Population size reduction for the differential evolution algorithm
Applied Intelligence
Self-adapting differential evolution algorithm with chaos random for global numerical optimization
ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
A new electromagnetism-like algorithm with a population shrinking strategy
ICOSSSE'07 Proceedings of the 6th WSEAS international conference on System science and simulation in engineering
A new electromagnetism-like algorithm with a population shrinking strategy
MACMESE'07 Proceedings of the 9th WSEAS international conference on Mathematical and computational methods in science and engineering
A modification to MOEA/D-DE for multiobjective optimization problems with complicated Pareto sets
Information Sciences: an International Journal
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This paper presents an algorithm for finding a global minimum of a multimodal, multivariate and nondifferentiable function. The algorithm is a modification to the new version of the Price's algorithm given in Brachetti et al. [J. Global Optim. 10, 165---184 (1997)]. Its distinguishing features include: (1) The number-theoretic method is applied to generate the initial population so that the points in the initial population are uniformly scattered, and therefore the algorithm could explore uniformly the region of interest at the initial iteration; (2) The simplified quadratic approximation with the three best points is employed to improve the local search ability and the accuracy of the minimum function value, and to reduce greatly the computational overhead of the algorithm. Two sets of experiments are carried out to illustrate the efficiency of the number-theoretic method and the simplified quadratic model separately. The proposed algorithm has also been compared with the original one by solving a wide set of benchmark problems. Numerical results show that the proposed algorithm requires a smaller number of function evaluations and, in many cases, yields a smaller or more accurate minimum function value. The algorithm can also be used to deal with the medium size optimization problems.