Simulated annealing: theory and applications
Simulated annealing: theory and applications
Computational intelligence PC tools
Computational intelligence PC tools
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
Journal of Global Optimization
A computationally efficient evolutionary algorithm for real-parameter optimization
Evolutionary Computation
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Comparison between Genetic Algorithms and Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Local Parameters Particle Swarm Optimization
HIS '06 Proceedings of the Sixth International Conference on Hybrid Intelligent Systems
MOPSO: a proposal for multiple objective particle swarm optimization
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
Toward a theory of evolution strategies: Self-adaptation
Evolutionary Computation
Empirical comparison of MOPSO methods: guide selection and diversity preservation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Particle Swarm Optimization and Intelligence: Advances and Applications
Particle Swarm Optimization and Intelligence: Advances and Applications
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
The fully informed particle swarm: simpler, maybe better
IEEE Transactions on Evolutionary Computation
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
Multiswarms, exclusion, and anti-convergence in dynamic environments
IEEE Transactions on Evolutionary Computation
A hybrid of genetic algorithm and particle swarm optimization for recurrent network design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Improving differential evolution through a unified approach
Journal of Global Optimization
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Evolutionary Algorithms (EAs) are emerging as competitive and reliable techniques for several optimization tasks. Juxtapositioning their higher-level and implicit correspondence; it is provocative to query if one optimization algorithm can benefit from another by studying underlying similarities and dissimilarities. This paper establishes a clear and fundamental algorithmic linking between particle swarm optimization (PSO) algorithm and genetic algorithms (GAs). Specifically, we select the task of solving unimodal optimization problems, and demonstrate that key algorithmic features of an effective Generalized Generation Gap based Genetic Algorithm can be introduced into the PSO by leveraging this algorithmic linking while significantly enhance the PSO's performance. However, the goal of this paper is not to solve unimodal problems, neither is to demonstrate that the modified PSO algorithm resembles a GA, but to highlight the concept of algorithmic linking in an attempt towards designing efficient optimization algorithms. We intend to emphasize that the evolutionary and other optimization researchers should direct more efforts in establishing equivalence between different genetic, evolutionary and other nature-inspired or non-traditional algorithms. In addition to achieving performance gains, such an exercise shall deepen the understanding and scope of various operators from different paradigms in Evolutionary Computation (EC) and other optimization methods.