Hybrid real-code ant colony optimisation for constrained mechanical design

References

• Afshar, A., & Madadgar, S. (2008). Ant colony optimization for continuous domains: Application to reservoir operation problems. In Proceedings of the Eighth International Conference on Hybrid Intelligent Systems (pp. 13–18). Barcelona, Spain.
   Google Scholar
• Afshar, M.H. (2010). A parameter free Continuous Ant Colony Optimization Algorithm for the optimal design of storm sewer networks: Constrained and unconstrained approach. Advances in Engineering Software, 41, 188–195.
   Web of Science ®Google Scholar
• Bäck, T. (1996). Evolutionary algorithms in theory and practice. Oxford: Oxford University Press.
   Google Scholar
• Baluja, S. (1994). Population-based incremental learning: A method for integrating genetic search based function optimisation and competitive learning (Technical Report CMU_CS_95_163). Pittsburgh, PA: School of Computer Science, Carnegie Mellon University.
   Google Scholar
• Botkin, M.A. (1995). Shape optimization with buckling and stress constraints. AIAA, 34, 423–425.
   Web of Science ®Google Scholar
• Brest, J., Korošec, P., Šilc, J., Zamuda, A., Bošković, B., & Maučec M.S. (2013). Differential evolution and differential ant-stigmergy on dynamic optimisation problems. International Journal of Systems Science, 44, 663–679.
   Web of Science ®Google Scholar
• Bureerat, S. (2011a). Population-based incremental learning in continuous spaces. Advances in Intelligent and Soft Computing, 96, 77–86.
   Google Scholar
• Bureerat, S. (2011b). Hybrid population-based incremental learning using real codes. Lecture Notes in Computer Science, 6683, 379–391.
   Google Scholar
• Bureerat, S., & Limtragool, J. (2008). Structural topology optimisation using simulated annealing with multiresolution design variables. Finite Elements in Analysis and Design, 44, 738–747.
   Web of Science ®Google Scholar
• Bureerat, S., & Sriworamas, K. (2007). Population-based incremental learning for multiobjective optimisation. Advances in Soft Computing, 9, 223–231.
   Google Scholar
• Chebouba, A., Yalaoui, F., Smati, A., Amodeo, L., Younsib, K., & Tairi, A. (2009). Optimization of natural gas pipeline transportation using ant colony optimization. Computers and Operations Research, 36, 1916–1923.
   Web of Science ®Google Scholar
• Chelouah, R., & Siarry, P. (2005). A hybrid method combining continuous tabu search and Nelder–Mead simplex algorithms for the global optimization of multiminima functions. European Journal of Operational Research, 161, 636–654.
   Web of Science ®Google Scholar
• Cheng, F.Y., & Li, D. (1997). Fuzzy set theory with genetic algorithms in constrained structural optimization. In D.M. Frangopol & F.Y. Cheng (Eds.), Advances in structural optimization: ASCE Proceedings of the US-Japan Joint Seminar on Structural Optimization (pp. 55–66). New York, NY: American Society of Civil Engineers.
   Google Scholar
• Ciornei, I., & Kyriakides, E. (2012). Hybrid ant colony-genetic algorithm (GAAPI) for global continuous optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 42, 234–245.
   PubMed Web of Science ®Google Scholar
• Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGAII. IEEE Transactions on Evolutionary Computation, 6, 182–197.
   Web of Science ®Google Scholar
• Dorigo, M. (1992). Optimization, learning and natural algorithms (in Italian) (Ph.D. thesis). Dipartimento di Elettronica, Politecnico di Milano, Italy.
   Google Scholar
• Erol, O.K., & Eksin, I. (2006). A new optimisation method: Big Bang–Big Crunch. Advances in Engineering Software, 37, 106–111.
   Web of Science ®Google Scholar
• Geem, Z.W., & Kim, J.H. (2001). A new heuristic optimisation algorithm: Harmony search. Simulation, 76, 60–68.
   Google Scholar
• Guo, P., & Zhu, L. (2012). Ant colony optimization for continuous domains. In Proceedings of the 8th International Conference on Natural Computation (ICNC 2012) (pp. 758–762). Sichuan, China.
   Google Scholar
• Hajizadeh, Y., Christie, M., & Demyanov, V. (2011). Ant colony optimization for history matching and uncertainty quantification of reservoir models. Journal of Petroleum Science and Engineering, 77, 78–92.
   Web of Science ®Google Scholar
• Hansen, N., Müller, S.D., & Koumoutsakos, P. (2003). Reducing the time complexity of the derandomised evolution strategy with covariance matrix adaptation (CMA-ES). Evolutionary Computation, 11, 1–18.
   PubMed Web of Science ®Google Scholar
• Hazra, J., & Sinha, A. (2009). Application of soft computing methods for economic dispatch in power systems. International Journal of Electrical and Electronics Engineering, 3(9), 538–543.
   Google Scholar
• Herrera, F., Lozano, M., & Molona, D. (2006). Continuous scatter search: An analysis of the integration of some combination methods and improvement strategies. European Journal of Operational Research, 169, 450–476.
   Web of Science ®Google Scholar
• Hsu, C.-M. (2012). Applying genetic programming and ant colony optimisation to improve the geometric design of a reflector. International Journal of Systems Science, 43, 972–986.
   Web of Science ®Google Scholar
• Huang, K.-L., & Liao, C.-J. (2008). Ant colony optimization combined with taboo search for the job shop scheduling problem. Computers & Operations Research, 35, 1030–1046.
   Web of Science ®Google Scholar
• Kabir, M.M., Shahjahan, M., & Murase, K. (2012). A new hybrid ant colony optimization algorithm for feature selection. Expert Systems with Applications, 39, 3747–3763.
   Web of Science ®Google Scholar
• Kanyakam, S., & Bureerat, S. (2012). Comparative performance of surrogate-assisted MOEAs for geometrical design of pin-fin heat sinks. Journal of Applied Mathematics, 2012, Article ID 534783.
   Google Scholar
• Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimisation: Artificial bee colony (ABC) algorithm. Journal of Global Optimisation, 39, 459–171.
   Web of Science ®Google Scholar
• Karen, I., Yildiz, A.R., Kaya, N., Öztürk, N., & Öztürk, F. (2006). Hybrid approach for genetic algorithm and Taguchi's method based design optimization in the automotive industry. International Journal of Production Research, 44(22), 4897–4914.
   Web of Science ®Google Scholar
• Karimi, A., Nobahari, H., & Siarry, P. (2010). Continuous ant colony system and tabu search algorithms hybridized for global minimization of continuous multi-minima functions. Computational Optimization and Applications, 45, 639–661.
   Web of Science ®Google Scholar
• Kashan, A.H. (2011). An efficient algorithm for constrained global optimisation and application to mechanical engineering design: League championship algorithm (LCA). Computer-Aided Design, 43, 1769–1792.
   Web of Science ®Google Scholar
• Kaveh, A., & Talatahari, S. (2008). A hybrid particle swarm and ant colony optimization for design of truss structures. Asian Journal of Civil Engineering (Building and Housing), 9, 329–348.
   Google Scholar
• Kaveh, A., & Talatahari, S. (2009). Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures. Computers and Structures, 87, 267–283.
   Web of Science ®Google Scholar
• Kaveh, A., & Talatahari, S. (2010). A novel heuristic optimisation method: Charged System Search. Acta Mechanica, 213, 267–289.
   Web of Science ®Google Scholar
• Kong, M., & Tian, P. (2006). A direct application of ant colony optimization to function optimization problem in continuous domain. Lecture Notes in Computer Science, 4150, 324–331.
   Google Scholar
• Lee, C.-Y., Lee, Z.-J., Lin, S.-W., & Ying, K.-C. (2010). An enhanced ant colony optimization (EACO) applied to capacitated vehicle routing problem. Applied Intelligence, 32, 88–95.
   Web of Science ®Google Scholar
• Liao, T., Montes de Oca, M.A., Aydin, D., Stützle, T., & Dorigo, M. (2011). An incremental ant colony algorithm with local search for continuous optimization. In Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation(GECCO’11) (pp. 125–132). New York, NY: ACM.
   Google Scholar
• Liao, T.W., Kuo, R.J., & Hu, J.T.L. (2012). Hybrid ant colony optimization algorithms for mixed discrete–continuous optimization problems. Applied Mathematics and Computation, 219, 3241–3252.
   Web of Science ®Google Scholar
• Lindfield, G., & Penny, J. (1995) Numerical methods using MATLAB. New York, NY: Ellis Horwood.
   Google Scholar
• Lion, C.-D., Hsieh, Y.-C., & Chen, Y.-Y. (2013). New encoding scheme-based hybrid algorithm for minimising two-machine flow-shop group scheduling problem. International Journal of Systems Science, 44, 77–93.
   Web of Science ®Google Scholar
• Madadgar, S., & Afshar, A. (2009). An improved continuous ant algorithm for optimization of water resources problems. Water Resources Management, 23, 2119–2139.
   Web of Science ®Google Scholar
• Majumdar, A., Maiti, D.K., & Maity, D. (2012). Damage assessment of truss structures from changes in natural frequencies using ant colony optimization. Applied Mathematics and Computation, 218, 9759–9772.
   Web of Science ®Google Scholar
• Mariani, V.C., & dos Santos Coelho, L. (2011). A hybrid shuffled complex evolution approach with pattern search for unconstrained optimization. Mathematics and Computers in Simulation, 81, 1901–1909.
   Web of Science ®Google Scholar
• Mohamad, M., Tokhi, M.O., & Omar, M. (2011). Continuous ant colony optimisation for active vibration control of flexible beam structures. In Proceedings of the IEEE International Conference on Mechatronics (ICM) (pp. 803–808). Istanbul, Turkey.
   Google Scholar
• Nakawiro, W., & Erlich, I. (2009). Optimal load shedding for voltage stability enhancement by ant colony optimization. In Proceedings of the 15th International Conference on Intelligent System Applications to Power Systems, 2009 (pp. 1–6). Curitiba, Brazil.
   Google Scholar
• Nelder, J.A., & Mead, R. (1965). A simplex method for function minimization. Computer Journal, 7, 308–313.
   Web of Science ®Google Scholar
• Panahi, H., & Tavakkoli-Moghaddam, R. (2011). Solving a multi-objective open shop scheduling problem by a novel hybrid ant colony optimization. Expert Systems with Applications, 38, 2817–2822.
   Web of Science ®Google Scholar
• Pholdee, N., & Bureerat, S. (2012). Performance enhancement of multiobjective evolutionary optimizers for truss design using an approximate gradient. Computers and Structures, 106–107, 115–124.
   Web of Science ®Google Scholar
• Pholdee, N., & Bureerat, S. (2013). Hybridisation of real-code population-based incremental learning and differential evolution for multiobjective design of trusses. Information Sciences, 223, 136–152.
   Web of Science ®Google Scholar
• Rajendran, I., & Vijayaranagan, S. (2007). Simulated annealing approach to the optimal design of automotive suspension systems. International Journal of Vehicle Design, 43(1–4), 11–30.
   Web of Science ®Google Scholar
• Rao, R.V., Savsani, V.J., & Vakharia, D.P. (2011). Teaching–learning-based optimisation: A novel method for constrained mechanical design optimisation problems. Computer-Aided Design, 43, 303–315.
   Web of Science ®Google Scholar
• Rashedi, E., Nezamabadi-pour, H., & Saryazdi, S. (2009). GSA: A gravitational search algorithm. Information Sciences, 179, 2232–2248.
   Web of Science ®Google Scholar
• Reyes-Sierra, M., & Coello Coello, C.A. (2006). Multi-objective particle swarm optimisers: A survey of the state-of-the-art. International Journal of Computational Intelligence Research, 2, 287–308.
   Google Scholar
• Savsani, V., Rao, R.V., & Vakharia, D.P. (2010). Optimal weight design of a gear train using particle swarm optimization and simulated annealing algorithms. Mechanism and Machine Theory, 45, 531–541.
   Web of Science ®Google Scholar
• Schluter, M., Egea, J.A., Antelo, L.T., Alonso, A.A., & Banga, J.R. (2009) An extended ant colony optimization algorithm for integrated process and control system design. Industrial & Engineering Chemistry Research, 48, 6723–6738.
   Web of Science ®Google Scholar
• Shelokar, P.S., Siarry, P., Jayaraman, V.K., & Kulkarni, B.D. (2007). Particle swarm and ant colony algorithms hybridized for improved continuous optimization. Applied Mathematics and Computation, 188, 129–142.
   Web of Science ®Google Scholar
• Socha, K., & Blum, C. (2007). An ant colony optimization algorithm for continuous optimization: Application to feed-forward neural network training. Neural Computing and Applications, 16, 235–247.
   Web of Science ®Google Scholar
• Socha, K., & Dorigo, M. (2008). Ant colony optimisation for continuous domains. European Journal of Operational Research, 185, 1155–1173.
   Web of Science ®Google Scholar
• Srisoporn, S., & Bureerat, S. (2008). Geometrical design of plate-fin heat sinks using hybridization of MOEA and RSM. IEEE Transactions on Components and Packaging Technologies, 31, 351–360.
   Web of Science ®Google Scholar
• Storn, R., & Price, K. (1997). Differential evolution – A simple and efficient heuristic for global optimisation over continuous spaces. Journal of Global Optimisation, 11, 341–359.
   Web of Science ®Google Scholar
• Tenne, Y., Izui, K., & Nishiwaki, S. (2012). A hybrid model-classifier framework for managing predition uncertainty in expensive optimization problems. International Journal of Systems Science, 43, 1305–1321
   Web of Science ®Google Scholar
• Viana, F.A.C., Venter, G., & Balabanov, V. (2010). An algorithm for fast optimal Latin hypercube design of experiments. International Journal for Numerical Methods in Engineering, 82(2), 135–156.
   Web of Science ®Google Scholar
• Wang, J., Osagie, E., Thulasiraman, P., & Thulasiram, R.K. (2009). HOPNET: A hybrid ant colony optimization routing algorithm for mobile ad hoc network. Ad Hoc Networks, 7, 690–705.
   Web of Science ®Google Scholar
• Wang, L., Xu, Y., & Li, L. (2011). Parameter identification of chaotic systems by hybrid Nelder–Mead simplex search and differential evolution algorithm. Expert Systems with Applications, 38, 3238–3245.
   Web of Science ®Google Scholar
• Xiao, J., & Li, L. (2011). A hybrid ant colony optimization for continuous domains. Expert Systems with Applications, 38, 11072–11077.
   Web of Science ®Google Scholar
• Yang, X.S., & Deb, S. (2010). Engineering optimisation by cuckoo search. International Journal of Mathematical Modeling and Numerical Optimisation, 1, 330–343.
   Google Scholar
• Yildiz, A.R. (2009a). A new design optimization framework based on immune algorithm and Taguchi's method. Computers in Industry, 60(8), 613–620.
   Web of Science ®Google Scholar
• Yildiz, A.R. (2009b). A novel hybrid immune algorithm for global optimization in design and manufacturing. Robotics and Computer-Integrated Manufacturing, 25, 261–270.
   Web of Science ®Google Scholar
• Yildiz, A.R. (2009c). A novel particle swarm optimization approach for product design and manufacturing. The International Journal of Advanced Manufacturing Technology, 40, 617–628.
   Web of Science ®Google Scholar
• Yildiz, A.R. (2012). A new hybrid particle swarm optimization approach for structural design optimization in the automotive industry. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 226, 1340–1351.
   Web of Science ®Google Scholar
• Yildiz, A.R. (2013). Optimization of cutting parameters in multi-pass turning using artificial bee colony-based approach. Information Sciences, 220, 399–407.
   Web of Science ®Google Scholar
• Yu, L., & Xu, P. (2011). Structural health monitoring based on continuous ACO method. Microelectronics Reliability, 51(2), 270–278.
   Web of Science ®Google Scholar
• Zahara, E., & Kao, Y.T. (2009). Hybrid Nelder–Mead simplex search and particle swarm optimization for constrained engineering design problems. Expert Systems with Applications, 36, 3880–3886.
   Web of Science ®Google Scholar
• Zhang, X.L., Chen, X.F., & He, Z.J. (2010). An ACO-based algorithm for parameter optimization of support vector machines. Expert Systems with Applications, 37, 6618–6628.
   Web of Science ®Google Scholar
• Zhu, Q., Yang, Z., & Ma, W. (2011). A quickly convergent continuous ant colony optimization algorithm with Scout Ants. Applied Mathematics and Computation, 218, 1805–1819.
   Web of Science ®Google Scholar