Journal of Global Optimization
Journal of Global Optimization
Particle swarm optimization with adaptive population size and its application
Applied Soft Computing
Efficient population utilization strategy for particle swarm optimizer
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Improvement strategies for the F-Race algorithm: sampling design and iterative refinement
HM'07 Proceedings of the 4th international conference on Hybrid metaheuristics
An incremental ant colony algorithm with local search for continuous optimization
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on scalability of evolutionary algorithms and other metaheuristics for large-scale continuous optimization problems
Incremental Social Learning in Particle Swarms
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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We modify an artificial bee colony algorithm as follows: we make the population size grow over time and apply local search on strategically selected solutions. The modified algorithm obtains very good results on a set of large-scale continuous optimization benchmark problems. This is not the first time we see that the two aforementioned modifications make an initially non-competitive algorithm obtain state-of-the-art results. In previous work, we have shown that the same modifications substantially improve the performance of particle swarm optimization and ant colony optimization algorithms. Altogether, these results suggest that population growth coupled with local search help obtain high-quality results.