Modified Differential Evolution Based on Global Competitive Ranking for Engineering Design Optimization Problems
Engineering design optimization problems are formulated as large-scale mathematical programming problems with nonlinear objective function and constraints. Global optimization finds a solution while satisfying the constraints. Differential evolution is a population-based heuristic approach that is shown to be very efficient to solve global optimization problems with simple bounds. In this paper, we propose a modified differential evolution introducing self-adaptive control parameters, modified mutation, inversion operation and modified selection for obtaining global optimization. To handle constraints effectively, in modified selection we incorporate global competitive ranking which strikes the right balance between the objective function and the constraint violation. Sixteen well-known engineering design optimization problems are considered and the results compared with other solution methods. It is shown that our method is competitive when solving these problems.
KeywordsEngineering design constraints handling ranking differential evolution global optimization
Unable to display preview. Download preview PDF.
- 2.Bernardino, H.S., Barbosa, H.J.C., Lemonge, A.C.C.: A hybrid genetic algorithm for constrained optimization problems in mechanical engineering. IEEE Congress on Evolutionary Computation, 646–653 (2007)Google Scholar
- 7.Deb, K., Goyal, M.: Optimizing engineering designs using a combined genetic search. In: Back, I.T. (ed.) 7th International Conference on Genetic Algorithms, pp. 512–528 (1997)Google Scholar
- 15.Lampinen, J., Zelinka, I.: Mixed integer-discrete-continuous optimization by differential evolution. In: Proceedings of the 5th International Conference on Soft Computing, pp. 71–76 (1999)Google Scholar
- 17.Liu, T.-C.: Developing a fuzzy proportional-derivative controller optimization engine for engineering optimization problems. PhD Thesis, ch. 6 (2006), http://grc.yzu.edu.tw/OptimalWeb/Content.aspx?CatSubID=129
- 19.Ray, T., Tai, K.: An evolutionary algorithm with a multilevel pairing strategy for single and multiobjective optimization. Found. Comput. Decis. Sci. 26(1), 75–98 (2001)Google Scholar
- 25.Runarsson, T.P., Yao, X.: Constrained evolutionary optimization – the penalty function approach. In: Sarker, R., Mohammadian, M., Yao, X. (eds.) Evolutionary Optimization: International Series in Operations Research and Management Science, pp. 87–113 (2003)Google Scholar