A Comparison of Meta-heuristic Based Optimization Methods Using Standard Benchmarks

Abstract

Optimization problems are a type of problem in which multiple solutions satisfy the problem’s constraints, so not only must a good solution be found, but the objective is to find the best solution among all those considered valid. Optimization problems can be solved by using deterministic and stochastic algorithms. Those categories can be divided into different kinds of problems. One of the categories inside stochastic algorithms is metaheuristics. This work implements three well-known meta-heuristics –Grey Wolf Optimizer, Whale Optimization Algorithm, and Moth Flame Optimizer–, and compares them using ten mathematical optimization problems that combine non-constrained from other studies and constrained problems from CEC2017 competition. Results show the Grey Wolf Optimizer as the method with faster convergence and best fitness for almost all the problems. This work aims to implement and compare various metaheuristics to carry out future work on solving various real-world problems.