Multiple Shadows Layered Cooperative Velocity Updating Particle Swarm Optimization

In real-time high dimensions optimization problem, how to quickly find the optimal solution and give timely response or decisive adjustment is very important. Inspired by space projection behavior, this paper suggests a new PSO variant, Multiple-shadows Layered Cooperative Velocity Updating Particle Swarm Optimization (ML-CVUPSO) that involves visual instructive projections among multiple shadows. According to several different views, the original problem can be divided into different relevant characteristic sub-problems after feature extraction. The ML-CVUPSO provides a flexible and feasible decomposed mechanism to simplify the high dimensions problem into a series of tractable sub-problems. The proposed variant is examined on several widely used benchmark functions, and the experimental results show that the proposed ML-CVUPSO algorithm improves the existing performance of other algorithms when dealing with the high dimension and multimodal problems.