An Improved Artificial Bee Colony Algorithm With Fitness-Based Information

An Improved Artificial Bee Colony Algorithm With Fitness-Based Information

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Artificial bee colony (ABC) algorithm is widely known for its distinguished exploration ability.However, its exploitation ability is relatively poor.To solve the problem, we propose a novel combinatorial search here strategy, whose guided vector can be freely switched between a random vector and the global best vector.

It can help improve the exploitation ability of ABC.At the same time, a random vector is beneficial to regulate the enhanced exploitation ability.In addition, both of them can pass information on to a current vector instead of only perturbing a current vector itself.

The two guided vectors are chosen with a probability depending on the ratio of fitness information of a current vector to that of the global-best vector.Thus, one of the two guided vectors can be adaptively selected to direct the search.In addition, a mechanism of frequency of perturbation is employed to enhance the scale of information sharing between a current vector and a guided vector for each onlooker bee.

Moreover, a modified simply boho classroom greedy selection mechanism is designed to choose a child vector inspired by simulated annealing.Furthermore, the search strategy of multiple scouts is also employed in the last stage.Based on all these modifications, an improved ABC (IABC) is proposed.

Finally, a few experiments are carried on 58 benchmark problems, including CEC2014 benchmark problems.The computational results exhibit the merit of IABC.