Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Swarm intelligence
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Ant Colony Optimization
Learning probability distributions in continuous evolutionary algorithms– a comparative review
Natural Computing: an international journal
Exposing origin-seeking bias in PSO
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
A hierarchical particle swarm optimizer and its adaptive variant
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Preventing premature convergence in a PSO and EDA hybrid
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Expert Systems with Applications: An International Journal
Estimation of particle swarm distribution algorithms: Combining the benefits of PSO and EDAs
Information Sciences: an International Journal
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In this paper we present an estimation of distribution particle swarm optimization algorithm that borrows ideas from recent developments in ant colony optimization which can be considered an estimation of distribution algorithm. In the classical particle swarm optimization algorithm, particles exploit their individual memory to explore the search space. However, the swarm as a whole has no means to exploit its collective memory (represented by the array of previous best positions or pbests) to guide its search. This causes a re-exploration of already known bad regions of the search space, wasting costly function evaluations. In our approach, we use the swarm’s collective memory to probabilistically guide the particles’ movement towards the estimated promising regions in the search space. Our experiments show that this approach is able to find similar or better solutions than the canonical particle swarm optimizer with fewer function evaluations.