ACM Transactions on Mathematical Software (TOMS)
Global optimization
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Computers and Operations Research
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Journal of Global Optimization
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Journal of Global Optimization
MAMECTIS'08 Proceedings of the 10th WSEAS international conference on Mathematical methods, computational techniques and intelligent systems
An evolutionary method for complex-process optimization
Computers and Operations Research
Random search optimization approach for highly multi-modal nonlinear problems
Advances in Engineering Software
Quasi-random initial population for genetic algorithms
Computers & Mathematics with Applications
Efficient hybrid methods for global continuous optimization based on simulated annealing
Computers and Operations Research
Shrinking neighborhood evolution: a novel stochastic algorithm for numerical optimization
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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International Journal of Bio-Inspired Computation
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MIWAI'11 Proceedings of the 5th international conference on Multi-Disciplinary Trends in Artificial Intelligence
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This paper proposes a novel metaheuristics approach to find the global optimum of continuous global optimization problems with box constraints. This approach combines the characteristics of modern metaheuristics such as scatter search (SS), genetic algorithms (GAs), and tabu search (TS) and named as hybrid scatter genetic tabu (HSGT) search. The development of the HSGT search, parameter settings, experimentation, and efficiency of the HSGT search are discussed. The HSGT has been tested against a simulated annealing algorithm, a GA under the name GENOCOP, and a modified version of a hybrid scatter genetic (HSG) search by using 19 well known test functions. Applications to Neural Network training are also examined. From the computational results, the HSGT search proved to be quite effective in identifying the global optimum solution which makes the HSGT search a promising approach to solve the general nonlinear optimization problem.