An improved Gaussian distribution based quantum-behaved particle swarm optimization algorithm for engineering shape design problems. Issue 5 (4th May 2022)

• Record Type: Journal Article
• Authors: Chen, Qidong; Sun, Jun; Palade, Vasile; Wu, Xiaojun; Shi, Xiaoqian
• Is Part Of: Engineering optimization. Volume 54:Issue 5(2022)
• Page Start: 743
• Page End: 769
• Publication Date: 2022-05-04
• Subjects: Engineering shape design problems -- Gaussian distribution -- quantum-behaved optimization algorithm -- multiple constraints -- weighted mean best position; Engineering design -- Periodicals; Mathematical optimization -- Periodicals; 620.0042
• DOI: 10.1080/0305215X.2021.1900154
• Languages: English
• ISSNs: 0305-215X

Abstract

Abstract: In this article, an improved Gaussian distribution based quantum-behaved particle swarm optimization (IG-QPSO) algorithm is proposed to solve engineering shape design problems with multiple constraints. In this algorithm, the Gaussian distribution is employed to generate the sequence of random numbers in the QPSO algorithm. By decreasing the variance of the Gaussian distribution linearly, the algorithm is able not only to maintain its global search ability during the early search stages, but can also obtain gradually enhanced local search ability in the later search stages. Additionally, a weighted mean best position in the IG-QPSO is employed to achieve a good balance between local search and global search. The proposed algorithm and some other well-known PSO variants are tested on ten standard benchmark functions and six well-studied engineering shape design problems. Experimental results show that the IG-QPSO algorithm can optimize these problems effectively in terms of precision and robustness compared to its competitors.