Abstract
Nowadays, real word problems are often characterized by very large and complex search spaces. Finding high-quality solutions to NP-hard problems in a scalable and efficient manner has become a priority in the field of combinatorial optimization. This paper presents a new framework to handle large satisfiability problem instances (SAT) using a method designed by hybridizing two promising approaches. The first one is a bio-inspired metaheuristic called Bee Swarm Optimization (BSO) and the second one is the multilevel paradigm. The results obtained by comparing BSO with and without the multilevel concept showed that the latter improved significantly the performance of BSO.
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Djeffal, M., Drias, H. (2013). Multilevel Bee Swarm Optimization for Large Satisfiability Problem Instances. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2013. IDEAL 2013. Lecture Notes in Computer Science, vol 8206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41278-3_72
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DOI: https://doi.org/10.1007/978-3-642-41278-3_72
Publisher Name: Springer, Berlin, Heidelberg
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