Abstract
Particle swarm optimisation (PSO) is a population-based stochastic swarm intelligence (SI) optimization algorithm that converges very fast and thus lacks diversity. Heterogeneous vector evaluated particle swarm optimisation (HVEPSO) tries to introduce the ability to balance exploration and exploitation by increasing diversity of the particles’ behaviour. This study evaluates the performance of different HVEPSO configurations in static multi-objective environments. The particles of each sub-swarm of HVEPSO use different position and velocity update approaches selected from a behaviour pool. Strategies to determine when to change the particles’ behaviour are investigated for various knowledge transfer strategies (KTSs). Results indicate that the parent-centric crossover (PCX) KTS using the dynamic heterogeneous PSO (dHPSO) behaviour selection strategy with periodic window management performed the best. However, HVEPSO experienced problems converging to the optimal solutions and finding a diverse set of solutions for certain benchmarks, such as WFG1, which is a separable unimodal function with a convex Pareto-optimal front.
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Doman, D., Helbig, M., Engelbrecht, A. (2016). Heterogeneous Vector-Evaluated Particle Swarm Optimisation in Static Environments. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9712. Springer, Cham. https://doi.org/10.1007/978-3-319-41000-5_29
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