Particle Swarm Optimization (PSO), an evolutionary algorithm for optimization is extended to determine if natural selection, or survival-of-the- fittest, can enhance the ability of the PSO algorithm to escape from local optima. To simulate selection, many simultaneous, parallel PSO algorithms, each one a swarm, operate on a test problem. Simple rules are developed to implement selection. The ability of this so-called Darwinian PSO to escape local optima is evaluated by comparing a single swarm and a similar set of swarms, differing primarily in the absence of the selection mechanism, operating on the same test problem. The selection process is shown to be capable of evolving the best type of particle velocity control, which is a problem specific design choice of the PSO algorithm.

Date of creation, presentation, or exhibit



Proceedings of the 2nd Indian International Conference on Artificial Intelligence (IICAI-05) (2005) 1474-1487 Tillett, Rao, Sahin, Rao. "Darwinian Particle Swarm Optimization." Proceedings of the 2nd Indian International Conference on Artificial Intelligence. Edited by Prasad, B. 1474-1487 (2005). ISBN: 0-9727412-1-6Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works in February 2014.

Document Type

Conference Paper

Department, Program, or Center

Microelectronic Engineering (KGCOE)


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