Benchmarking Meta-heuristic Optimization

TL;DR

This paper benchmarks various meta-heuristic algorithms like Genetic Algorithm, Differential Evolution, and Particle Swarm Optimization on nonlinear and non-convex problems, comparing their performance and convergence.

Contribution

It provides a comparative evaluation of multiple meta-heuristic algorithms on nonlinear optimization tasks, highlighting their strengths and weaknesses.

Findings

Differential Evolution outperforms others in convergence speed.

Genetic Algorithm achieves results close to the optimal in certain cases.

Particle Swarm Optimization shows competitive performance in diverse scenarios.

Abstract

Solving an optimization task in any domain is a very challenging problem, especially when dealing with nonlinear problems and non-convex functions. Many meta-heuristic algorithms are very efficient when solving nonlinear functions. A meta-heuristic algorithm is a problem-independent technique that can be applied to a broad range of problems. In this experiment, some of the evolutionary algorithms will be tested, evaluated, and compared with each other. We will go through the Genetic Algorithm\, Differential Evolution, Particle Swarm Optimization Algorithm, Grey Wolf Optimizer, and Simulated Annealing. They will be evaluated against the performance from many points of view like how the algorithm performs throughout generations and how the algorithm's result is close to the optimal result. Other points of evaluation are discussed in depth in later sections.