Abstract
Artificial Bee Colony (ABC) algorithm is one of the efficient nature-inspired optimization algorithms for solving continuous problems. It has no sensitive control parameters and has been shown to be competitive with other well-known algorithms. However, the slow convergence, premature convergence, and being trapped within the local solutions may occur during the search. In this paper, we propose a new Modified Artificial Bee Colony (MABC) algorithm to overcome these problems. All phases of ABC are determined for improving the exploration and exploitation processes. We use a new search equation in employed bee phase, increase the probabilities for onlooker bees to find better positions, and replace some worst positions by the new ones in onlooker bee phase. Moreover, we use the Firefly algorithm strategy to generate a new position replacing an unupdated position in scout bee phase. Its performance is tested on selected benchmark functions. Experimental results show that MABC is more effective than ABC and some other modifications of ABC.
Highlights
Most continuous optimization problems in various application areas such as science, engineering, economics, and management are nonlinear and difficult to find the optimal solutions
We select 8 benchmark functions consisting of 2 functions from one of 4 different types: unimodal and separable (US), unimodal and nonseparable (UN), multimodal and separable (MS), and multimodal and nonseparable (MN)
The values “+”, “0”, and “–” denote that Modified Artificial Bee Colony (MABC) performs significantly better than, to, and worse than a compared method
Summary
Most continuous optimization problems in various application areas such as science, engineering, economics, and management are nonlinear and difficult to find the optimal solutions. In 2010, Zhu and Kwong presented the Gbest-guided ABC (GABC) algorithm [14] They improved the search equation to increase the exploitation in employed and onlooker bee phases by using the strategy of the particle swarm optimization. In 2012, Gao et al proposed a modified ABC algorithm (ABC/best) [9], which is inspired by the differential evolution algorithm, to improve the exploitation by searching only around the best solution of the previous iteration. Their results showed better performance when compared with ABC. The performance of MABC is tested on selected benchmark functions and compared with those of ABC, GABC, ABCROC, and ABC/best
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