Multipopulation artificial bee colony algorithm based on a modified probability selection model

Abstract

Artificial bee colony (ABC) performs excellently over many problems, but it has some shortcomings, such as weak exploitation as well as slow convergence. For the sake of dealing with these issues, a modified ABC known as MPABC is presented. Firstly, the entire population is partitioned into two different subpopulations at the stage of employed bees, and they use different search strategies. Then, a new probability selection strategy is designed on the basis of the principle of Soft Maximum function. Finally, a novel search method is constructed for improving the intensity of exploitation by gradually increasing the ratio of the current optimal solutions. In order to comprehensively validate the capability of MPABC, 12 benchmark problems are employed. Computational results clearly demonstrate MPABC surpasses the basic ABC and some other famous ABCs.

Multi-UAV path planning based on IB-ABC with restricted planned arrival sequence

“…The ABC algorithm is proposed by Karaboga. It is known for its multi-variable global optimization capability, which can overcome the trap of local optimization while searching for the global [6]. In order to make better use of the advantages of the ABC algorithm, Chiang et al proposed a discretized optimized honey source algorithm, which is adjusted together with the core parameters of the support vector machine to enhance the classification accuracy and convergence speed.…”