A filled function method for finding a global minimizer of a function of several variables
Mathematical Programming: Series A and B
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Tabu Search
Route Optimization for Mobile IP
Cluster Computing
Journal of Global Optimization
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Ant Colony Optimization
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Differential Evolution: In Search of Solutions (Springer Optimization and Its Applications)
Differential Evolution: In Search of Solutions (Springer Optimization and Its Applications)
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
Dissipative particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Hybridisation of particle swarm optimization and fast evolutionary programming
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Evolutionary programming made faster
IEEE Transactions on Evolutionary Computation
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Evolutionary programming using mutations based on the Levy probability distribution
IEEE Transactions on Evolutionary Computation
On the computation of all global minimizers through 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
IEEE Transactions on Evolutionary Computation
Hybrid particle swarm optimisation based on history information sharing
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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Evolutionary Algorithms (EAs) and Swarm Intelligence (SI) are widely used to tackle black-box global optimization problems when no prior knowledge is available. In order to increase search diversity and avoid stagnation in local optima, the mutation operator was introduced and has been extensively studied in EAs and SI-based algorithms. However, the performance after introducing mutation can be affected in many aspects and the parameters used to perform mutations are very hard to determine. For the purpose of developing efficient mutation operators, this article proposes a unified tabu and mutation framework with parameter adaptations in the context of the Particle Swarm Optimizer (PSO). The proposed framework is a significant extension of our preliminary work [Wang et al. 2007]. Empirical studies on 25 benchmark functions indicate that under the proposed framework: (1) excellent performance can be achieved even with a small number of mutations; (2) the derived algorithm consistently performs well on diverse types of problems and overall performance even surpasses the state-of-the-art PSO variants and representative mutation-based EAs; and (3) fast convergence rates can be preserved despite the use of a long jump mutation operator (the Cauchy mutation).