Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Swarm intelligence
A species conserving genetic algorithm for multimodal function optimization
Evolutionary Computation
An Investigation of Niche and Species Formation in Genetic Function Optimization
Proceedings of the 3rd International Conference on Genetic Algorithms
Finding Multimodal Solutions Using Restricted Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Efficient differential evolution using speciation for multimodal function optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
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
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Informative performance metrics for dynamic optimisation problems
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A multimodal particle swarm optimizer based on fitness Euclidean-distance ratio
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Particle swarm optimization for multimodal functions: a clustering approach
Journal of Artificial Evolution and Applications - Particle Swarms: The Second Decade
Particle Swarm Optimization with Variable Population Size
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
The Explicit Exploration Information Exchange Mechanism for Niche Technique
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
A Weighted Local Sharing Technique for Multimodal Optimisation
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Scalability of the vector-based particle swarm optimizer
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A Particle Swarm Optimization Method for Multimodal Optimization Based on Electrostatic Interaction
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
Niching without niching parameters: particle swarm optimization using a ring topology
IEEE Transactions on Evolutionary Computation
A novel particle swarm niching technique based on extensive vector operations
Natural Computing: an international journal
Modified particle swarm optimization for a multimodal mixed-variable laser peening process
Structural and Multidisciplinary Optimization
IEEE Transactions on Evolutionary Computation
MO-TRIBES, an adaptive multiobjective particle swarm optimization algorithm
Computational Optimization and Applications
Information Sciences: an International Journal
A dynamic archive based niching particle swarm optimizer using a small population size
ACSC '11 Proceedings of the Thirty-Fourth Australasian Computer Science Conference - Volume 113
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Niching techniques play an important role in evolutionary algorithms. Existing niching methods often require user-specified parameters, limiting their usefulness. This paper proposes a niching method for Particle Swarm Optimisation (PSO) where population statistics are used to adaptively determine the niching parameters during a run. The proposed niching method is compared to another niching based PSO, SPSO. Our results show that the proposed adaptive niching PSO can solve difficult multimodal functions more reliably and with fewer evaluations.