Niching methods for genetic algorithms
Niching methods for genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
A species conserving genetic algorithm for multimodal function optimization
Evolutionary Computation
The nature of niching: genetic algorithms and the evolution of optimal, cooperative populations
The nature of niching: genetic algorithms and the evolution of optimal, cooperative populations
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Multi-Species Particle Swarm Optimizer for Multimodal Function Optimization
IEICE - Transactions on Information and Systems
A hybrid genetic algorithm and particle swarm optimization for multimodal functions
Applied Soft Computing
A study of particle swarm optimization particle trajectories
Information Sciences: an International Journal
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
On the computation of all global minimizers through particle swarm optimization
IEEE Transactions on Evolutionary Computation
Locating and tracking multiple dynamic optima by a particle swarm model using speciation
IEEE Transactions on Evolutionary Computation
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
High dimensional problem based on elite-grouped adaptive particle swarm optimization
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
Computational Intelligence and Neuroscience
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This paper presents a hybrid niching algorithm based on the PSO to deal with multimodal function optimization problems. First, we propose to evolve directly both the particle population and memory population (archive population), called the P&A pattern, to enhance the efficiency of the PSO for solving multimodal optimization functions, and investigate illustratively the niching capability of the PSO and the PSO"P"&"A. It is found that the global version PSO is disable, but the local version PSO"P"&"A is able, to niche multiple species for locating multiple optima. Second, the recombination-replacement crowding strategy that works on the archive population is introduced to improve the exploration capability, and the hybrid niching PSO"P"&"A (HN-PSO"P"&"A) is developed. Finally, experiments are carried out on multimodal functions for testing the niching efficiency and scalability of the proposed method, and it is verified that the proposed method has a sub-quadratic scalability with dimension in terms of fitness function evaluations on specific MMFO problems.