The paper describes a new stochastic heuristic algorithm for global optimization. The new optimization algorithm, called Intelligent-Particle Swarm Optimization (IPSO), offers more intelligence to particles by using concepts such as: group experiences, unpleasant memories (tabu to be avoided), local landscape models based on virtual neighbors, and memetic replication of successful behavior parameters. The new individual complexity is amplified at the group level and consequently generates a more efficient optimization procedure. A simplified version of the IPSO algorithm was implemented and compared with the classical PSO algorithm for a simple test function and for the Loney's solenoid.
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