Swarm intelligence metaheuristics have been successfully used for hard optimization problems. After the initial introduction phase such algorithms are further improved by modifications and hybridizations. Parallelization is usually introduced for performance improvement and better resources utilization. In this paper we present an improved parallelized artificial bee colony (ABC) algorithm with multiple swarm inter-communication and learning that not only significantly improves computational time, but also improves the results. Proposed algorithm was tested on large set of standard benchmark functions and it outperformed the state-of-art ABC algorithm.
Artificial bee colony, Optimization metaheuristics, Swarm intelligence, Parallelized algorithms, Nature inspired algorithms.
Milos SUBOTIC, Milan TUBA, "Parallelized Multiple Swarm Artificial Bee Colony Algorithm (MS-ABC) for Global Optimization", Studies in Informatics and Control, ISSN 1220-1766, vol. 23(1), pp. 117-126, 2014. https://doi.org/10.24846/v23i1y201412
All articles published by Studies in Informatics and Control (SIC) are published in accordance with the terms and conditions of the Creative Commons Attribution License (CC BY-NC-SA 4.0).