计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2015年
18期
32-37,85
,共7页
徐迅%鲁海燕%徐向平
徐迅%魯海燕%徐嚮平
서신%로해연%서향평
约束优化%粒子群优化%动态目标方法(DOM)%自适应速度粒子群优化(SAVPSO)%约束处理机制%环形邻域拓扑
約束優化%粒子群優化%動態目標方法(DOM)%自適應速度粒子群優化(SAVPSO)%約束處理機製%環形鄰域拓撲
약속우화%입자군우화%동태목표방법(DOM)%자괄응속도입자군우화(SAVPSO)%약속처리궤제%배형린역탁복
constrained optimization%particle swarm optimization%Dynamic-Objective Method(DOM)%Self-Adaptive Velocity Particle Swarm Optimization(SAVPSO)%constraint-handling mechanism%ring neighborhood topology
为克服全局粒子群优化算法易陷入局部最优的缺点，基于全局自适应速度粒子群优化（SAVPSO）算法，给出一种基于环形邻域拓扑的局部SAVPSO算法来求解约束优化问题，同时采用动态目标方法（DOM）来有效处理约束条件，并以13个经典的测试函数为例对算法的性能进行仿真实验研究。测试结果表明，与全局SAVPSO算法相比，该算法具有较强的全局寻优能力，可以较好地避免陷入局部最优；另外，粒子的邻域大小及实现形式对算法的性能均有一定的影响。
為剋服全跼粒子群優化算法易陷入跼部最優的缺點，基于全跼自適應速度粒子群優化（SAVPSO）算法，給齣一種基于環形鄰域拓撲的跼部SAVPSO算法來求解約束優化問題，同時採用動態目標方法（DOM）來有效處理約束條件，併以13箇經典的測試函數為例對算法的性能進行倣真實驗研究。測試結果錶明，與全跼SAVPSO算法相比，該算法具有較彊的全跼尋優能力，可以較好地避免陷入跼部最優；另外，粒子的鄰域大小及實現形式對算法的性能均有一定的影響。
위극복전국입자군우화산법역함입국부최우적결점，기우전국자괄응속도입자군우화（SAVPSO）산법，급출일충기우배형린역탁복적국부SAVPSO산법래구해약속우화문제，동시채용동태목표방법（DOM）래유효처리약속조건，병이13개경전적측시함수위례대산법적성능진행방진실험연구。측시결과표명，여전국SAVPSO산법상비，해산법구유교강적전국심우능력，가이교호지피면함입국부최우；령외，입자적린역대소급실현형식대산법적성능균유일정적영향。
Based on the global version of Self-Adaptive Velocity Particle Swarm Optimization(SAVPSO)algorithm, this paper proposes a local version of SAVPSO using ring neighborhood topology for solving constrained optimization problems in order to counter the disadvantage of the global SAVPSO of easily falling in local optima, and uses Dynamic-Objective Method(DOM)to effectively deal with the constraints. The performance of the proposed algorithm is evaluated on 13 well-known benchmark functions. Experimental results show that the proposed algorithm has stronger ability to find global optimal solutions and to avoid falling in local optima compared with the global SAVPSO, and that the neighborhood size and realization have impact on the performance of the algorithm.
