Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Swarm intelligence
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization
Evolutionary Computation
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
Stochastic ranking for constrained evolutionary optimization
IEEE Transactions on Evolutionary Computation
A simple multimembered evolution strategy to solve constrained optimization problems
IEEE Transactions on Evolutionary Computation
Solving nonlinearly constrained global optimization problem via an auxiliary function method
Journal of Computational and Applied Mathematics
International Journal of Bio-Inspired Computation
Computers & Mathematics with Applications
A mixed-discrete Particle Swarm Optimization algorithm with explicit diversity-preservation
Structural and Multidisciplinary Optimization
A simple adaptive algorithm for numerical optimization
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Computational Intelligence - Volume Part II
Information Sciences: an International Journal
An adaptive single-point algorithm for global numerical optimization
Expert Systems with Applications: An International Journal
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This paper presents a particle swarm optimizer for solving constrained optimization problems which adopts a very small population size (five particles). The proposed approach uses a reinitialization process for preserving diversity, and does not use a penalty function nor it requires feasible solutions in the initial population. The leader selection scheme adopted is based on the distance of a solution to the feasible region. In addition, a mutation operator is incorporated to improve the exploratory capabilities of the algorithm. The approach is tested with a well-know benchmark commonly adopted to validate constrainthandling approaches for evolutionary algorithms. The results show that the proposed algorithm is competitive with respect to state-of-the-art constraint-handling techniques. The number of fitness function evaluations that the proposed approach requires is almost the same (or lower) than the number required by the techniques of the state-of-the-art in the area.