A new collaborative evolutionary-swarm optimization technique

A new hybrid approach to optimization in dynamical environments called Collaborative Evolutionary-Swarm Optimization (CESO) is presented. CESO tracks moving optima in a dynamical environment by combining the search abilities of an evolutionary algorithm for multimodal optimization and a particle swarm optimization algorithm. A collaborative mechanism between the two methods is proposed by which the diversity provided by the multimodal technique is transmitted to the particle swarm in order to prevent its premature convergence. Numerical experiments indicate CESO as an efficient method compared with other evolutionary approaches. Categories and Subject Descriptors I.2.8 [Artificial Intelligence]: Problem Solving, Control

• Type: Conference
• Year: 2007
• Where: GECCO
• Authors: Rodica Ioana Lung, D. Dumitrescu
• Keywords: Dynamical Environment, GECCO 2007, Particle Swarm, Particle Swarm Optimization