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<p>Heat Pipe-Constrained Component Layout Optimization (HCLO) aims to enhance the thermal performance of electronic equipment by optimizing the component layout, predominantly relying on heat pipes for heat dissipation. Current studies face challenges in generating high-quality solutions and are hindered by the curse of dimensionality, making it infeasible to apply to complex HCLO applications. To tackle these challenges, a novel Parallel Bi-population and Clan-based Hybrid Algorithm (PBCHA) is proposed. PBCHA introduces (i) a new algorithm based on Genetic Algorithm (GA) and Fireworks Algorithm (FWA) to improve the search effectiveness; (ii) two kinds of parallel strategies considering the inherent parallelization properties of bi-population and clan-based framework to increase the search efficiency. The proposed algorithm was verified on the IEEE Congress on Evolutionary Computation 2022 (CEC2022) HCLO benchmark problems and their variants with more complexities. The experimental results indicated that PBCHA performs better than existing algorithms in all criteria, including objective value, calculation time and feasibility rate.</p>

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A parallel Genetic-Fireworks algorithm for heat pipe-constrained component layout optimization

  • 期刊论文

  • 发表日期：

    2026年1月21日

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  • Peitian Chen,
  • Helan Liang,
  • Bingji Yan,
  • Hongwei Guo

摘要

Heat Pipe-Constrained Component Layout Optimization (HCLO) aims to enhance the thermal performance of electronic equipment by optimizing the component layout, predominantly relying on heat pipes for heat dissipation. Current studies face challenges in generating high-quality solutions and are hindered by the curse of dimensionality, making it infeasible to apply to complex HCLO applications. To tackle these challenges, a novel Parallel Bi-population and Clan-based Hybrid Algorithm (PBCHA) is proposed. PBCHA introduces (i) a new algorithm based on Genetic Algorithm (GA) and Fireworks Algorithm (FWA) to improve the search effectiveness; (ii) two kinds of parallel strategies considering the inherent parallelization properties of bi-population and clan-based framework to increase the search efficiency. The proposed algorithm was verified on the IEEE Congress on Evolutionary Computation 2022 (CEC2022) HCLO benchmark problems and their variants with more complexities. The experimental results indicated that PBCHA performs better than existing algorithms in all criteria, including objective value, calculation time and feasibility rate.