Comparative Analysis Among Five Stochastic Search-Based Optimization Algorithms

Evolution of biological events is represented with help of five stochastic search-based optimization algorithms which is: particle swarm optimization, differential evolution, teaching, and learning optimization technique, shuffled frog leaping optimization technique, and gravitational search optimization. They help us to overcome large-scale optimization problems and convert it to near optimum solutions. The explanation in the paper explains the framing and outcomes of five such techniques along with their respective pseudocode to facilitate smooth implementation. This paper also contains a comparison of a benchmark among the entire algorithm with respect to the quality of results, processing time, and convergence speed. The comparative result of EAs is discussed in this paper along with guidelines to interpret the best operators for each algorithm.