Fuzzy Cognitive Maps Learning Using Particle Swarm Optimization
Journal of Intelligent Information Systems
Swarm bias-variance analysis of an evolutionary neural network classifier
AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
An efficient algorithm for large scale global optimization of continuous functions
Journal of Computational and Applied Mathematics
Particle swarm optimization based on dynamic niche technology with applications to conceptual design
Advances in Engineering Software
EURASIP Journal on Applied Signal Processing
A fuzzy adaptive turbulent particle swarm optimisation
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Journal of Global Optimization
A software tool for teaching of particle swarm optimization fundamentals
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Fuzzy Evolutionary Probabilistic Neural Networks
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Introducing dynamic diversity into a discrete particle swarm optimization
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Identifying core sets of discriminatory features using particle swarm optimization
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Particle swarm optimization with preference order ranking for multi-objective optimization
Information Sciences: an International Journal
Synchronous parallelization of Particle Swarm Optimization with digital pheromones
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Glowworm swarm optimisation: a new method for optimising multi-modal functions
International Journal of Computational Intelligence Studies
Cooperative micro-particle swarm optimization
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Problem difficulty analysis for particle swarm optimization: deception and modality
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
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Cryptanalysis of four-rounded DES using binary particleswarm optimization
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This paper presents approaches for effectively computing all global minimizers of an objective function. The approaches include transformations of the objective function through the recently proposed deflection and stretching techniques, as well as a repulsion source at each detected minimizer. The aforementioned techniques are incorporated in the context of the particle swarm optimization (PSO) method, resulting in an efficient algorithm which has the ability to avoid previously detected solutions and, thus, detect all global minimizers of a function. Experimental results on benchmark problems originating from the fields of global optimization, dynamical systems, and game theory, are reported, and conclusions are derived.