A bi-population clan-based genetic algorithm for heat pipe-constrained component layout optimization

Component Layout Optimization (CLO) is attracting growing attention as electronic equipment rapidly advances toward integration, , and high function. Heat pipe-constrained Component Layout Optimization (HCLO) seeks to maximize the thermal performance of electronic equipment by optimizing its component layout, where heat dissipation relies heavily on heat pipes. HCLO poses its peculiar challenges with Many-dimensions, Many-constraints and Many-optima. Although many approaches have been proposed for various CLO applications, they do not consider heat pipe-related constraints and therefore underperform on HCLO. In this paper, we propose a Bi-population Clan-based Genetic Algorithm (BCGA) tailored for HCLO. BCGA introduces (i) A bi-population strategy to decompose HCLO into two interrelated sub-problems, which alleviates the challenge of Many-dimensions and Many-constraints; (ii) A clan-based framework inspired by human evolution to handle Many-optima, under which improved GA operators are also designed. The performance of BCGA is evaluated on a suite of HCLO benchmark problems of varying complexity. Compared with other algorithms recently proposed for CLO and HCLO, BCGA can deliver much more satisfying layout solutions within only half of the computation time.