Randomized directed neighborhoods with edge migration in particle swarm optimization

Abstract

A key feature of Particle Swarm Optimization algorithms is that fitness information is shared with individuals in a particle's neighborhood. The kind of neighborhood structure that is used affects the rate at which information is disseminated throughout the population. Existing work has studied global and simple local topologies, as well as more complex, but fixed neighborhood structures. This paper looks at randomly generated, directed graph structures in which information flows in one direction only, and also outgoing edges randomly migrate from one source node to another. Experimental evidence indicates that this random dynamic topology, when used with an inertia weight PSO, performs competitively with some existing methods and outperforms others.

Particle swarm optimisation with gradually increasing directed neighbourhoods

“…Mohais et al [11,21] have proposed to use random neighbourhoods in the PSOs, together with dynamism operators. Their random neighbourhoods can be represented using directed graphs as the relationships between particles are single directional.…”

“…Both the size and member of the in-neighbourhood set H + t (p i ) are generated uniformly. Two methods of dynamism called random edge migration and total re-structuring are given in [11,21].…”

“…The PSO-NO is the PSO with a neighbourhood operator that was proposed by Suganthan [5]. The PSO-RDN is the PSO with randomised directed neighbourhoods and edge migrations [11]. The dynamic multi-swarm PSO (PSO-DMS) uses many small swarms and regroups these swarms frequently [20].…”

Particle swarm optimization with spatially meaningful neighbours

“…Dynamic random topologies for both directed and undirected connections include variations such as: randomly increasing the number of undirected neighbour connections with successive iterations (moving the swarm from a state of exploration to one of exploitation) [2][18], randomly changing unconnected neighbours [14] and using random discrete structures and edge migrations for directed connections [17]. Experiments with different aspects of neighbourhoods and network connections including effects of out degree and the size of the population have been performed to help determine the properties of topologies that make for successful societies [14][6] [3].…”

“…Directed connections have also been used with these static-random topologies. Experiments with random static topologies include the use of discrete random undirected graphs and acyclic random links [16] [17].…”