Swarm intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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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
EPSO - best-of-two-worlds meta-heuristic applied to power system problems
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Particle swarm optimization: an introduction and its recent developments
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
A review of particle swarm optimization. Part I: background and development
Natural Computing: an international journal
Natural Computing: an international journal
Dynamics and stability of the sampling distribution of particle swarm optimisers via moment analysis
Journal of Artificial Evolution and Applications - Particle Swarms: The Second Decade
A novel hybrid immune algorithm for global optimization in design and manufacturing
Robotics and Computer-Integrated Manufacturing
A survey of particle swarm optimization applications in electric power systems
IEEE Transactions on Evolutionary Computation
JADE: adaptive differential evolution with optional external archive
IEEE Transactions on Evolutionary Computation
Scale-free fully informed particle swarm optimization algorithm
Information Sciences: an International Journal
Information Sciences: an International Journal
No free lunch theorems for 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
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
Differential Evolution: A Survey of the State-of-the-Art
IEEE Transactions on Evolutionary Computation
A method of a spread-spectrum radar polyphase code design
IEEE Journal on Selected Areas in Communications
Engineering Applications of Artificial Intelligence
Compact Particle Swarm Optimization
Information Sciences: an International Journal
IGA-based point cloud fitting using B-spline surfaces for reverse engineering
Information Sciences: an International Journal
Two-layer particle swarm optimization with intelligent division of labor
Engineering Applications of Artificial Intelligence
Particle swarm optimization with increasing topology connectivity
Engineering Applications of Artificial Intelligence
Quadratic interpolation based orthogonal learning particle swarm optimization algorithm
Natural Computing: an international journal
Information Sciences: an International Journal
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The concept of particle swarms originated from the simulation of the social behavior commonly observed in animal kingdom and evolved into a very simple but efficient technique for optimization in recent past. Since its advent in 1995, the Particle Swarm Optimization (PSO) algorithm has attracted the attention of a lot of researchers all over the world resulting into a huge number of variants of the basic algorithm as well as many parameter selection/control strategies. PSO relies on the learning strategy of the individuals to guide its search direction. Traditionally, each particle utilizes its historical best experience as well as the global best experience of the whole swarm through linear summation. The Comprehensive Learning PSO (CLPSO) was proposed as a powerful variant of PSO that enhances the diversity of the population by encouraging each particle to learn from different particles on different dimensions, in the metaphor that the best particle, despite having the highest fitness, does not always offer a better value in every dimension. This paper presents a variant of single-objective PSO called Dynamic Neighborhood Learning Particle Swarm Optimizer (DNLPSO), which uses learning strategy whereby all other particles' historical best information is used to update a particle's velocity as in CLPSO. But in contrast to CLPSO, in DNLPSO, the exemplar particle is selected from a neighborhood. This strategy enables the learner particle to learn from the historical information of its neighborhood or sometimes from that of its own. Moreover, the neighborhoods are made dynamic in nature i.e. they are reformed after certain intervals. This helps the diversity of the swarm to be preserved in order to discourage premature convergence. Experiments were conducted on 16 numerical benchmarks in 10, 30 and 50 dimensions, a set of five constrained benchmarks and also on a practical engineering optimization problem concerning the spread-spectrum radar poly-phase code design. The results demonstrate very competitive performance of DNLPSO while locating the global optimum on complicated and multimodal fitness landscapes when compared with five other recent variants of PSO.