A Diversity-Guided Particle Swarm Optimizer - the ARPSO

The particle swarm optimization (PSO) algorithm is a new population based search strategy, which has exhibited good performance on well-known numerical test problems. However, 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 total 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.

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