A Study on the Performance of Grey Wolf Optimizer
No Thumbnail Available
Date
2021
Authors
Journal Title
Journal ISSN
Volume Title
Abstract
In recent years there is an enormous increase in the emergence of non-deterministic search methods. The effective way of animals in problem-solving (like discovering the shortest path to the food source) has been examined by scientists and swarm intelligence has become a research field that imitates the behaviour of animals in swarm. The moth-flame optimization (MFO) algorithm, salp swarm algorithm (SSA), firefly algorithm (FFA), bat (BAT) algorithm, cuckoo search (CS) algorithm, genetic algorithm (GA), and grey wolf optimizer (GWO) are some of the swarm intelligence based non-deterministic methods. In the present study, the seven methods above are investigated separately. Five mathematical functions are resolved individually by these seven methods. Each algorithm is run 30 times in each benchmark function. The performances of these optimization methods are evaluated and compared within each function individually. Performances of algorithms over convergence are compared by plotting convergence rate graph and boxplots of methods for each function. Considering most of the functions, GWO is observed to be stronger than other algorithms. © 2021, Springer Nature Switzerland AG.