融合吸引排斥和双向学习的改进粒子群算法

针对粒子群算法在计算时存在收敛速度慢、易陷入局部收敛等缺陷，提出了一种融合吸引排斥和双向学习的改进粒子群算法来提高算法的寻优能力。双向学习策略扩大了粒子的搜索范围、丰富了种群多样性；在吸引-排斥策略中，粒子能够分别被全局最优粒子和全局最差粒子所引导进而朝着更优的方向进化，提高了算法的局部寻优性能和收敛能力。同时，在双向学习策略中，为了克服单一性的学习因子和惯性权重在优化复杂函数时无法很好地调节寻优进程的问题，提出了双重自适应策略，更好地平衡群体中粒子的搜索行为。最后利用标准测试函数对该算法进行仿真验证，并与其他两种改进的算法对比。实验结果表明，在相同的实验条件下，改进后的粒子群算法在寻优能力和收敛速度方面具有明显优势。

Improved Particle Swarm Optimization Algorithm Based on Attraction-Repulsion and Bidirectional Learning Strategies

An improved particle swarm optimization（PSO） algorithm which combines attraction-repulsion strategy and bidirectional learning strategy is proposed to overcome the shortcomings of traditional PSO, such as slow convergence speed and easy to fall into local extreme. The bidirectional learning strategy overcomes the limitations of the traditional one-way learning method by expanding the searching range of the particles, so as to enrich the diversity of the population. In the attraction-repulsion strategy, the particle can be guided by the global best and the global worst particles to evolve towards the better direction, which improves the local optimization performance and convergence ability of the algorithm. Furthermore, in order to overcome the problem that the single learning factor and inertial weight cannot adjust the optimization process well when optimizing complex functions, a dual adaptive strategy is proposed to better balance the search behavior of the particles in the group. Finally, the proposed algorithm is simulated and verified by using the standard test function, and compared with the other two improved algorithms. The experimental results show that the improved algorithm has advantages in the optimization effect and convergence speed under the same experimental conditions.