Swarm intelligence algorithms are created to build collective intelligence based on inherent properties of populations. However, this sort of problem solving approaches use collective evolution of solutions, which make up the population relied on. Therefore the collective behaviour development is omitted. This paper addresses agentification of individuals forming up swarms, populations considered in problem solving using swarm intelligence algorithms. As each agentified individual will keep its own intelligence and use it in problem solving, the swarm intelligence algorithms used will be coordinating the agents and be supporting them in diversification of the search. This paper describes a framework of swarms of metaheuristic agents for problem solving and discusses the performance of particle swarm optimisation algorithms for purpose regarding the homogeneity in agents.
['Mehmet Emin Aydin', 'Joyce Y. Wu', 'Liang Zhang']
Swarms of Metaheuristic Agents: A Model for Collective intelligence