融合禁忌搜索和惯性权重动态调整的二进制粒子群优化算法

Binary Particle Swarm Optimization Algorithm Combining Tabu Search and Dynamic Adjustment of Inertial Weights

传统的二进制粒子群算法在优化过程中容易出现收敛速度慢和容易陷入局部最优的问题,因此提出了一种融合禁忌搜索和惯性权重动态调整的二进制粒子群优化算法。该算法不仅综合了粒子群优化算法的快速性、随机性、全局收敛性的优点,而且能够进行禁忌搜索以实现局部寻优。通过在4个基准测试函数上进行实验,实验结果表明该算法在逃离局部最优解方面具有更好的能力,提高了算法的收敛性能。

The traditional binary particle swarm optimization algorithm is prone to slow convergence speed and easy to fall into local optimum in the optimization process.To solve the above problems,this paper proposes a binary particle swarm optimization algorithm that combines tabu search and inertial weight dynamic adjustment.This algorithm not only combines the advantages of fastness,randomness and global convergence of the particle swarm optimization algorithm,but also performs tabu search to achieve local optimization.Through experiments on four benchmark functions,the experimental results show that the algorithm has a better ability to escape from local optimal solutions,and also improves the convergence performance of the algorithm.