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.
Citation : Wang, H., Wang, D. and Yang, S. (2007) Triggered memory-based swarm optimization in dynamic environments. EvoWorkshops 2007: Applications of Evolutionary Computing, Lecture Notes in Computer Science, 4448, pp. 637-646
Research Group : Centre for Computational Intelligence
Research Institute : Institute of Artificial Intelligence (IAI)
Peer Reviewed : Yes