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References
- Liu X, Jiao X, Li C, Huang M (2013) Research of Job-Shop Scheduling Problem Based on Improved Crossover Strategy Genetic Algorithm. In: Proc. 2013 3rd Int. Conf. Comput. Sci. Netw. Technol. pp 1–4
- Mitchell M (1996) An introduction to genetic algorithms. MIT Press, Cambridge
- Goldberg D (1989) Genetic algorithms in search, optimization and machine learning. Addison Wesley, New York
- Kirkpatrick S, Gelatt CD, Vecchi MP (2007) Optimization by Simulated Annealing. Science (80- ) 220:671–680. doi: 10.1126/science.220.4598.671
- Junghans L, Darde N (2015) Hybrid single objective genetic algorithm coupled with the simulated annealing optimization method for building optimization. Energy Build 86:651–662. doi: 10.1016/j.enbuild.2014.10.039
- Mahmudy WF (2014) Improved simulated annealing for optimization of vehicle routing problem with time windows ( VRPTW ). Kursor 7:109–116.
- Liu W, Ye J (2014) Collapse optimization for domes under earthquake using a genetic simulated annealing algorithm. J Constr Steel Res 97:59–68. doi: 10.1016/j.jcsr.2014.01.015
- Chen PH, Shahandashti SM (2009) Hybrid of genetic algorithm and simulated annealing for multiple project scheduling with multiple resource constraints. Autom Constr 18:434–443. doi: 10.1016/j.autcon.2008.10.007
- Deb K (2001) Multi-objective Optimization using Evolutionary Algorithms. Wiley, Chichester, United Kingdom
- Vasan A (2005) Studies on advanced modeling techniques for optimal reservoir operation and performance evaluation of an irrigation system. Birla Institute of Technology and Science, Pilani, India
- Brownlee J (2011) Clever Algorithms: Nature-Inspired Programming Recipes, 2nd ed.
- Al-Khateeb B, Tareq WZ (2013) Solving 8-Queens Problem by Using Genetic Algorithms, Simulated Annealing, and Randomization Method. In: Int. Conf. Dev. eSystems Eng. pp 187–191
- Vasan A, Raju KS (2009) Comparative analysis of Simulated Annealing, Simulated Quenching and Genetic Algorithms for optimal reservoir operation. Appl Soft Comput 9:274–281.
- Crossland AF, Jones D, Wade NS (2014) Electrical Power and Energy Systems Planning the location and rating of distributed energy storage in LV networks using a genetic algorithm with simulated annealing. Int J Electr Power Energy Syst 59:103–110. doi: 10.1016/j.ijepes.2014.02.001
- Czapinski M (2010) Parallel Simulated Annealing with Genetic Enhancement for flowshop problem. Comput Ind Eng 59:778–785. doi: 10.1016/j.cie.2010.08.003
- Mahmudy W, Marian R, Luong L (2013) Hybrid Genetic Algorithms for Multi-Period Part Type Selection and Machine Loading Problems in Flexible Manufacturing System. In: IEEE Int. Conf. Comput. Intell. Cybern. pp 126–130
- Jamil M, Yang X-S (2013) A Literature Survey of Benchmark Functions For Global Optimization Problems Citation details: Momin Jamil and Xin-She Yang, A literature survey of benchmark functions for global optimization problems. Int J Math Model Numer Optim 4:150–194. doi: 10.1504/IJMMNO.2013.055204
- Oltean M (2003) Evolving Evolutionary Algorithms for Function Optimization. 7th Jt Conf Inf Sci 1:295–298.
- Pehlivanoglu YV (2013) A New Particle Swarm Optimization Method Enhanced With a Periodic Mutation Strategy. 17:436–452.
- Pant M, Thangaraj R, Abraham A (2009) Particle Swarm Optimization : Performance Tuning and Empirical Analysis. Foundations 3:101–128. doi: 10.1007/978-3-642-01085-9
References
Liu X, Jiao X, Li C, Huang M (2013) Research of Job-Shop Scheduling Problem Based on Improved Crossover Strategy Genetic Algorithm. In: Proc. 2013 3rd Int. Conf. Comput. Sci. Netw. Technol. pp 1–4
Mitchell M (1996) An introduction to genetic algorithms. MIT Press, Cambridge
Goldberg D (1989) Genetic algorithms in search, optimization and machine learning. Addison Wesley, New York
Kirkpatrick S, Gelatt CD, Vecchi MP (2007) Optimization by Simulated Annealing. Science (80- ) 220:671–680. doi: 10.1126/science.220.4598.671
Junghans L, Darde N (2015) Hybrid single objective genetic algorithm coupled with the simulated annealing optimization method for building optimization. Energy Build 86:651–662. doi: 10.1016/j.enbuild.2014.10.039
Mahmudy WF (2014) Improved simulated annealing for optimization of vehicle routing problem with time windows ( VRPTW ). Kursor 7:109–116.
Liu W, Ye J (2014) Collapse optimization for domes under earthquake using a genetic simulated annealing algorithm. J Constr Steel Res 97:59–68. doi: 10.1016/j.jcsr.2014.01.015
Chen PH, Shahandashti SM (2009) Hybrid of genetic algorithm and simulated annealing for multiple project scheduling with multiple resource constraints. Autom Constr 18:434–443. doi: 10.1016/j.autcon.2008.10.007
Deb K (2001) Multi-objective Optimization using Evolutionary Algorithms. Wiley, Chichester, United Kingdom
Vasan A (2005) Studies on advanced modeling techniques for optimal reservoir operation and performance evaluation of an irrigation system. Birla Institute of Technology and Science, Pilani, India
Brownlee J (2011) Clever Algorithms: Nature-Inspired Programming Recipes, 2nd ed.
Al-Khateeb B, Tareq WZ (2013) Solving 8-Queens Problem by Using Genetic Algorithms, Simulated Annealing, and Randomization Method. In: Int. Conf. Dev. eSystems Eng. pp 187–191
Vasan A, Raju KS (2009) Comparative analysis of Simulated Annealing, Simulated Quenching and Genetic Algorithms for optimal reservoir operation. Appl Soft Comput 9:274–281.
Crossland AF, Jones D, Wade NS (2014) Electrical Power and Energy Systems Planning the location and rating of distributed energy storage in LV networks using a genetic algorithm with simulated annealing. Int J Electr Power Energy Syst 59:103–110. doi: 10.1016/j.ijepes.2014.02.001
Czapinski M (2010) Parallel Simulated Annealing with Genetic Enhancement for flowshop problem. Comput Ind Eng 59:778–785. doi: 10.1016/j.cie.2010.08.003
Mahmudy W, Marian R, Luong L (2013) Hybrid Genetic Algorithms for Multi-Period Part Type Selection and Machine Loading Problems in Flexible Manufacturing System. In: IEEE Int. Conf. Comput. Intell. Cybern. pp 126–130
Jamil M, Yang X-S (2013) A Literature Survey of Benchmark Functions For Global Optimization Problems Citation details: Momin Jamil and Xin-She Yang, A literature survey of benchmark functions for global optimization problems. Int J Math Model Numer Optim 4:150–194. doi: 10.1504/IJMMNO.2013.055204
Oltean M (2003) Evolving Evolutionary Algorithms for Function Optimization. 7th Jt Conf Inf Sci 1:295–298.
Pehlivanoglu YV (2013) A New Particle Swarm Optimization Method Enhanced With a Periodic Mutation Strategy. 17:436–452.
Pant M, Thangaraj R, Abraham A (2009) Particle Swarm Optimization : Performance Tuning and Empirical Analysis. Foundations 3:101–128. doi: 10.1007/978-3-642-01085-9