Patch-Levy-based initialization algorithm for Bees Algorithm

• Published in: Applied soft computing 2014-10, Vol.23, p.104-121
• Main Authors: Hussein, Wasim A., Sahran, Shahnorbanun, Sheikh Abdullah, Siti Norul Huda
• Format: Article
• Language: English
• Subjects: Bees Algorithm; Levy flight; Metaheuristic algorithm; Patch environment; Population initialization
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2014.06.004

•A novel initialization algorithm for Bees Algorithm (BA) has been proposed.•The enhanced Bees Algorithm compared with other BA variants.•The enhanced Bees Algorithm compared with the Artificial Bee Colony and its variants.•The enhanced algorithm outperforms other algorithms in terms of convergence speed.•The enhanced algorithm outperforms other algorithms in terms of quality and success rate. The Bees Algorithm (BA) is a population-based metaheuristic algorithm inspired by the foraging behavior of honeybees. This algorithm has been successfully used as an optimization tool in combinatorial and functional optimization fields. In addition, its behavior very closely mimics the actual behavior that occurs in nature, and it is very simple and easy to implement. However, its convergence speed to the optimal solution still needs further improvement and it also needs a mechanism to obviate getting trapped in local optima. In this paper, a novel initialization algorithm based on the patch concept and Levy flight distribution is proposed to initialize the population of bees in BA. Consequently, we incorporate this initialization procedure into a proposed enhanced BA variant. The proposed variant is more natural than conventional variants of BA. It mimics the patch environment in nature and Levy flight, which is believed to characterize the foraging patterns of bees in nature. The results of experiments conducted on several widely used high-dimensional benchmarks indicate that our proposed enhanced BA variant significantly outperforms other BA variants and state-of-the-art variants of the Artificial Bee Colony (ABC) algorithm in terms of solution quality, convergence speed, and success rate. In addition, the results of experimental analyses conducted indicate that our proposed enhanced BA is very stable, has the ability to deal with differences in search ranges, and rapidly converges without getting stuck in local optima.