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
The landscape adaptive particle swarm optimizer
Applied Soft Computing
A distributed PSO-SVM hybrid system with feature selection and parameter optimization
Applied Soft Computing
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Expert Systems with Applications: An International Journal
Particle swarm optimization algorithm based on dynamic memory strategy
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
A perturbed particle swarm algorithm for numerical optimization
Applied Soft Computing
Particle Swarm Optimization Algorithm for Agent-Based Artificial Markets
Computational Economics
Finite cut-based approximation of fuzzy sets and its evolutionary optimization
Fuzzy Sets and Systems
Expert Systems with Applications: An International Journal
Solving nonlinear optimal control problems using a hybrid IPSO-SQP algorithm
Engineering Applications of Artificial 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.