Abstract
Artificial bee colony (ABC) is a novel population-based optimization method, having the advantage of less control parameters, being easy to implement, and having strong global optimization ability. However, ABC algorithm has some shortcomings concerning its position-updated equation, which is skilled in global search and bad at local search. In order to coordinate the ability of global and local search, we first propose a self-adaptive ABC algorithm (denoted as SABC) in which an improved position-updated equation is used to guide the search of new candidate individuals. In addition, good-point-set approach is introduced to produce the initial population and scout bees. The proposed SABC is tested on 12 well-known problems. The simulation results demonstrate that the proposed SABC algorithm has better search ability with other several ABC variants.
Highlights
Simulation results show that Artificial bee colony (ABC) is superior to many other population-based optimization methods, namely, genetic algorithm (GA), evolution strategies (ES), and particle swarm optimization (PSO) [9, 10]
The experimental results of Self-Adaptive Artificial Bee Colony (SABC) and basic ABC are given in Table 2 regarding the best, the mean, the worst, the standard deviation (St.dev), and the convergence iteration (CI)
An improved version of ABC, called SABC, is developed by using good-point-set initialization employed to enhance the population distribution, rank-based selection strategy used to enhance the global search ability, self-adaptive positionupdated equation applying for balancing the exploration and exploitation
Summary
Population-based optimization algorithms, such as whale optimization algorithm (WOA) [1], flower pollination algorithm (FPA) [2], bacterial foraging optimizer (BFO) [3], cuckoo search algorithm (CSA) [4], fruit fly optimization (FFO) [5], gravitational search optimizer (GSO) [6], and chemical reaction optimization (CRO) [7], have many advantages over classical optimization methods and have been successfully and broadly applied to solve global continuous optimization problems in the last few decades [6]. Zhu and Kwong [11] presented an improved Gbest-guided ABC (denoted as GABC) through combining the global best (Gbest) individual with the position-updated equation to enhance the ability of local search. Li et al [15] presented an improved ABC variant based on inertia weight and accelerating factors and to coordinate the ability of global and local search. Gao et al [18] presented an improved version of ABC (denoted as CABC) based on a modified position-updated equation. Different from the previous work, the position-updated equation is modified by self-adaptive adopting of the previous and global best solution to generate new candidate offspring to coordinate the ability of global and local search.
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