A parallel Bees Algorithm implementation on GPU

Bees Algorithm is a population-based method that is a computational bound algorithm whose inspired by the natural behavior of honey bees to finds a near-optimal solution for the search problem. Recently, many parallel swarm based algorithms have been developed for running on GPU (Graphic Processing Unit). Since nowadays developing a parallel Bee Algorithm running on the GPU becomes very important. In this paper, we extend the Bees Algorithm (CUBA (i.e. CUDA based Bees Algorithm)) in order to be run on the CUDA (Compute Unified Device Architecture). CUBA (CUDA based Bees Algorithm). We evaluate the performance of CUBA by conducting some experiments based on numerous famous optimization problems. Results show that CUBA significantly outperforms standard Bees Algorithm in numerous different optimization problems. Finding an optimal solution for the search problem becomes an important research question nowadays [21–23]. There are increasingly swarm intelligence [4] which is in nature the collective behavior of social animals used for finding a near optimal solution. The swarm-based optimization algorithms (SOAs) drive a search towards the optimal solution. Various algorithms, such as Ant Colony Optimization (ACO) proposed by Marco Dorigo [1], Genetic Algorithm (GA) [24], Particle swarm optimization (PSO) [3] developed by Kennedy, Artificial Bee Colony Algorithm (ABC) by proposed D. Karaboga [5], and Bees Algorithm proposed by DT Pham [2], mod-eled the behaviors of the swarm of animals with social organization. Self-organization is one of the system features that gets global-level response by means of many different low-level interactions. In the SOAs, the ACO algorithm is a non-greedy population-based algorithm which emulates the behavior of real ants. The GA is based on natural selection and genetic recombination. It efficiently exploits historical information to speculate on new search areas with improved performance. The PSO is an optimization procedure based on the social behavior of groups of organizations. And the ABC is also another optimization algorithm inspired on the intelligent behavior of honey bee swarms. Bees Algorithm (BA) [2] is also a population-based method to search optimization of the problems which is inspired by the behavior of honey bees [2,6]. The algorithm performs a kind of neighborhood search combined with random search and can be used for both combinatorial optimization [25,26] and functional optimization [2]. Based on the BA, researchers have come up with several real-world applications such as data mining [7], robot controlling [8], electronic engineering [9], job scheduling [10], E-Testing [35], task allocation [36], and so …