Novelty-driven particle swarm optimization

摘要: Particle Swarm Optimization (PSO) is a well-known population-based optimization algorithm. Most often it is applied to optimize objective-based fitness functions that reward progress towards a desired objective or behavior. As a result, search increasingly focuses on higher-fitness areas. However, in problems with many local optima, such focus often leads to premature convergence that precludes reaching the intended objective. To remedy this problem in certain types of domains, this paper introduces Novelty-driven Particle Swarm Optimization (NdPSO), which is motivated by the novelty search algorithm in evolutionary computation. In this method particles are driven only towards instances significantly different from those found before. By ignoring the objective this way, NdPSO can circumvent the problem of deceptive local optima. Because novelty search has previously shown potential for solving tasks …