Multiobjective Ant Lion Approaches Applied to Electromagnetic Device Optimization

Technologies

VivianaC. Mariani¹  Leandrodos S. Coelho¹  Juliano Pierezan¹  NikolaosV. Kantartzis²  Christos.S. Antonopoulos²  AchillesD. Boursianis³  SotiriosK. Goudos³  Spiridon Nikolaidis³ 

[1] Department of Electrical Engineering, Federal University of Parana, Curitiba 80060-000, PR, Brazil;Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece;ELEDIA@AUTH, Department of Physics, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece;

关键词: electromagnetic optimization;    multiobjective optimization;    metaheuristics;    brushless DC motor design;    ant lion optimizer;   

DOI  :  10.3390/technologies9020035

来源: DOAJ

【 摘 要 】

Nature-inspired metaheuristics of the swarm intelligence field are a powerful approach to solve electromagnetic optimization problems. Ant lion optimizer (ALO) is a nature-inspired stochastic metaheuristic that mimics the hunting behavior of ant lions using steps of random walk of ants, building traps, entrapment of ants in traps, catching preys, and re-building traps. To extend the classical single-objective ALO, this paper proposes four multiobjective ALO (MOALO) approaches using crowding distance, dominance concept for selecting the elite, and tournament selection mechanism with different schemes to select the leader. Numerical results from a multiobjective constrained brushless direct current (DC) motor design problem show that some MOALO approaches present promising performance in terms of Pareto-optimal solutions.

【 授权许可】

Unknown