Affiliation:
1. School of Computer Science and Technology, Qinghai Normal University, Xining 810008, China
2. School of Mathematics and Statistics, Qinghai Normal University, Xining 810008, China
3. Academy of Plateau Science and Sustainability, Xining 810008, China
Abstract
<abstract><p>The trade-off between exploitation and exploration is a dilemma inherent to particle swarm optimization (PSO) algorithms. Therefore, a growing body of PSO variants is devoted to solving the balance between the two. Among them, the method of self-adaptive multi-strategy selection plays a crucial role in improving the performance of PSO algorithms but has yet to be well exploited. In this research, with the aid of the reinforcement learning technique to guide the generation of offspring, a novel self-adaptive multi-strategy selection mechanism is designed, and then a multi-strategy self-learning PSO algorithm based on reinforcement learning (MPSORL) is proposed. First, the fitness value of particles is regarded as a set of states that are divided into several state subsets non-uniformly. Second, the $ \varepsilon $-greedy strategy is employed to select the optimal strategy for each particle. The personal best particle and the global best particle are then updated after executing the strategy. Subsequently, the next state is determined. Thus, the value of the Q-table, as a scheme adopted in self-learning, is reshaped by the reward value, the action and the state in a non-stationary environment. Finally, the proposed algorithm is compared with other state-of-the-art algorithms on two well-known benchmark suites and a real-world problem. Extensive experiments indicate that MPSORL has better performance in terms of accuracy, convergence speed and non-parametric tests in most cases. The multi-strategy selection mechanism presented in the manuscript is effective.</p></abstract>
Publisher
American Institute of Mathematical Sciences (AIMS)
Subject
Applied Mathematics,Computational Mathematics,General Agricultural and Biological Sciences,Modeling and Simulation,General Medicine
Cited by
2 articles.
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