Benchmarking Meta-heuristic Optimization

• URL: http://arxiv.org/abs/2007.13476v1
• Date: Mon, 27 Jul 2020 12:25:31 GMT
• Title: Benchmarking Meta-heuristic Optimization
• Authors: Mona Nasr, Omar Farouk, Ahmed Mohamedeen, Ali Elrafie, Marwan Bedeir and Ali Khaled
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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