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
Department, Program, or Center
Microelectronic Engineering (KGCOE)
Tillett, Jason; Rao, T.; Sahin, Ferat; and Rao, Raghuveer, "Darwinian particle swarm optimization" (2005). Accessed from
RIT – Main Campus