计算机工程与设计
計算機工程與設計
계산궤공정여설계
COMPUTER ENGINEERING AND DESIGN
2014年
7期
2552-2556
,共5页
粒子群优化%惯性权重%自适应%分布模型%局部搜索
粒子群優化%慣性權重%自適應%分佈模型%跼部搜索
입자군우화%관성권중%자괄응%분포모형%국부수색
particle swarm optimizer%inertia weight%self-adaptive%distribution model%local search
惯性权重对粒子群优化算法的性能有重要影响，但目前的改进方法大多将惯性权重按照设定规律变化，未能与新产生个体的质量建立有效联系。为此，提出了一种基于学习机制和局部搜索机制的混合策略来调整惯性权重，以进一步提高算法的性能。基于学习机制的策略通过统计一定代数内新个体的优劣情况，建立了用以生成惯性权重的分布模型；基于局部搜索机制的策略以惯性权重为变量，利用低维下的局部搜索来调整其取值。在5个标准函数和1个输电网扩展规划问题函数上的测试结果表明，该算法在其中5个函数上取得了优于对比算法的测试结果。
慣性權重對粒子群優化算法的性能有重要影響，但目前的改進方法大多將慣性權重按照設定規律變化，未能與新產生箇體的質量建立有效聯繫。為此，提齣瞭一種基于學習機製和跼部搜索機製的混閤策略來調整慣性權重，以進一步提高算法的性能。基于學習機製的策略通過統計一定代數內新箇體的優劣情況，建立瞭用以生成慣性權重的分佈模型；基于跼部搜索機製的策略以慣性權重為變量，利用低維下的跼部搜索來調整其取值。在5箇標準函數和1箇輸電網擴展規劃問題函數上的測試結果錶明，該算法在其中5箇函數上取得瞭優于對比算法的測試結果。
관성권중대입자군우화산법적성능유중요영향，단목전적개진방법대다장관성권중안조설정규률변화，미능여신산생개체적질량건립유효련계。위차，제출료일충기우학습궤제화국부수색궤제적혼합책략래조정관성권중，이진일보제고산법적성능。기우학습궤제적책략통과통계일정대수내신개체적우렬정황，건립료용이생성관성권중적분포모형；기우국부수색궤제적책략이관성권중위변량，이용저유하적국부수색래조정기취치。재5개표준함수화1개수전망확전규화문제함수상적측시결과표명，해산법재기중5개함수상취득료우우대비산법적측시결과。
Inertia weight was a very important parameter for the performance of particle swarm optimizer.Many algorithms ad-justed inertia weight in a given way.However,these methods had not well established the relationship to the quality of new solu-tions.A hybrid strategy based on learning process and local search used to adjust the inertia weight was propsed to improve the algorithm’s performance.The learning process counted the quality of new solutions during a predefined generation interval to ob-tain the distribution model of inertia weight,and then the model was used to generate new values of inertia weight.The local search process set the inertia weight as the variable and tuned it by executing the searching in the low dimensional space.The ex-perimental results on five standard functions and one transmission network expansion planning problem function showed that the proposed algorithm on five of six functions obtained better results than others algorithms.
