Abstract
The Performance of most of the Nature-/bio-inspired optimization algorithms is severely affected when applied for solving constrained problems. The approach of Genetic Algorithm (GA) is one of the most popular techniques; however, similar to other contemporary algorithms, its performance may also degenerate when applied for solving constrained problems. There are several constraint handling techniques proposed so far; however, developing efficient constraint handling technique still remains a challenge for the researchers. This paper presents a multi-objective optimization approach referred to as Boundary Searching GA (BSGA). It considers every constraint as an objective function and focuses on locating boundary of the feasible region and further search for the optimum solution. The approach is validated by solving four test problems. The solutions obtained are very competent in terms of the best and mean solutions in comparison with contemporary algorithms. The results also demonstrated its robustness solving these problems. The advantages, limitations and future directions are also discussed.
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References
Kulkarni, A.J., Tai, K.: Solving Constrained Optimization Problems Using Probability Collectives and a Penalty Function Approach. International Journal of Computational Intelligence and Applications (10), 445–470 (2011)
Coello Coello, C.A.: Theorotical and Numerical Constraint-Handling Techniques Used with Evolutionary Algorithms: A Survey of the State of the Art. Computer Methods in Applied Mechanics and Engineering 191(11-12), 1245–1287 (2002)
Camponogara, E., Talukdar, S.: A Genetic Algorithm for Constrained and Multiobjective Optimization. In: Alander, J.T. (ed.) 3rd Nordic Workshop on Genetic Algorithm and their Applications, pp. 49–62 (1997)
Coello Coello, C.A.: Treating Constraints as Objective for Single-Objective Evolutionary Optimization. Engineering Optimization 32(3), 275–308 (2000)
Ray, T., Tai, K., Chye, S.K.: An Evolutionary Algorithm for Constrained Optimization. In: Proceedings of the Genetic and Evolutionary Computation Conference, San Francisco, California, pp. 771–777 (2000)
Runarson, T.P., Yao, X.: Stochastic Ranking for Constrained Evolutionary Optimization. IEEE Transactions on Evolutionary Computation 4(3), 284–294 (2000)
Schoenauer, M., Michalewicz, Z.: Evolutionari Computation at the Edge of Feasibility. In: Ebeling, W., Rechenberg, I., Voigt, H.-M., Schwefel, H.-P. (eds.) PPSN 1996. LNCS, vol. 1141, pp. 245–254. Springer, Heidelberg (1996)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. In: Giannakoglou, K.C. (ed.) Evolutionary Methods for Design, Optimisation and Control with Application to Industrial Problems (EUROGEN 2001), pp. 95–100 (2002)
Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II. Evolutionary Computation 6(2), 182–197 (2002)
Tai, K., Prasad, J.: Target-Matching Test Problem for Multiobjective Topology Optimization Using Genetic Algorithms. Structural and Multidisciplinary Optimization 34, 333–345 (2007)
Coello Coello, C.A.: Use of a Self-Adaptive Penalty Approach for Engineering Optimization Problems. Computers in Industry 41(2), 113–127 (2000)
Farmani, R., Wright, J.A.: Self-Adaptive Fitness Formulation for Constrained Optimization. IEEE Transactions on Evolutionary Computation 7(5), 445–455 (2003)
Lampinen, J.: A Constraint Handling Approach for the Differential Evolution Algorithm. In: Proceedings of the IEEE Congress on Evolutionary Computation, vol. 2, pp. 1468–1473 (2002)
Koziel, S., Michalewicz, Z.: Evolutionary Algorithms, Homomorphous Mappings, and Constrained Parameter Optimization. Evolutionary Computation 7(1), 19–44 (1999)
Becerra, R.L., Coello Coello, C.A.: Cultured Differential Evolution for Constrained Optimization. Computer Methods in Applied Mechanics and Engineering 195(33-36), 4303–4322 (2006)
Hedar, A.R., Fukushima, M.: Derivative-Free Simulated Annealing Method for Constrained Continuous Global Optimization. Journal of Global Optimization 35(4), 521–549 (2006)
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Metkar, S.J., Kulkarni, A.J. (2014). Boundary Searching Genetic Algorithm: A Multi-objective Approach for Constrained Problems. In: Satapathy, S., Udgata, S., Biswal, B. (eds) Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2013. Advances in Intelligent Systems and Computing, vol 247. Springer, Cham. https://doi.org/10.1007/978-3-319-02931-3_30
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DOI: https://doi.org/10.1007/978-3-319-02931-3_30
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02930-6
Online ISBN: 978-3-319-02931-3
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