Multiobjective Ant Lion Approaches Applied to Electromagnetic Device Optimization

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