Enhancing Pressure Vessel Design Optimization with a Hybrid CSA-PSO Algorithm - Adel Ahmed, Ammar; Badran, Amera Istiqlal; M. Ahmed, Ibrahim; T. Qassim, Yahya; Zebari, Dilovan Asaad; Abdulhakeem, Reving Masoud (Journal of Soft Computing and Data Mining, 2025)
The optimization of the Pressure Vessel Design Problem (PVDP) plays a crucial role in various engineering applications where structures with the most efficient material utilization are required. However, the intricate interplay between design parameters and strict constraints presents a significant barrier for classical optimization algorithms to efficiently traverse the convoluted landscape of PVDP. Applying metaheuristic algorithms has been introduced as a solution to tackle these problems. The techniques introduced have the potential to confront the difficulties associated with nonlinearity, multimodality, and complex constraints that are frequently encountered in complex engineering optimization problems. This work carries out a comprehensive and comparative analysis of the performance of well-established metaheuristic algorithms in solving the PVDP. The methods under consideration at present are Big Bang –Big Crunch (BB-BC), Cuckoo Search Algorithm (CSA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Vibrating Particles System (VPS), and Water Evaporation Optimization (WEO). Third, we introduced the CSA-PSO algorithm. Through extensive numerical experiments, their rates of convergence, the quality of the solutions, and the amount of computation required are compared and analyzed, revealing their relative pros and cons in terms of the PVDP. As a result, the knowledge of how metaheuristic algorithms perform on the PVDP can be exploited when selecting optimization methods.