A decentralization approach for swarm intelligence algorithms in networks applied to multi swarm PSO

Purpose - The purpose of this paper is to present an approach for the decentralization of swarm intelligence algorithms that run on computing systems with autonomous components that are connected by a network. The approach is applied to a particle swarm optimization (PSO) algorithm with multiple sub-swarms. PSO is a nature inspired metaheuristic where a swarm of particles searches for an optimum of a function. A multiple sub-swarms PSO can be used for example in applications where more than one optimum has to be found. Design/methodology/approach - In the studied scenario the particles of the PSO algorithm correspond to data packets that are sent through the network of the computing system. Each data packet contains among other information the position of the corresponding particle in the search space and its sub-swarm number. In the proposed decentralized PSO algorithm the application specific tasks, i.e. the function evaluations, are done by the autonomous components of the system. The more general tasks, like the dynamic clustering of data packets, are done by the routers of the network. Findings - Simulation experiments show that the decentralized PSO algorithm can successfully find a set of minimum values for the used test functions. It was also shown that the PSO algorithm works well for different type of networks, like scale-free network and ring like networks. Originality/value - The proposed decentralization approach is interesting for the design of optimization algorithms that can run on computing systems that use principles of self-organization and have no central control.