An improved artificial bee colony algorithm based on elite search strategy with segmentation application on robot vision system

Abstract

Summary Aiming at accelerating the convergence speed and enhancing relative poor local search ability of the traditional artificial bee colony algorithm (ABC), this article introduces an ABC with a new elite search strategy. First, we propose a strategy of recording individuals with high performance. Then bees have more chances to learn from a real elite. In the onlooked bee phase, its updating equation is changed for having more opportunities to search in a valuable area. Furthermore, for saving the value of function evaluations, a new learning equation for the best onlooked bee is proposed. The image segmentation of a robot binocular stereo vision system is a key problem in mechanical robot vision system, but the computation time limits its application. The experimental results show that the proposed algorithm achieves better performance on 10 benchmark functions and the image segmentation problem of mechanical robot in comparison with several other state of the art algorithms.

An advanced hybrid algorithm for nonlinear function optimization with real world applications

“…1 Presently, a bunch of optimization methods known as meta-heuristics algorithms (MAs) has been introduced to solve COPs, to overcome drawbacks of traditional optimization methods. According to mechanical differences the MAs can be categorized into four groups as follows: swarm intelligence algorithms (SIAs): inspired from behavior of social insects or animals like particle swarm optimization (PSO), 2 artificial bee colony (ABC), 3,4 animal migration optimization (AMO), 5 whale optimization algorithm (WOA), 6,7 social spider optimization (SSO), 8 chicken swarm optimization (CSO), 9 wind driven dragonfly algorithm (WDDA), 10 firefly algorithm (FA), 11 and so forth; evolutionary algorithms (EAs)-inspired from biology such as differential evolution (DE), 12 genetic algorithm (GA), 13 and so forth; physics based algorithms (PBAs): inspired by the rules governing a natural phenomenon like harmony search (HS), 14 gravitational search algorithm (GSA), 15 and so forth and human behavior based algorithms (HBAs): inspired from the human being like teaching-learning-based optimization (TLBO), 16 gaining sharing knowledge based Algorithm (GSK), 17 and so forth.…”