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首页> 外文期刊>Applied Mathematical Modelling >Hybrid Chaotic Quantum behaved Particle Swarm Optimization algorithm for thermal design of plate fin heat exchangers
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Hybrid Chaotic Quantum behaved Particle Swarm Optimization algorithm for thermal design of plate fin heat exchangers

机译：板翅式换热器热设计的混合混沌量子行为粒子群优化算法

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摘要

This study investigates the utilization of Hybrid Chaotic Quantum behaved Particle Swarm Optimization (HCQPSO) algorithm for thermal design of plate fin heat exchangers. HCPQSO algorithm successfully combines a variant of Quantum behaved Particle Swarm Optimization (LQPSO), with efficient local search mechanisms to yield better results in terms of solution accuracy and convergence rate. Hot and cold side length of the heat exchanger, fin height, fin frequency (fins per meter), fin thickness, lance length of the fin and number of fin layers are considered as design variables to minimize the heat transfer area, total pressure drop and total cost of heat exchanger with a specified heat duty under a given search space. Constraint handling is maintained with the Automatic Dynamic Penalization method which is adaptive and does not need of tuning the penalty coefficient for any optimization problem. The robustness of the proposed algorithm is benchmarked with various types of optimization test problems and case studies taken from the literature. Comparison results indicate that hybrid algorithm outperforms many optimization algorithms available in the literature. It is also observed that the proposed algorithm successfully converges to optimum configuration with a higher accuracy.
机译：本文研究了混合混沌量子行为粒子群优化算法（HCQPSO）在板翅式换热器热设计中的应用。 HCPQSO算法成功地结合了量子行为粒子群优化（LQPSO）的变体和有效的局部搜索机制，以在求解精度和收敛速度方面产生更好的结果。热交换器的热侧和冷侧长度，翅片高度，翅片频率（翅片每米），翅片厚度，翅片的喷枪长度和翅片层数被视为设计变量，以最大程度地减少传热面积，总压降和在给定搜索空间下具有指定热负荷的热交换器的总成本。约束处理通过自动动态惩罚方法来保持，该方法是自适应的，无需针对任何优化问题调整惩罚系数。所提算法的鲁棒性以各种类型的优化测试问题和来自文献的案例研究为基准。比较结果表明，混合算法的性能优于文献中提供的许多优化算法。还可以观察到，所提出的算法以较高的精度成功地收敛于最优配置。

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