A novel single and multi-objective optimization approach based on Bees Algorithm Hybrid with Particle Swarm Optimization (BAHPSO): Application to thermal-economic design of plate fin heat exchangers

As a novel hybrid optimization approach, the single and multi-objective BAHPSO are investigated for thermal designing of the cross-flow plate fin heat exchanger (PFHE) under given heat duty and pressure drop constraints. Because both of the Particle Swarm Optimization (PSO) and Bess Algorithm (BA) are operating with a random primary population of solutions, the current study combined their searching abilities for the first time, and presented a novel searching procedure named BAHPSO. In the current investigation, Multi-Objective optimization (MO) of BAHPSO is simultaneously employed to acquire the maximum effectiveness and the minimum total annual cost (TAC) of a heat exchanger as two contradict objectives and then results are compared with MOPSO and MOBA. Hot and cold side length, fin frequency, number of fin layers, fin thickness, fin height, and fin lance length are chosen as seven decision parameters. Also, a sensitivity analysis is performed to study the impact of geometrical parameters on each objective function. Finally, accuracy and efficiency of the presented algorithm is proven via illustrative single-objective optimization case studies which adopted from the references. Results demonstrate that the BAHPSO can detect optimal shape with higher accuracy compared to other algorithms.