Analysis Of Genetic Algorithm, Particle Swarm Optimization and Simulated Annealing On Benchmark Functions

Optimization is a crucial part of every field that requires computation and plays an important role in modern optimization. Metaheuristics algorithms are stochastic algorithms designed in such a way that they don't require the calculation of gradient for optimization. Hence they can be used for optimizing non-linear functions too. Since most real-world optimization problems are non-linear in nature, algorithms provide a sufficiently good solution (close to actual solution) for such optimization problems. This paper is aimed to explain the Genetic Algorithm(GA), Simulated Annealing(SA), and Particle Swarm Optimization(PSO) and compare their performance on benchmark functions. Benchmark functions provides a baseline measurement of the performance of algorithms and allows the algorithm to be compared to some other algorithms in the same manner. The algorithm runs on these functions with an aim to locate an optimal position. The performance and the results obtained can be affected by the parameter setting or operations inside each method. Based on the results, PSO performed better compared to GA and SA in obtaining best minimum fitness.