Effective motive profiles and swarm compositions for motivated particle swarm optimisation applied to task allocation

This paper examines the behaviour of agents with four distinct motive profiles with the aim of identifying the most effective profiles and swarm compositions to aid task discovery and allocation in a motivated particle swarm optimisation algorithm. We first examine the behaviour of agents with affiliation, achievement and power motive profiles and the impact on behaviour when these profiles are perturbed. We then examine the behaviour of swarms with different compositions of agents motivated by affiliation, achievement, power and a new leadership motive profile. Results show that affiliation-motivated agents tend to perform local search and allocate themselves to tasks. In contrast, power-motivated agents tend to explore to find new tasks. These agents perform better in the presence of achievement-motivated agents, informing the design of the leadership motive profile, which demonstrates good performance in two task allocation settings studied in this paper.

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