基于Sigmoid惯性权值的自适应粒子群优化算法

针对粒子群优化算法存在的缺点，提出了基于Sigmoid惯性权值的自适应粒子群优化算法。一方面，引入粒子群早熟收敛的计算公式，以指导算法在进化过程中的具体执行策略，有效避免计算的盲目性，加快算法的收敛速度；另一方面，通过设定粒子群聚集程度的判定阈值，以使算法在线性递减惯性权值和基于Sigmoid函数思想的非线性递减惯性权值之间进行自适应地动态调整，从而有效减少了算法陷入局部最优的可能。测试函数仿真结果表明了该算法的可行性和有效性。

Adaptive particle swarm optimization algorithm based on Sigmoid inertia weight

Due to the disadvantages of the canonical Particle Swarm Optimization (PSO), a new adaptive particle swarm optimization algorithm based on Sigmoid inertia weight was proposed. On the one hand, the premature computing formula of the particles was introduced so as to conduct the evolution process and enhance the convergent speed. On the other hand, the decision threshold of particles' focusing degree was employed in order to make the PSO adaptively adopt the inertia weight between linear decrease and nonlinear decrease based on Sigmoid function, which could effectively prevent the PSO from plunging into local minimum. The experimental results show that the proposed algorithm is feasible and effective.