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