Efficient multi agent based self organization techniques for The genetic algorithm

Abstract

Genetic Algorithms (GAs) are popular global optimization approach for solving complex problems with large search space, based on the survival of the fittest concept of natural evolution. The traditional GA consists of different stages, namely: population initialization, selection, crossover, mutation and evaluation. The traditional GA with random individual initialization method is quite simple and obviously effective in terms of computation time; however, the population may contain poor quality solutions which would take longer time to converge to an optimal solution. The Meta heuristic models could be used at each stage of GA, results in Hybrid GA, may improve the exploration ability of the algorithm to obtain the optimal solution. Self-organization is the scenario in which individuals modify themselves based on the information of better quality individuals.In recent literatures, bio-inspired algorithms are used as a booster for the traditional optimization algorithms such as genetic algorithm and others, to improve the overall performance. Various recent and best working animal inspired algorithms had been proposed and utilized as self- organization model for the genetic algorithm in order to solve large sized test problems. The biological behaviour and intelligence of these animals can be utilized among the individuals of Genetic Algorithm to self-organize in order to better cooperate and coordinate among themselves.In this perspective, the research has three prominent Self- Organization Genetic Algorithm (SOGA) models, namely: Self-Organization based on Grey Wolf Based Incremental Learning (GWIL-SOGA), Self- Organization based on the Group Mosquito Host Seeking Behaviour (GMHSA-SOGA), and Stinging Behaviour of Bees (SBB-SOGA). The first model uses the bio-inspired computing model, Grey-wolf optimization newline