Multiobjective Ant Lion Approaches Applied to Electromagnetic Device Optimization
| Autor: | Viviana Cocco Mariani, Sotirios K. Goudos, Leandro dos Santos Coelho, Christos S. Antonopoulos, Spiridon Nikolaidis, Juliano Pierezan, Nikolaos V. Kantartzis, Achilles D. Boursianis |
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| Rok vydání: | 2021 |
| Předmět: |
010302 applied physics
metaheuristics Technology Mathematical optimization Computer science brushless DC motor design 020209 energy MathematicsofComputing_NUMERICALANALYSIS 02 engineering and technology Random walk ComputingMethodologies_ARTIFICIALINTELLIGENCE 01 natural sciences Multi-objective optimization Swarm intelligence Tournament selection Field (computer science) electromagnetic optimization Motor design 0103 physical sciences 0202 electrical engineering electronic engineering information engineering multiobjective optimization Electromagnetic optimization ant lion optimizer Metaheuristic |
| Zdroj: | Technologies, Vol 9, Iss 35, p 35 (2021) |
| ISSN: | 2227-7080 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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