Ant Lion Approach Based on Lozi Map for Multiobjective Transformer Design Optimization

Abstract

Metaheuristic algorithm is a generic computational approach aiming at efficiently solving optimization problems, mainly global optimization problems. The No Free Lunch theorem states that no single algorithm can perform well on every optimization problem, encouraging the development of new optimization metaheuristics. Ant lion optimizer (ALO) is a nature inspired stochastic metaheuristic algorithm which mimics the hunting behavior of ant lions in nature using steps of hunting prey such as the random walk of ants, building traps, entrapment of ants in traps, catching preys, and re-building traps. In this paper, an ALO approach is adapted to multiobjective optimization (MOALO) using external archiving and ranking with crowding distance. Furthermore, a MOALO version with the control parameter setup based on Lozi map with chaotic dynamical behavior is also proposed to solve a Transformer Design Optimization (TDO) problem with two competing objectives. The effectiveness of the proposed algorithms is demonstrated by the simulations applied to a TDO problem.