A novel particle swarm optimization based on hybrid-learning model

Abstract

<abstract><p>The convergence speed and the diversity of the population plays a critical role in the performance of particle swarm optimization (PSO). In order to balance the trade-off between exploration and exploitation, a novel particle swarm optimization based on the hybrid learning model (PSO-HLM) is proposed. In the early iteration stage, PSO-HLM updates the velocity of the particle based on the hybrid learning model, which can improve the convergence speed. At the end of the iteration, PSO-HLM employs a multi-pools fusion strategy to mutate the newly generated particles, which can expand the population diversity, thus avoid PSO-HLM falling into a local optima. In order to understand the strengths and weaknesses of PSO-HLM, several experiments are carried out on 30 benchmark functions. Experimental results show that the performance of PSO-HLM is better than other the-state-of-the-art algorithms.</p></abstract>

A Novel Hybrid Firefly Algorithm with Double-Level Learning Strategy

“…NDFA improves the accuracy of water demand prediction through three different estimation models (linear, exponential, and mixed) combining historical water consumption and local economic structure. Due to the ability of FA to effectively and quickly solve constrained optimization problems, a novel particle swarm optimization based on a hybrid-learning model was proposed by Wang et al [13]. It is used to solve global optimization problems in the continuous domain and has achieved excellent results.…”