Open Access

ARTICLE

Application of a Parallel Adaptive Cuckoo Search Algorithm in the Rectangle Layout Problem

Weimin Zheng, Mingchao Si, Xiao Sui, Shuchuan Chu, Jengshyang Pan^*

College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, China

* Corresponding Author: Jengshyang Pan. Email:

(This article belongs to this Special Issue: Bio-inspired Computer Modelling: Theories and Applications in Engineering and Sciences)

Computer Modeling in Engineering & Sciences 2023, 135(3), 2173-2196. https://doi.org/10.32604/cmes.2023.019890

Received 22 October 2021; Accepted 22 July 2022; Issue published 23 November 2022

Abstract

The meta-heuristic algorithm is a global probabilistic search algorithm for the iterative solution. It has good performance in global optimization fields such as maximization. In this paper, a new adaptive parameter strategy and a parallel communication strategy are proposed to further improve the Cuckoo Search (CS) algorithm. This strategy greatly improves the convergence speed and accuracy of the algorithm and strengthens the algorithm’s ability to jump out of the local optimal. This paper compares the optimization performance of Parallel Adaptive Cuckoo Search (PACS) with CS, Parallel Cuckoo Search (PCS), Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Differential Evolution (DE) and Artificial Bee Colony (ABC) algorithms by using the CEC-2013 test function. The results show that PACS algorithm outperforms other algorithms in 20 of 28 test functions. Due to the superior performance of PACS algorithm, this paper uses it to solve the problem of the rectangular layout. Experimental results show that this scheme has a significant effect, and the material utilization rate is improved from 89.5% to 97.8% after optimization.

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Keywords

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