Abstract
Artificial bee colony (ABC) algorithm has good performance in discovering the optimal solutions to difficult optimization problems, but it has weak local search ability and easily plunges into local optimum. In this paper, we introduce the chemotactic behavior of Bacterial Foraging Optimization into employed bees and adopt the principle of moving the particles toward the best solutions in the particle swarm optimization to improve the global search ability of onlooker bees and gain a hybrid artificial bee colony (HABC) algorithm. To obtain a global optimal solution efficiently, we make HABC algorithm converge rapidly in the early stages of the search process, and the search range contracts dynamically during the late stages. Our experimental results on 16 benchmark functions of CEC 2014 show that HABC achieves significant improvement at accuracy and convergence rate, compared with the standard ABC, best-so-far ABC, directed ABC, Gaussian ABC, improved ABC, and memetic ABC algorithms.
Highlights
In recent years, facing optimization problems, people have put forward a series of traditional solving methods, such as linear programming and dynamic planning
We introduce the chemotactic behavior of Bacterial Foraging Optimization into employed bees and adopt the principle of moving the particles toward the best solutions in the particle swarm optimization to improve the global search ability of onlooker bees and gain a hybrid artificial bee colony (HABC) algorithm
ABC algorithm has been widely used in many fields, for example, determining the optimal size and selecting optimum locations of shunt capacitors by El-Fergany and Abdelaziz [8], solving the economic lot scheduling problem by Bulut and Tasgetiren [9], segmenting SAR image by Ma et al [10], enhancing image contrast by Draa and Bouaziz [11], and solving the Leaf-Constrained Minimum Spanning Tree (LCMST) problem by Singh [12]
Summary
In recent years, facing optimization problems, people have put forward a series of traditional solving methods, such as linear programming and dynamic planning. To improve global searching capability by escaping the local solutions, Alatas [16] adopted a method to adjust parameters for ABC algorithm using random numbers generated from different chaotic systems. Xiang et al proposed a particle swarm inspired multielitist ABC algorithm [17] which updates the parameters of the solutions using global best solution and an elitist randomly selected from an elitist archive. To efficiently solve the constraint optimization problems, Li and Yin [18] presented a self-adaptive constrained ABC algorithm by introducing feasible rule and multiobjective optimization methods. To enhance the ability of local searching and exploitation, we applied chemotactic behavior in Bacterial Foraging Optimization algorithm [19] into employed bees and adopted the global best solution search equation in PSO [20, 21] algorithm into onlooker bees.
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