Two-layer particle swarm optimization with intelligent division of labor

• A PSO variant, i.e. TLPSO-IDL is proposed for current and memory swarm evolution.
• New learning strategy is proposed in the current swarm evolution.
• An IDL module is proposed to evolve memory swarm through adaptive task allocation.
• EBF module is developed to alleviate the premature convergence issue.
• TLPSO-IDL eclipses its peers in terms of searching accuracy and convergence speed.

Early studies in particle swarm optimization (PSO) algorithm reveal that the social and cognitive components of swarm, i.e. memory swarm, tend to distribute around the problem's optima. Motivated by these findings, we propose a two-layer PSO with intelligent division of labor (TLPSO-IDL) that aims to improve the search capabilities of PSO through the evolution memory swarm. The evolution in TLPSO-IDL is performed sequentially on both the current swarm and the memory swarm. A new learning mechanism is proposed in the former to enhance the swarm's exploration capability, whilst an intelligent division of labor (IDL) module is developed in the latter to adaptively divide the swarm into the exploration and exploitation sections. The proposed TLPSO-IDOL algorithm is thoroughly compared with nine well-establish PSO variants on 16 unimodal and multimodal benchmark problems with or without rotation property. Simulation results indicate that the searching capabilities and the convergence speed of TLPSO-IDL are superior to the state-of-art PSO variants.

The graphical illustration of the proposed two-layer particle swarm optimization with intelligent division of labor (TLPSO-IDL), consisting of the current swarm evolution, elitist-based perturbation (EBP) module, and memory swarm evolution.Figure optionsDownload as PowerPoint slide