基于混合策略自适应学习的并行粒子群优化算法

针对当前各种粒子群优化算法解决问题时存在的局限性,提出一种基于混合策略自适应学习的粒子群优化算法(HLPSO)。该算法从收敛速度、跳出局部极值、探索、开发几个不同角度融合了4种具有不同优势的变异策略,当面对不同形态的复杂问题时通过自适应学习机制选择出合适的策略来完成全局寻优。通过对7个标准测试函数的仿真实验并与其他算法相比较,所得结果表明了所提出的算法具有较快的收敛速度、较高的精度以及很强的跳出局部极值的能力。

Improved parallel particle swarm optimization algorithm with hybrid strategy and self-adaptive learning

In view of the limitation of the current kinds of particle swarm optimization(PSO) algorithm, a self-adaptive
learning of hybrid strategy algorithm based on parallel particle swarm optimization(HLPSO) is proposed. The algorithm
combines four strategies reasonably in the different point of view: Convergence, jump out, exploration and exploitation,
which chooses an appropriate strategies to solve the different forms of problems through adjusting the probability of the
strategies gradually in the process of optimizing. Moreover, simulation experiment on a suite of 7 benchmark functions
is given, and the comparisons with other algorithms are provided. The results show that the proposed approach has better
convergence rate and great capability of preventing premature convergence.