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Call for Papers on Particle Swarm Optimization for a Special Issue of the IEEE Transactions on Evolutionary Computation This special issue will be devoted to exploring particle swarm optimization theory, paradigms, and implementations. Particle swarm optimization is a relatively new evolutionary algorithm. PSO is similar to other evolutionary algorithms (EAs) in that the system is initialized with a population of random solutions. It is unlike other EAs, however, in that each potential solution is also assigned a randomized velocity, and the potential solutions, called particles, are then "flown" through the problem space. Each particle keeps track of its coordinates in the problem space which are associated with the best solution (fitness) it has achieved so far. (The fitness value is also stored.) This value is called pbest. Another "best" value that is tracked by the global version of the particle swarm optimizer is the overall best value, and its location, obtained so far by any particle in the population. This location is called gbest. The particle swarm optimization concept consists of, at each time step, changing the velocity of (accelerating) each particle toward its pbest and gbest locations (global version of PSO). Acceleration is weighted by a random term, with separate random numbers being generated for acceleration toward pbest and gbest locations. There is also a local version of PSO in which, in addition to pbest, each particle keeps track of the best solution, called lbest, attained within a local topological neighborhood of particles. One of the reasons that particle swarm optimization is attractive is that there are few parameters to adjust. One version, with slight variations, works well in a wide variety of applications. Particle swarm optimization has been used for approaches that can be used across a wide range of applications, as well as for specific applications focused on a specific requirement. The main objective of this special issue is to assemble a collection of high-quality contributions that reflect the latest advances in the emerging field of particle swarm optimization. Original contributions are encouraged in, but are not limited to, the following areas: Particle swarm algorithms based on biological/social principles Self-organizing (emergent) properties of particle swarms Performance benchmarking of particle swarm optimization algorithms Applications of particle swarm optimization Hybrid algorithms utilizing neural networks, fuzzy systems, etc. Analyses of convergence of the particle swarm algorithm The deadline for submitting a full paper is July 10, 2002. Electronic submission is preferred. Send all submissions to one of the guest editors either through email or by post. Information on this special issue is available at the Internet address: http://www.engr.iupui.edu/~eberhart/IEEE-TEC-PSO.html. Guest Editors: Russ Eberhart Electrical and Computer Engineering Dept. 723 West Michigan, SL-160 Indianapolis, IN 46202-5132, USA [log in to unmask] Yuhui Shi EDS Embedded Systems Group 1401 E. Hoffer Street Kokomo, IN 46902, USA [log in to unmask]