An experimental analysis of meta heuristic techniques on unimodal and multimodal functions

Abstract

The advancement in the technology leads to the increase in the complexity of the problems. The traditional heuristic algorithms are not suitable for the optimized results of such complex problems. This leads to generation of Meta heuristic techniques which incorporate the exploration as well as the exploitation search. This paper studies different state of art Meta heuristic techniques like ant colony optimization, particle swarm optimization, differential evolution and genetic algorithm. This paper also covers different stable modified version of these techniques and implements the same to analyze the performance on different unimodal and the multimodal functions. The analysis clearly signifies the use of Meta heuristic techniques based on application.