🤖 AI Summary
This work proposes a novel approach that integrates deep neural networks into the particle swarm optimization (PSO) framework to enhance its capability in dynamic environments. Traditional PSO algorithms struggle to effectively track moving global optima due to their limited adaptability to environmental changes. By embedding a deep neural network that learns the underlying dynamics of the environment, the proposed method enables particles to predict and follow the shifting optimum more accurately. The architecture supports both centralized and distributed implementations and significantly reduces reliance on large swarm sizes. Experimental results demonstrate that, with fewer particles, the approach achieves substantially lower cumulative tracking error compared to state-of-the-art PSO variants, thereby markedly improving the accuracy of global optimum tracking in dynamic optimization scenarios.
📝 Abstract
We propose novel particle swarm optimization (PSO) variants incorporated with deep neural networks (DNNs) for particles to pursue globally optimal positions in dynamic environments. PSO is a heuristic approach for solving complex optimization problems. However, canonical PSO and its variants struggle to adapt efficiently to dynamic environments, in which the global optimum moves over time, and to track them accurately. Many PSO algorithms improve convergence by increasing the swarm size beyond potential optima, which are global/local optima but are not identified until they are discovered. Additionally, in dynamic environments, several methods use multiple sub-population and re-diversification mechanisms to address outdated memory and local optima entrapment. To track the global optimum in dynamic environments with smaller swarm sizes, the DNNs in our methods determine particle movement by learning environmental characteristics and adapting dynamics to pursue moving optimal positions. This enables particles to adapt to environmental changes and predict the moving optima. We propose two variants: a swarm with a centralized network and distributed networks for all particles. Our experimental results show that both variants can track moving potential optima with lower cumulative tracking error than those of several recent PSO-based algorithms, with fewer particles than potential optima.