A Diversity-Guided Particle Swarm Optimizer - the ARPSO

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  • Jacob Svaneborg Vesterstrøm, Denmark
  • Jacques Riget, Denmark
  • Department of Computer Science
The particle swarm optimization (PSO) algorithm is a new population based search strat- egy, which has exhibited good performance on well-known numerical test problems. How- ever, on strongly multi-modal test problems the PSO tends to suffer from premature convergence. This is due to a decrease of diversity in search space that leads to a to- tal implosion and ultimately fitness stagnation of the swarm. An accepted hypothesis is that maintenance of high diversity is crucial for preventing premature convergence in multi-modal optimization. We introduce the attractive and repulsive PSO (ARPSO) in trying to overcome the problem of premature convergence. It uses a diversity measure to control the swarm. The result is an algorithm that alternates between phases of attraction and repulsion. The performance of the ARPSO is compared to a basic PSO (bPSO) and a genetic algorithm (GA). The results show that the ARPSO prevents premature convergence to a high degree, but still keeps a rapid convergence like the basic PSO. Thus, it clearly outperforms the basic PSO as well as the implemented GA in multi-modal optimization. Keywords Particle Swarm Optimization, Diversity-Guided Search 1 Introduction The PSO model is a new population based optimization strategy introduced by J. Kennedy et al. in 1995 (Kennedy95). It has already shown to be comparable in performance with tra- ditional optimization algorithms such as simulated annealing (SA) and the genetic algorithm (GA) (Angeline98; Eberhart98; Krink01; Vesterstrom01). A major problem with evolutionary algorithms (EAs) in multi-modal optimization is premature convergence (PC), which results in great performance loss and sub-optimal so- lutions. As far as GAs are concerned, the main reason for premature convergence is a too high selection pressure or a too high gene flow between population individuals. With PSOs the fast information flow between particles seems to be the reason for clustering of particles. Diversity declines rapidly, leaving the PSO algorithm with great difficulties of escaping local optima. Consequently, the clustering leads to low diversity with a fitness stagnation as an overall result. The problem with premature convergence will always persist, since we obviously must check the whole search-space in order to ensure that a result is not sub-optimal. In spite of this fact, and although the goals of maintaining high diversity and obtaining fast convergence EVALife, Department of Computer Science, University of Aarhus, Bldg. 540, Ny Munkegade, DK-8000 Aarhus C, Denmark. This report is online at www.evalife.dk
Original languageEnglish
JournalEVALife Technical Report
Publication statusPublished - 2002

    Research areas

  • Particle Swarm Optimization, Diversity-Guided Search

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