Multi-objective lichtenberg algorithm: A hybrid physics-based meta-heuristic for solving engineering problems

Abstract

With the advancement of computing and inspired by optimal phenomena found in nature, several algorithms capable of solving complex engineering problems have been developed. This work details the development of the Multi-objective Lichtenberg Algorithm, the version capable of dealing with more than one objective of a newly created meta-heuristic inspired by the propagation of radial intra-cloud lightning and Lichtenberg figures. The algorithm considers in its optimization routine a hybrid system based on both the population and the trajectory, demonstrating a great capacity for exploration and exploitation since it distributes points to be evaluated in the objective function through a Lichtenberg figure that is shot in sizes and different rotations at each iteration. The Multi-objective Lichtenberg Algorithm (MOLA) is the first hybrid multi-objective meta-heuristic and was tested against traditional and recent meta-heuristics using famous and complex test function groups and also constrained complex engineering problems. Regarding important metrics for convergence and coverage assessment, the Multi-objective Lichtenberg Algorithm proved to be a promising multi-objective algorithm surpassing others traditional and recent algorithms such as NSGA-II, MOPSO, MOEA/D, MOGOA and MOGWO with expressive values of convergence and maximum spread.