Research on artificial bee colony algorithm with social cognition search strategy

Artificial bee colony (ABC) algorithm is the one of the newest nature inspired heuristics for optimization problems. In order to improve the convergence characteristics and to prevent the ABC to get stuck on local solutions, the modified algorithms based on social cognition thinking are proposed. The new candidate solutions are generated through mimicking the search mechanism of particle swarm optimization. To compare and analyze the performance of our proposed modified algorithms, a number of experiments are carried out on a set of well-known benchmark continuous optimization problems. Simulation results and comparisons with the standard ABC and several meta-heuristics show that the proposed algorithms can effectively enhance the searching efficiency and greatly improve the searching quality.