Hybrid Chaotic Quantum behaved Particle Swarm Optimization algorithm for thermal design of plate fin heat exchangers

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.