Abstract
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.
Highlights
Population-based metaheuristics such as evolutionary algorithms and swarm-based intelligence (SI) algorithms have gained prominence to solve different kinds of electromagnetic optimization problems [1,2,3,4,5,6,7,8,9,10] involving nonlinear, non-convex, multi-modal, and non-differentiable functions mainly due to advantages when compared with single-point search algorithms of mathematical programming in terms of no need to objective function be differentiable and continuous, and global searching capability
Brushless direct current (DC) motors, known as electronically commutated motor and synchronous nous DC motors, are synchronous motors driven by DC electricity through an inverter or motors,power are synchronous motors driven
Considering Be, these schemes have presented slightly smaller values and the evaluated to a The brushless motor multiobjective design problem
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. It is not easy to obtain a proper result in a large solution space where multiobjective algorithms need to explore and collect a set of optimal solutions at the same time to satisfy all objectives. A major problem is to design a DC wheel motor so that it operates optimally in the sense of producing maximum efficiency with minimal material cost, and satisfy inequality constraints simultaneously. In this context, MOALO can be useful to obtain a well-distributed set of Pareto-optimal solutions.
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