Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Swarm intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Recent approaches to global optimization problems through Particle Swarm Optimization
Natural Computing: an international journal
Comparison between Genetic Algorithms and Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Intelligent evolutionary algorithms for large parameter optimization problems
IEEE Transactions on Evolutionary Computation
A modified particle swarm optimization for correlated phenomena
Applied Soft Computing
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This study proposes an orthogonal momentum-type particle swarm optimization (PSO) that finds good solutions to global optimization problems using a delta momentum rule to update the flying velocity of particles and incorporating a fractional factorial design (FFD) via several factorial experiments to determine the best position of particles. The novel combination of the momentum-type PSO and FFD is termed as the momentum-type PSO with FFD herein. The momentum-type PSO modifies the velocity-updating equation of the original Kennedy and Eberhart PSO, and the FFD incorporates classical orthogonal arrays into a velocity-updating equation for analyzing the best factor associated with cognitive learning and social learning terms. Twelve widely used large parameter optimization problems were used to evaluate the performance of the proposed PSO with the original PSO, momentum-type PSO, and original PSO with FFD. Experimental results reveal that the proposed momentum-type PSO with an FFD algorithm efficiently solves large parameter optimization problems.