Artificial bee colony algorithm on distributed environments

Artificial Bee Colony (ABC) is a metaheuristic approach in which a colony of artificial bees cooperates in finding good solutions for numerical optimization problems. ABC is adopted widely for use in several domains of solution optimization. However, the algorithm generally requires a considerably large computational time and resources. In order to enhance the performance of this algorithm for a large problem size, we introduce a distributed version of ABC. In our parallel algorithm, the entire bee colony is decomposed into several subgroups. Each subgroup then performs a local search concurrently on each processor node. The local best solutions are then exchanged among processor nodes. The algorithm implementation utilizes the message passing technique as a communication paradigm. We then empirically assess the performance based on both result accuracy and algorithm's efficiency. The experimental results show improvement in both solution quality and computing time when comparing to the sequential ABC algorithm.