The Artificial Bee Colony (ABC) algorithm is a new swarm optimization algorithm with good numerical functions optimization results. In order to enhance the performance ability of ABC algorithm, a hybrid ABC (HAB) algorithm is presented where swarming behavior of bacterial foraging optimization algorithm is introduced into the ABC algorithm to do local search. The performance of the proposed method is examined on well-known six numerical benchmark functions and the obtained results are compared with basic ABC algorithm and BFO algorithm. The experimental results show that the proposed approach is very effective method for solving numeric benchmark functions and successful in terms of solution quality and convergence to the global optimum, especially on the multimodal functions.

Abstract-A hybrid evolutionary algorithm is presented here to solve the problem of conformal array pattern synthesis. In order to overcome the disadvantage of standard genetic algorithm (GA) and artificial bee colony algorithm (ABC), a hybrid algorithm is introduced, which combines GA and ABC to take advantages of both methods. Crossover operator of GA is adopted to maintain the diversity of population. Multi-dimensional neighborhood search strategy of ABC is introduced to improve the local search efficiency for conformal array pattern synthesis. Finally, the hybrid GA/ABC algorithm is used to optimize the weight vector of the circular conformal array. Experimental results show that the proposed method can achieve the desired pattern very well, and has a better performance than standard GA and ABC.