化
입자군우화%동태배경%우화문제%쌍자군협동%대칭위이영사%차이진화
Particle Swarm Optimization ( PSO )%dynamic environment%optimization problem%two subpopulation swarm cooperation%symmetric displacement mapping%differential evolution
粒子群优化算法在求解动态优化问题时存在多样性缺失和寻优效率低的问题，为此，提出一种运用对称位移映射的双子群算法。该算法通过2组相互协同的主、辅子群并行地搜索变化的最优值。辅子群采取差异进化机制不断探索新环境，在感知环境变化时引入一种对称位移映射策略，使粒子对称分布在最优解的周围，以提高算法收敛到最优解的概率。使用MPB和DF1两种经典的Benchmark测试函数生成复杂的动态环境，对该算法进行实验仿真，结果表明，该算法能提高跟踪动态变化极值的准确性。
粒子群優化算法在求解動態優化問題時存在多樣性缺失和尋優效率低的問題，為此，提齣一種運用對稱位移映射的雙子群算法。該算法通過2組相互協同的主、輔子群併行地搜索變化的最優值。輔子群採取差異進化機製不斷探索新環境，在感知環境變化時引入一種對稱位移映射策略，使粒子對稱分佈在最優解的週圍，以提高算法收斂到最優解的概率。使用MPB和DF1兩種經典的Benchmark測試函數生成複雜的動態環境，對該算法進行實驗倣真，結果錶明，該算法能提高跟蹤動態變化極值的準確性。
입자군우화산법재구해동태우화문제시존재다양성결실화심우효솔저적문제，위차，제출일충운용대칭위이영사적쌍자군산법。해산법통과2조상호협동적주、보자군병행지수색변화적최우치。보자군채취차이진화궤제불단탐색신배경，재감지배경변화시인입일충대칭위이영사책략，사입자대칭분포재최우해적주위，이제고산법수렴도최우해적개솔。사용MPB화DF1량충경전적Benchmark측시함수생성복잡적동태배경，대해산법진행실험방진，결과표명，해산법능제고근종동태변화겁치적준학성。
Particle Swarm Optimization ( PSO ) algorithm is inclined to fall into diversity loss and low optimizing efficiency in dynamic environment. In this paper,a PSO algorithm with symmetric displacement mapping is proposed. The main subpopulation and assistant subpopulation particle swarm work with each other to search the changing global optimum by the parallel searching. The assistant subpopulation particle swarm uses differential evolutionary mechanism to constantly explore the new environment, when the environment is changed, and a symmetrical displacement mapping strategy is introduced to improve the convergence probability to the global optimum through symmetrical particle distribution surrounding the global optimum. The simulative environment in experiments is generated by MPB and DF1 two benchmark functions,the results demonstrate that the algorithm can improve the accuracy of tracking the changing global optimum.
