Triggered Memory-Based Swarm Optimization in Dynamic Environments

In recent years, there has been an increasing concern from the evolutionary computation community on dynamic optimization problems since many real-world optimization problems are time-varying. In this paper, a triggered memory scheme is introduced into the particle swarm optimization to deal with dynamic environments. The triggered memory scheme enhances traditional memory scheme with a triggered memory generator. Experimental study over a benchmark dynamic problem shows that the triggered memory-based particle swarm optimization algorithm has stronger robustness and adaptability than traditional particle swarm optimization algorithms, both with and without traditional memory scheme, for dynamic optimization problems.

• Type: Conference
• Year: 2007
• Where: EVOW
• Authors: Hongfeng Wang, Dingwei Wang, Shengxiang Yang
• Keywords: Artificial Intelligence, Dynamic Optimization Problems, EVOW 2007, Particle Swarm Optimization, Triggered Memory Scheme