A Conceptual Comparison of Six Nature-Inspired Metaheuristic Algorithms in Process Optimization

Processes

Subham Pal¹  Shankar Rajendran²  Narayanan R. C.³  Ganesh N.⁴  Robert Čep⁵  Kanak Kalita⁶ 

[1] Department of Aerospace Engineering and Applied Mechanics, Indian Institute of Engineering Science and Technology, Howrah 711 103, India;Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur 522 502, India;Department of Computer Science and Engineering, Sona College of Technology, Salem 636 005, India;Department of Computer Science and Engineering, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai 600 062, India;Department of Machining, Assembly and Engineering Metrology, Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, 17. Listopadu 2172/15, 708 00 Ostrava, Czech Republic;Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi 600 062, India;

关键词: optimization;    non-traditional algorithms;    process optimization;    process parameters;    algorithms;   

DOI  :  10.3390/pr10020197

来源: DOAJ

【 摘 要 】

In recent years, several high-performance nature-inspired metaheuristic algorithms have been proposed. It is important to study and compare the convergence, computational burden and statistical significance of these metaheuristics to aid future developments. This study focuses on six recent metaheuristics, namely, ant lion optimization (ALO), arithmetic optimization algorithm (AOA), dragonfly algorithm (DA), grey wolf optimizer (GWO), salp swarm algorithm (SSA) and whale optimization algorithm (WOA). Optimization of an industrial machining application is tackled in this paper. The optimal machining parameters (peak current, duty factor, wire tension and water pressure) of WEDM are predicted using the six aforementioned metaheuristics. The objective functions of the optimization study are to maximize the material removal rate (MRR) and minimize the wear ratio (WR) and surface roughness (SR). All of the current algorithms have been seen to surpass existing results, thereby indicating their superiority over conventional optimization algorithms.

【 授权许可】

Unknown