Benchmarking Meta-heuristic Optimization
This work provides a comparative analysis for researchers and practitioners in optimization, though it appears incremental as it applies existing methods without introducing new techniques.
The paper benchmarks several meta-heuristic optimization algorithms, including Genetic Algorithm, Differential Evolution, Particle Swarm Optimization, Grey Wolf Optimizer, and Simulated Annealing, by evaluating their performance on nonlinear and non-convex functions, but does not report specific numerical results.
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.