Rational swarm for global optimisation
by Chenyang Li; Alfredo Garcia
International Journal of System Control and Information Processing (IJSCIP), Vol. 1, No. 1, 2012

Abstract: In this paper, we propose a novel bio-inspired multi-agent co-operative searching methodology for global optimisation, named Rational Swarm algorithm. It can be used both as a meta-heuristic guiding local search algorithm and as a high-level multi-agent co-operative searching strategy to coordinate multiple agents using meta-heuristics. In this work, the Rational Swarm methodology has been applied to a popular meta-heuristics Simulated Annealing (SA) and a pure local search algorithm Monotonic Sequential Basin Hopping (MSBH). Numerical experiments on various continuous optimisation problems show Rational Swarm can improve the performance of applied meta-heuristics/heuristics in terms of solution quality and robustness under the same computational budget. Convergence analysis gives the theoretical insights about why the proposed Rational Swarm Methodology will work.

Online publication date: Fri, 23-Nov-2012

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