Rational swarm for global optimisation Online publication date: Fri, 23-Nov-2012
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.
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