Improving the JAYA Algorithm for High-Cost Computing Applications

Abstract

This article presents a novel optimization algorithm, JAYA-HCO (High-Cost Optimization), which combines the strengths of JAYA and SCA algorithms. By incorporating the update mechanism of SCA for global search and JAYA’s rapid search capability, JAYA-HCO improves the utilization of the population in JAYA algorithm and makes it more suitable for high-cost computing. Furthermore, JAYA-HCO uses sine functions to introduce mutations at appropriate times to expand the search space. Initially, a set of benchmark functions, including single and multi-modal functions, were used to compare JAYA-HCO with other algorithms in terms of mean value and computation speed. Then, a numerical example of optimizing the fiber orientation angle of laminated composite plates under unidirectional axial compression was conducted to demonstrate the effectiveness and reliability of the proposed algorithm. The objective was to maximize the critical buckling load of the laminated composite plate by adjusting the fiber orientation angle of each layer. The results show that JAYA-HCO algorithm can provide accurate and efficient optimization results, which are validated by comparison with other algorithms and finite element calculations.