为解决灰狼优化(grey wolf optimizer,GWO)算法收敛速度慢、易陷入局部最优等问题,提出一种基于混合变异的灰狼优化(hybrid mutation grey wolf optimizer,HMGWO)算法。采用Tent混沌映射策略初始化种群,融入自适应收敛因子策略平衡搜索...为解决灰狼优化(grey wolf optimizer,GWO)算法收敛速度慢、易陷入局部最优等问题,提出一种基于混合变异的灰狼优化(hybrid mutation grey wolf optimizer,HMGWO)算法。采用Tent混沌映射策略初始化种群,融入自适应收敛因子策略平衡搜索多样性,引入高斯-柯西混合变异策略提高算法性能。利用6个基准测试函数进行仿真实验,从寻优能力与收敛性等方面对HMGWO算法进行综合分析。将HMGWO算法应用于离散泊位-岸桥调度问题,1000次迭代实验后,HMGWO算法的船舶在港时间最短。展开更多
To increase the variety and security of communication, we present the definitions of modified projective synchronization with complex scaling factors （CMPS） of real chaotic systems and complex chaotic systems, where...To increase the variety and security of communication, we present the definitions of modified projective synchronization with complex scaling factors （CMPS） of real chaotic systems and complex chaotic systems, where complex scaling factors establish a link between real chaos and complex chaos. Considering all situations of unknown parameters and pseudo-gradient condition, we design adaptive CMPS schemes based on the speed-gradient method for the real drive chaotic system and complex response chaotic system and for the complex drive chaotic system and the real response chaotic system, respectively. The convergence factors and dynamical control strength are added to regulate the convergence speed and increase robustness. Numerical simulations verify the feasibility and effectiveness of the presented schemes.展开更多
文摘为解决灰狼优化(grey wolf optimizer,GWO)算法收敛速度慢、易陷入局部最优等问题,提出一种基于混合变异的灰狼优化(hybrid mutation grey wolf optimizer,HMGWO)算法。采用Tent混沌映射策略初始化种群,融入自适应收敛因子策略平衡搜索多样性,引入高斯-柯西混合变异策略提高算法性能。利用6个基准测试函数进行仿真实验,从寻优能力与收敛性等方面对HMGWO算法进行综合分析。将HMGWO算法应用于离散泊位-岸桥调度问题,1000次迭代实验后,HMGWO算法的船舶在港时间最短。
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61273088,10971120,and 61001099)the Natural Science Foundation of Shandong Province,China(Grant No.ZR2010FM010)
文摘To increase the variety and security of communication, we present the definitions of modified projective synchronization with complex scaling factors （CMPS） of real chaotic systems and complex chaotic systems, where complex scaling factors establish a link between real chaos and complex chaos. Considering all situations of unknown parameters and pseudo-gradient condition, we design adaptive CMPS schemes based on the speed-gradient method for the real drive chaotic system and complex response chaotic system and for the complex drive chaotic system and the real response chaotic system, respectively. The convergence factors and dynamical control strength are added to regulate the convergence speed and increase robustness. Numerical simulations verify the feasibility and effectiveness of the presented schemes.
