Authors: Chenyang Li; Alfredo Garcia
Addresses: Department of Systems and Information Engineering, University of Virginia, Charlottesville, Virginia 22904-4747, USA ' Department of Systems and Information Engineering, University of Virginia, Charlottesville, Virginia 22904-4747, USA
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.
Keywords: metaheuristics; cooperative search; global optimisation; swarm intelligence; bio-inspired computation; multi-agent systems; MAS; agent-based systems; multiple agents; simulated annealing; local search; rational swarm.
International Journal of System Control and Information Processing, 2012 Vol.1 No.1, pp.3 - 28
Available online: 18 Nov 2012 *Full-text access for editors Access for subscribers Purchase this article Comment on this article