Using the cheetah optimizer for cost reduction in shell-and-tube and plate-fin heat exchangers

This study investigates the effectiveness of the Cheetah Optimizer for optimizing heat exchanger designs, specifically for shell and tube and plate fin heat exchangers. The optimizer's performance was validated through comparisons with established metaheuristic methods, ensuring robust results. In the case of shell and tube heat exchangers, the Cheetah Optimizer achieved a 3% reduction in overall costs compared to Particle Swarm Optimization (PSO). While the Cheetah Optimizer did not outperform α-EHO and Gravitational Search Algorithm (GSA) in the first case study, it surpassed both in the second case. For plate fin heat exchangers, the Cheetah Optimizer demonstrated superior performance over the Imperialist Competitive Algorithm and Teaching Learning-Based Optimization. These findings confirm the Cheetah Optimizer as a viable and competitive alternative for heat exchanger design optimization, particularly in certain scenarios.