Improving particle swarm optimization with differentially perturbed velocity
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Particles with Age for Data Clustering
ICIT '06 Proceedings of the 9th International Conference on Information Technology
Fundamentals of Computational Swarm Intelligence
Fundamentals of Computational Swarm Intelligence
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Multiobjective optimization using dynamic neighborhood particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Don't push me! Collision-avoiding swarms
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Extending particle swarm optimisers with self-organized criticality
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
A review of particle swarm optimization. Part I: background and development
Natural Computing: an international journal
Analysis of the publications on the applications of particle swarm optimisation
Journal of Artificial Evolution and Applications - Regular issue
JADE: adaptive differential evolution with optional external archive
IEEE Transactions on Evolutionary Computation
Data Fitting and Uncertainty: A Practical Introduction to Weighted Least Squares and Beyond
Data Fitting and Uncertainty: A Practical Introduction to Weighted Least Squares and Beyond
Neighborhood topologies in fully informed and best-of-neighborhood particle swarms
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
An orthogonal genetic algorithm with quantization for globalnumerical optimization
IEEE Transactions on Evolutionary Computation
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Guest Editorial Special Issue on Particle Swarm Optimization
IEEE Transactions on Evolutionary Computation
The fully informed particle swarm: simpler, maybe better
IEEE Transactions on Evolutionary Computation
On the computation of all global minimizers through particle swarm optimization
IEEE Transactions on Evolutionary Computation
A Cooperative approach to particle swarm optimization
IEEE Transactions on Evolutionary Computation
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems
IEEE Transactions on Evolutionary Computation
A hierarchical particle swarm optimizer and its adaptive variant
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Particle Swarm Optimization With Recombination and Dynamic Linkage Discovery
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
OPSO: Orthogonal Particle Swarm Optimization and Its Application to Task Assignment Problems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A dynamic neighborhood learning based particle swarm optimizer for global numerical optimization
Information Sciences: an International Journal
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Concept of the particle swarms emerged from a simulation of the collective behavior of social creatures and gradually evolved into a powerful global optimization technique, now well-known as the Particle Swarm Optimization (PSO). PSO is arguably one of the most popular nature-inspired algorithms for real parameter optimization at present. The very basic PSO model does not ensure convergence to an optimal solution and it also suffers from its dependency on external parameters like acceleration parameters and inertia weight. Owing to its comparatively poor efficiency, a multitude of measures has been taken by the researchers to improve the performance of PSO. This paper presents a scheme to modify the very basic framework of PSO by the introduction of a novel dimensional mean based perturbation strategy, a simple aging guideline, and a set of nonlinearly time-varying acceleration coefficients to achieve a better tradeoff between explorative and exploitative tendencies and thus to avoid premature convergence on multimodal fitness landscapes. The aging guideline is used to introduce fresh solutions in the swarm when particles show no further improvement. A systematically rendered comparison between the proposed PSO framework and several other state-of-the-art PSO-variants as well as evolutionary algorithms on a test-suite comprising 16 standard numerical benchmarks and two real world problems indicates that the proposed algorithm can enjoy a statistically superior performance on a wide variety of problems.