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Aim of this article is to propose Micro-Genetic Simulated Annealing (μGSA) as a hybrid metaheuristics approach to find the global optimum of nonlinear optimisation problems. This approach combines the features of modern metaheuristics such as micro-genetic algorithms (μGAs) and simulated annealing (SA) with the general robustness of parallel exploration and asymptotic convergence, respectively. Therefore, μGSA approach can help in avoiding the premature convergence and can search for better global solution, because of its wide spread applicability, global perspective and inherent parallelism. For the superior performance of the μGSA, the five well-known benchmark test functions that were tested and compared with the two global optimisation approaches: scatter search (SS) and hybrid scatter genetic tabu (HSGT) approach. A practical application to structural sandwich panel is also examined by optimising the weight function. From the simulation results, it has been concluded that the proposed μGSA approach is an effective optimisation tool for solving continuous nonlinear global optimisation problems in suitable computational time frame.
목차
ABSTRACT
1. Introduction
2. Metaheuristics Optimisation Techniques
3. Micro-Genetic Simulated Annealing (μGAs) Approach
4. Benchmark Results
5. Optimum Design of Sandwich Panel
6. Concluding Remarks
Acknowledgements
Appendix: Benchmark Test Functions
References
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UCI(KEPA) : I410-ECN-0101-2009-559-015530917