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EVOW
2007
Springer

Triggered Memory-Based Swarm Optimization in Dynamic Environments

9 years 3 months ago
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.
Hongfeng Wang, Dingwei Wang, Shengxiang Yang
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where EVOW
Authors Hongfeng Wang, Dingwei Wang, Shengxiang Yang
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