Multi-Strategy Particle Swarm Optimization Algorithm Based on Evolution Ability

doi:10.3778/j.issn.1002-8331.2111-0423

Abstract

Abstract: Aiming at the shortcomings of particle swarm optimization algorithm, such as easy premature convergence and low solution accuracy, a multi-strategy particle swarm optimization algorithm based on evolution ability is proposed. According to the change direction of fitness value, particles are divided into progressive particles and retrogressive particles. The progressive particles are updated according to the original evolution strategy, retaining the advantages of the original algorithm. For the retrogressive particles, it is further divided into temporarily retrogressive particles and long-term retrogressive particles according to the particle activity. For temporarily retrogressive particles, the dependence on individual historical speed is reduced or even learning in the opposite direction. For the long-term retrogressive particles, different evolution strategies are adopted according to the fitness value of the particles to improve the global optimization ability. At the same time, it designs a kind of inertia weight with random fluctuations, so that the particles still have the ability to jump out of the current area in the later stage of the algorithm, which is conducive to the global search. Comparing the optimization results with other algorithms in 10 test functions in different dimensions shows that this algorithm has advantages in both the convergence speed and accuracy of solving low-dimensional and high-dimensional problems. The EAMSPSO algorithm is applied to solve semi-infinite programming problems. Experimental results show that this algorithm is suitable for solving semi-infinite programming problems and has advantages.

Key words: particle swarm optimization, fitness value change direction, particle activity, inertial weight, semi-infinite programming

摘要： 针对粒子群优化算法易早熟收敛、求解精度低等缺点，提出基于进化能力的多策略粒子群优化算法（multi-strategy particle swarm optimization algorithm based on evolution ability）。将粒子按照适应值变化方向分为进步粒子和停退粒子。对于进步粒子按照原始进化策略更新，保留原算法的优点。对于停退粒子进一步根据粒子活性分为暂时停退粒子和长久停退粒子，针对暂时停退的粒子，减小对个体历史速度的依赖甚至向相反方向学习，针对长久停退粒子，根据粒子的适应值优劣采用不同的进化策略，提高全局寻优能力。同时，设计一种带随机波动的惯性权重，使粒子在算法后期仍然具有跳出当前区域的能力，利于全局搜索。通过与其他算法在10个测试函数不同维度上的优化结果对比表明，该算法无论对低维还是高维问题求解的收敛速度和求解精度均有优势。将EAMSPSO算法应用于半无限规划问题的求解，实验结果表明，该算法可以用于半无限规划问题的求解，且具有优势。

关键词: 粒子群优化, 适应值变化方向, 粒子活性, 惯性权重, 半无限规划

WANG Xiaoyan, CAO Dexin. Multi-Strategy Particle Swarm Optimization Algorithm Based on Evolution Ability[J]. Computer Engineering and Applications, 2023, 59(5): 78-86.

王晓艳, 曹德欣. 基于进化能力的多策略粒子群优化算法[J]. 计算机工程与应用, 2023, 59(5): 78-86.

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