A COMPARISON OF OPTIMIZATION ALGORITHMS FOR STANDARD BENCHMARK FUNCTIONS

Abstract

Optimization algorithms are the search methods that are inspired from the natural biological evolution and social behavior of animals, insects, birds and humans, etc. The need of introducing the optimization algorithm is to achieve a near optimal solutions for complex and non-linear problems for which numerical methods may fail. This paper compares the performance of ten popular optimization algorithms: Grasshopper Optimization Algorithm, Adaptive Cuckoo Search Algorithm, Bird Swarm Algorithm, Chaotic Ant Lion Optimization Algorithm, Adaptive Wind Driven Algorithm, Real Coded Genetic Algorithm, Particle Swarm Optimization, Teaching Learning Based Optimization, Whale Optimization Algorithm and Ant Colony Optimization. A performance comparison of these optimization algorithms has been made on basis of accuracy by using five standard benchmark functions and it has been found that Whale Optimization Algorithm is performing better than other algorithms.