Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
A Hybrid Particle Swarm Optimization for Feed-Forward Neural Network Training
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Expert Systems with Applications: An International Journal
Parameter identification of chaotic dynamic systems through an improved particle swarm optimization
Expert Systems with Applications: An International Journal
Adaptive particle swarm optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Face and iris localization using templates designed by particle swarm optimization
Pattern Recognition Letters
A modified particle swarm optimizer using an adaptive dynamic weight scheme
ICDHM'07 Proceedings of the 1st international conference on Digital human modeling
Parameter estimation of bilinear systems based on an adaptive particle swarm optimization
Engineering Applications of Artificial Intelligence
A note on the learning automata based algorithms for adaptive parameter selection in PSO
Applied Soft Computing
Parameter estimation of chaotic systems by a nonlinear time-varying evolution PSO method
Artificial Life and Robotics
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
Expert Systems with Applications: An International Journal
A novel particle swarm optimization algorithm with adaptive inertia weight
Applied Soft Computing
A parallel network clustering of electric loads based PSO
ACOS'06 Proceedings of the 5th WSEAS international conference on Applied computer science
Intelligent identification and control using improved fuzzy particle swarm optimization
Expert Systems with Applications: An International Journal
A non-deterministic adaptive inertia weight in PSO
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Particle swarm optimization for determining fuzzy measures from data
Information Sciences: an International Journal
Metaheuristic optimization: algorithm analysis and open problems
SEA'11 Proceedings of the 10th international conference on Experimental algorithms
Handling boundary constraints for particle swarm optimization in high-dimensional search space
Information Sciences: an International Journal
An adaptive tribe-particle swarm optimization
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
An improved particle swarm optimization with an adaptive updating mechanism
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
Virus-Evolutionary particle swarm optimization algorithm
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
A novel particle swarm optimizer using optimal foraging theory
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
An adaptive staged PSO based on particles' search capabilities
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part I
Hybrid heuristic-waterfilling game theory approach in MC-CDMA resource allocation
Applied Soft Computing
Fault diagnosis and optimization for agent based on the d-s evidence theory
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part II
A multi-swarm PSO using charged particles in a partitioned search space for continuous optimization
Computational Optimization and Applications
Engineering Applications of Artificial Intelligence
The self-adaptive comprehensive learning particle swarm optimizer
ANTS'12 Proceedings of the 8th international conference on Swarm Intelligence
Acoustic sensor network node self-localization based on adaptive particle swarm optimization
AICI'12 Proceedings of the 4th international conference on Artificial Intelligence and Computational Intelligence
International Journal of Organizational and Collective Intelligence
Time-Varying mutation in particle swarm optimization
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
A model-independent Particle Swarm Optimisation software for model calibration
Environmental Modelling & Software
International Journal of Wireless and Mobile Computing
Expert Systems with Applications: An International Journal
Implications of a Reserve Price in an Agent-Based Common-Value Auction
Computational Economics
An improved quantum-behaved particle swarm optimization algorithm
Applied Intelligence
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The particle swarm optimization (PSO) is a relatively new generation of combinatorial metaheuristic algorithms which is based on a metaphor of social interaction, namely bird flocking or fish schooling. Although the algorithm has shown some important advances by providing high speed of convergence in specific problems it has also been reported that the algorithm has a tendency to get stuck in a near optimal solution and may find it difficult to improve solution accuracy by fine tuning. The present paper proposes a new variation of PSO model where we propose a new method of introducing nonlinear variation of inertia weight along with a particle's old velocity to improve the speed of convergence as well as fine tune the search in the multidimensional space. The paper also presents a new method of determining and setting a complete set of free parameters for any given problem, saving the user from a tedious trial and error based approach to determine them for each specific problem. The performance of the proposed PSO model, along with the fixed set of free parameters, is amply demonstrated by applying it for several benchmark problems and comparing it with several competing popular PSO and non-PSO combinatorial metaheuristic algorithms.