Gravity particle swarm optimization algorithm for solving shop visit balancing problem for repairable equipment. (January 2023)

• Record Type: Journal Article
• Authors: Xia, Xiangzhao; Fu, Xuyun; Zhong, Shisheng; Bai, Zhengfeng; Wang, Yanchao
• Is Part Of: Engineering applications of artificial intelligence. Volume 117:Part A(2023)
• Publication Date: 2023-01
• Subjects: Gravity particle swarm optimization -- Shop visit balancing problem -- Velocity updating strategy -- Combinatorial optimization -- Benchmark task; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.105543
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: The particle swarm optimization (PSO) algorithm has received much attention from engineering and scientific fields since it was proposed. Nevertheless, when solving complex combinatorial optimization tasks such as the proposed shop visit balancing problem for repairable equipment (SVBPRE), the canonical PSO is still prone to fall into the local optimal stagnation. Therefore, a novel intelligent optimization algorithm, namely gravity particle swarm optimization (GPSO) algorithm, is proposed to remedy the above defects. This algorithm improves the velocity updating strategy of particles in the population, which can effectively improve the global search ability of the algorithm without increasing the time complexity, so that it can jump out of the local optimal position and find the feasible solution in the complex solution space. To verify its performance, many experimental verifications were carried out. Firstly, the effectiveness of the proposed GPSO was verified by comparing with the PSO on the 8 benchmark functions. Secondly, the superiority and advancement of GPSO were proved by comparing with 10 state-of-the-art original optimizers and variant algorithms on 23 benchmark tasks. Finally, based on the constructed shop visit balancing problem model, a series of simulation data cases were generated according to the operation and maintenance data in engineering practice for verification. The results obtained by comparing with 4 commonly used algorithms in engineering demonstrate that the proposed GPSO is superior to other competitors in terms of quality of solutions and has important theoretical significance and application value for solving practical tasks with complex search space. Highlights: Proposed gravity particle swarm optimization (GPSO) algorithm. Constructed the shop visit balancing problem for repairable equipment (SVBPRE). GPSO algorithm has brilliant exploitation and exploration capability. Velocity updating strategy was improved to improve global search ability of GPSO.