计算机工程与应用
計算機工程與應用
계산궤공정여응용
Computer Engineering and Applications
2015年
22期
61-65,140
,共6页
王燕燕%葛洪伟%杨金龙%王娟娟
王燕燕%葛洪偉%楊金龍%王娟娟
왕연연%갈홍위%양금룡%왕연연
协同进化%K-均值%高维优化%粒子群优化%局部最优
協同進化%K-均值%高維優化%粒子群優化%跼部最優
협동진화%K-균치%고유우화%입자군우화%국부최우
cooperative co-evolution%k-means%high-dimensional optimization%particle swarm optimization%local optimal
针对粒子群优化（PSO）算法优化高维问题时，易陷入局部最优，提出一种基于K-均值聚类的协同进化粒子群优化（KMS-CCPSO）算法。该算法通过引入K-均值算法扩大种群的局部搜索范围，采用柯西分布和高斯分布相结合的方法更新粒子的位置。实验结果表明，该算法具有较好的优化性能，其优势在处理高维问题上更为明显。
針對粒子群優化（PSO）算法優化高維問題時，易陷入跼部最優，提齣一種基于K-均值聚類的協同進化粒子群優化（KMS-CCPSO）算法。該算法通過引入K-均值算法擴大種群的跼部搜索範圍，採用柯西分佈和高斯分佈相結閤的方法更新粒子的位置。實驗結果錶明，該算法具有較好的優化性能，其優勢在處理高維問題上更為明顯。
침대입자군우화（PSO）산법우화고유문제시，역함입국부최우，제출일충기우K-균치취류적협동진화입자군우화（KMS-CCPSO）산법。해산법통과인입K-균치산법확대충군적국부수색범위，채용가서분포화고사분포상결합적방법경신입자적위치。실험결과표명，해산법구유교호적우화성능，기우세재처리고유문제상경위명현。
Aimed at particle swarm optimization(PSO)algorithm is easy to fall into local optimal problems for optimizing a high-dimensional population, a new cooperative coevolving particle swarm optimization on K-means cluster(KMS-CCPSO) algorithm is put forward. In the proposed algorithm, the subspace of local search range is designed by K-means algorithm, and the new points’position and velocity in the search space is relied on Cauchy and Gaussian distributions. The experi-mental results suggest that the proposed algorithm has better optimization performance, its advantage on the large-scale population optimization problem is more apparent.
