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Commenced in January 2007 Frequency: Monthly Edition: International Publications Count: 31107

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Engineering Optimization Using Two-Stage Differential Evolution
This paper employs a heuristic algorithm to solve engineering problems including truss structure optimization and optimal chiller loading (OCL) problems. Two different type algorithms, real-valued differential evolution (DE) and modified binary differential evolution (MBDE), are successfully integrated and then can obtain better performance in solving engineering problems. In order to demonstrate the performance of the proposed algorithm, this study adopts each one testing case of truss structure optimization and OCL problems to compare the results of other heuristic optimization methods. The result indicates that the proposed algorithm can obtain similar or better solution in comparing with previous studies.


[1] R. Storn and K. Price, “Differential evolution—A simple and efficient heuristic for global optimization over continuous spaces,” Journal of Global Optimization, vol.11, pp.341-359, 1997.
[2] R. L. Becerra and C. A. C. Coello, “Cultured differential evolution for constrained optimization,” Computer Methods in Applied Mechanics and Engineering, vol. 195, pp. 4303–4322, 2006.
[3] C. C. Cheng, T. D. Tsai, D. W. Lin and C. C. Chiu, “Design and Analysis of Shock-Absorbing Structure for Flat Panel Display,” IEEE transactions on advanced packaging, vol.31, no.1, pp. 135-142, 2008.
[4] B. Qian, L. Wang, D. X. Huang and X. Wang, “Scheduling multi-objective job shops using a memetic algorithm based on differential evolution,” International Journal of Advanced Manufacturing Technology, vol. 35, pp. 1014-1027, 2008.
[5] K. Deb and S. Gulati, “Design of truss-structures for minimum weight using genetic algorithms,” Finite Elements in Analysis and Design, vol. 37 pp. 447-465, 2001.
[6] G. C. Luh and C. Y. Lin, “Optimal design of truss structures using ant algorithm,” Structural and Multidisciplinary Optimization, vol. 36, no.4, pp. 365–379, 2008.
[7] P. Hajela and E. Lee, “Genetic algorithms in truss topological optimization,” International journal of solids and structures vol. 32, pp. 3341-3357, 1995.
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[10] M. Schwedler and A. Yates, Multiple-chiller-system design and control, Trane, 2001.
[11] Y. C. Chang. “A novel energy conservation method – optimal chiller loading”, Electric Power Systems Research, vol. 69, pp. 221-226, 2004.
[12] Y. C. Chang, F. A. Lin and C.H. Lin, “Optimal chiller sequencing by branch and bound method for saving energy,” Energy Conversion and Management, vol. 46, pp. 2158-2172, 2005.
[13] Y. C. Chang, “Genetic algorithm based optimal chiller loading for energy conservation,” Applied Thermal Engineering, vol. 25, pp. 2800-2815, 2005.
[14] Y. C. Chang, “Optimal chiller loading by evolution strategy for saving energy,” Energy and Buildings, vol. 39, pp. 437-444, 2007.
[15] A J. Ardakani, F, F. Ardakani and S. H. Hosseinian, “A novel approach for optimal chiller loading using particle swarm optimization,” Energy and Building, vol. 40, pp. 2177-2187, 2008.
[16] L. d. S. Coelho, C. E. Klein, S. L. Sabat, and V. C. Mariani, “Optimal chiller loading for energy conservation using a new differential cuckoo search approach,” Energy, vol. 75, pp.237-243, 2014.
[17] C. Y Wu and K. Y. Tseng, “Topology optimization of structures using modified binary differential evolution,” Structural and Multidisciplinary Optimization, vol. 42, pp. 939-953, 2010.
[18] C. Y. Wu and K. Y. Tseng, “Stress-based binary differential evolution for topology optimization of structures,” Proceedings of the Institution of Mechanical Engineers- Part C: Journal of Mechanical Engineering Science, vol. 224, no. 2, pp. 443-457, 2010.
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