A new class of hybrid particle swarm optimization （PSO） algorithm is developed for solving the premature convergence caused by some particles in standard PSO fall into stagnation. In this algorithm, the linearly dec...A new class of hybrid particle swarm optimization （PSO） algorithm is developed for solving the premature convergence caused by some particles in standard PSO fall into stagnation. In this algorithm, the linearly decreasing inertia weight technique （LDIW） and the mutative scale chaos optimization algorithm （MSCOA） are combined with standard PSO, which are used to balance the global and local exploration abilities and enhance the local searching abilities, respectively. In order to evaluate the performance of the new method, three benchmark functions are used. The simulation results confirm the proposed algorithm can greatly enhance the searching ability and effectively improve the premature convergence.展开更多
针对粒子群优化算法(Particle Swarm Optimization,简称PSO)容易陷入局部极值、进化后期的收敛速度慢和精度低等缺点,提出了基于中心位的粒子群优化算法(Particle swarm optimization algorithm based on center particle,简称CPPSO)。...针对粒子群优化算法(Particle Swarm Optimization,简称PSO)容易陷入局部极值、进化后期的收敛速度慢和精度低等缺点,提出了基于中心位的粒子群优化算法(Particle swarm optimization algorithm based on center particle,简称CPPSO)。该算法采取双策略更新粒子位置,一种通过随机惯性权重作用的粒子和影响算子作用的个体极值、全局极值来更新粒子位置,另一种在之前更新的粒子位置基础上,通过中心位采用差分算法来更新粒子位置。通过和其他3种优化算法在18个典型基准函数的仿真测试结果表明,该算法具有更好的全局收敛能力,其收敛速度、寻优精度和稳定性都有明显的提升。展开更多
文摘A new class of hybrid particle swarm optimization （PSO） algorithm is developed for solving the premature convergence caused by some particles in standard PSO fall into stagnation. In this algorithm, the linearly decreasing inertia weight technique （LDIW） and the mutative scale chaos optimization algorithm （MSCOA） are combined with standard PSO, which are used to balance the global and local exploration abilities and enhance the local searching abilities, respectively. In order to evaluate the performance of the new method, three benchmark functions are used. The simulation results confirm the proposed algorithm can greatly enhance the searching ability and effectively improve the premature convergence.
文摘针对粒子群优化算法(Particle Swarm Optimization,简称PSO)容易陷入局部极值、进化后期的收敛速度慢和精度低等缺点,提出了基于中心位的粒子群优化算法(Particle swarm optimization algorithm based on center particle,简称CPPSO)。该算法采取双策略更新粒子位置,一种通过随机惯性权重作用的粒子和影响算子作用的个体极值、全局极值来更新粒子位置,另一种在之前更新的粒子位置基础上,通过中心位采用差分算法来更新粒子位置。通过和其他3种优化算法在18个典型基准函数的仿真测试结果表明,该算法具有更好的全局收敛能力,其收敛速度、寻优精度和稳定性都有明显的提升。
