A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
Recent approaches to global optimization problems through Particle Swarm Optimization
Natural Computing: an international journal
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Learning probability distributions in continuous evolutionary algorithms– a comparative review
Natural Computing: an international journal
Where are the hard knapsack problems?
Computers and Operations Research
The equation for response to selection and its use for prediction
Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Proceedings of the 11th Annual conference on Genetic and 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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The philosophy behind the original PSO is to learn from individual's own experience and best individual experience in the whole swarm. Estimation of distribution algorithms sample new solutions from a probability model which characterizes the distribution of promising solutions in the search space at each generation. In this paper, a novel discrete particle swarm optimization algorithm based on estimation of distribution is proposed for combinatorial optimization problems. The proposed algorithm combines the global statistical information collected from local best solution information of all particles and the global best solution information found so far in the whole swarm. To demonstrate its performance, experiments are carried out on the knapsack problem, which is a well-known combinatorial optimization problem. The results show that the proposed algorithm has superior performance to other discrete particle swarm algorithms as well as having less parameters.