| Summary: | 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.
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