Improved Simplified Particle Swarm Optimization Based on Piecewise Nonlinear Acceleration Coefficients and Mean Differential Mutation Strategy
Improved Simplified Particle Swarm Optimization Based on Piecewise Nonlinear Acceleration Coefficients and Mean Differential Mutation Strategy
复制标题
基于分段非线性加速系数和均值差分变异策略的改进简化粒子群优化
DOI：
10.1109/access.2020.2994984
复制
发表时间：
2020
期刊：
影响因子：
3.9
通讯作者：
Danfeng Chen
中科院分区：
文献类型：
--
作者：
Meijin Lin;Zhenyu Wang;Fei Wang;Danfeng Chen
粒子群优化算法（PSO）以其易于实现、效率高等优点被广泛应用于各个优化领域。但在求解高维优化问题时，该算法存在收敛速度慢、早熟等缺陷。本文试图解决这些悬而未决的问题。首先，在简化粒子群优化算法（SPSO）中引入一种新的参数调整方法--分段非线性加速系数法，提出了一种改进的基于分段非线性加速系数的SPSO（P-SPSO）算法。针对P-SPSO算法的更新机制，提出了一种均值差分变异策略，并提出了一种改进算法--嵌入均值差分变异策略的P-SPSO算法（MP-SPSO）。为了验证所提出的算法的性能，本文进行了四组不同的实验。结果表明：1）所提出的P-SPSO算法比其他四种经典的改进SPSO算法在不同加速系数下都能得到更好的解; 2）所提出的MP-SPSO算法比P-SPSO算法和基于平均差分变异策略的SPSO算法（M-SPSO）具有更好的优化性能; 3）所提出的MP-SPSO算法明显优于其他八种著名的PSO算法，4）与其他九种智能优化算法相比，MP-SPSO算法在解的质量和鲁棒性方面具有更好的性能。此外，所提出的MP-SPSO算法被成功地应用于一个真实的约束工程问题，并提供了更好的解决方案比其他方法。
Particle swarm optimization (PSO) has been widely used in various optimization fields because of its easy implementation and high efficiency. However, it suffers from some limitations like slow convergence and premature convergence when solving high-dimensional optimization problems. This paper attempts to address these open issues. Firstly, a new method of parameter adjustment named piecewise nonlinear acceleration coefficients is introduced to the simplified particle swarm optimization algorithm (SPSO), and an improved algorithm called piecewise-nonlinear-acceleration-coefficients-based SPSO (P-SPSO) is proposed. Then, a mean differential mutation strategy is developed for the update mechanism of P-SPSO, and another improved algorithm named mean-differential-mutation-strategy embedded P-SPSO (MP-SPSO) is proposed. To validate the performance of the proposed algorithms, four different sets of experiments are carried out in this paper. The results show that, 1) the proposed P-SPSO can get better solutions than other four classic improved SPSO with different acceleration coefficients, 2) the proposed MP-SPSO algorithm shows better optimization performance than P-SPSO and mean-differential-mutation-strategy-based SPSO (M-SPSO), 3) the proposed MP-SPSO is clearly seen to be more successful than other eight well-known PSO variants, 4) compared to other nine intelligent optimization algorithms, MP-SPSO achieves better performance in terms of solution quality and robustness. Moreover, the proposed MP-SPSO algorithm is successfully applied to a real constrained engineering problem and provides better solutions than other methods.
登录
查看更多内容
DOI：
10.1145/2598394.2605342
发表时间：
2014-07
期刊：
Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation
影响因子：
--
作者：
A. Engelbrecht
通讯作者：
A. Engelbrecht
远程维护机械臂数值运动学求解的混合差分进化与粒子群优化算法
影响因子：
1.7
作者：
Mao Bingyan;Xie Zhijiang;Wang Yongbo;H;roos Heikki;Wu Huapeng;Shi Shanshuang;Mao BY
通讯作者：
Mao BY
DOI：
10.1016/b978-0-12-409547-2.14581-0
发表时间：
2020
期刊：
Comprehensive Chemometrics
影响因子：
--
作者：
Federico Marini;Beata Walczak
通讯作者：
Federico Marini;Beata Walczak
DOI：
10.1201/9781003206477-5
发表时间：
2021-08
期刊：
Evolutionary Optimization Algorithms
影响因子：
--
作者：
A. Badar
通讯作者：
A. Badar
影响因子：
9.4
作者：
Chien-Hsing Lee;T. Chang;S. Hsu;Joe-Air Jiang
通讯作者：
Chien-Hsing Lee;T. Chang;S. Hsu;Joe-Air Jiang