Abstract
In this paper, an efficient modified Differential Evolution algorithm, named EMDE, is proposed for solving constrained non-linear integer and mixed-integer global optimization problems. In the proposed algorithm, new triangular mutation rule based on the convex combination vector of the triplet defined by the three randomly chosen vectors and the difference vectors between the best,better and the worst individuals among the three randomly selected vectors is introduced. The proposed novel approach to mutation operator is shown to enhance the global and local search capabilities and to increase the convergence speed of the new algorithm compared with basic DE. EMDE uses Deb’s constraint handling technique based on feasibility and the sum of constraints violations without any additional parameters. In order to evaluate and analyze the performance of EMDE, Numerical experiments on a set of 18 test problems with different features, including a comparison with basic DE and four state-of-the-art evolutionary algorithms are executed. Experimental results indicate that in terms of robustness, stability and efficiency, EMDE is significantly better than other five algorithms in solving these test problems. Furthermore, EMDE exhibits good performance in solving two high-dimensional problems, and it finds better solutions than the known ones. Hence, EMDE is superior to the compared algorithms.
Similar content being viewed by others
References
Mohamed AW, Sabry HZ (2012) Constrained optimization based on modified differential evolution algorithm. Inf Sci 194:171–208
Costa L, Oliveira P (2001) Evolutionary algorithms approach to the solution of mixed non-linear programming. Comput Chem Eng 25:257–266
Lin YC, Hwang KS, Wang FS (2004) A mixed-coding scheme of evolutionary algorithms to solve mixed-integer nonlinear programming problems. Comput Math Appl 47:1295–1307
Sandgren E (1990) Nonlinear integer and discrete programming in mechanical design, ASME Y. Mech Des 112:223–229
Dua V, Pistikopoulos EN (1998) Optimization techniques for process synthesis and material design under uncertainty. Chem Eng Res Des 76(3):408–416
Catalão JPS, Pousinho HMI, Mendes VMF (2010) Mixed-integer nonlinear approach for the optimal scheduling of a head-dependent hydro chain. Electr Power Syst Res 80(8):935–942
Garroppo RG, Giordano S, Nencioni G, Scutellà MG (2013) Mixed integer non-linear programming models for green network design. Comput Oper Res 40(1):273–281
Maldonado S, Pérez J, Weber R, Labbé M (2014) Feature selection for support vector machines via mixed integer linear programming. Inf Sci 279(20):163–175
Çetinkaya C, Karaoglan I, Gökçen H (2013) Two-stage vehicle routing problem with arc time windows: a mixed integer programming formulation and a heuristic approach. Eur J Oper Res 230(3):539–550
Liu P, Whitaker A, Pistikopoulos EN, Li Z (2011) A mixed-integer programming approach to strategic planning of chemical centres: a case study in the UK. Comput Chem Eng 35(8):1359–1373
Xu G, Papageorgiou LG (2009) A mixed integer optimisation model for data classification. Comput Ind Eng 56(4):1205–1215
Grossmann IE, Sahinidis NV (eds) (2002) Special issue on mixed-integer programming and its application to engineering, Part I, Optim. Eng., Kluwer Academic Publishers, Netherlands, vol. 3 (4)
Grossmann IE, Sahinidis NV (eds) (2002) Special issue on mixed-integer programming and its application to engineering, Part II, Optim. Eng., Kluwer Academic Publishers, Netherlands, vol. 4 (1)
Hsieh Y-C et al (2015) Solving nonlinear constrained optimization problems: an immune evolutionary based two-phase approach. Appl Math Model. doi:10.1016/j.apm.2014.12.019
Ng CK, Zhang LS, Li D, Tian WW (2005) Discrete filled function method for discrete global optimization. Comput Optim Appl 31(1):87–115
Gupta OK, Ravindran A (1985) Branch and bound experiments in convex nonlinear integer programming. Manag Sci 31(12):1533–1546
Borchers B, Mitchell JE (1994) An improved branch and bound algorithm for mixed integer nonlinear programming. Comput Oper Res 21:359–367
Geoffrion AM (1972) Generalized Benders decomposition. J Optim Theory Appl 10:237–260
DuranMA GI (1986) An outer approximation algorithm for a class of mixed-integer nonlinear programs. Math Program 36(3):307–339
Fletcher R, Leyffer S (1994) Solving mixed-integer programs by outer approximation. Math Program 66(1–3):327–349
Quesada I, Grossmann IE (1992) An LP/NLP based branch and bound algorithm for convex MINLP optimization problems. Comput Chem Eng 16(10–11):937–947
Westerlund T, Pettersson F (1995) A cutting plane method for solving convex MINLP problems. Comput Chem Eng 19:S131–S136
Lee S, Grossmann IE (2000) New algorithms for nonlinear generalized disjunctive programming. Comput Chem Eng 24:2125–2142
Bonami P, Biegler LT, Conn AR, Cornuéjols G, Grossmann IE, Laird CD, Lee J, Lodi A, Margot F, Sawaya N, Wächter A (2008) An algorithmic framework for convex mixed integer nonlinear programs. Discrete Optim 5:186–204
Abhishek K, Leyffer S, Linderoth JT (2010) FilMINT: an outer-approximation-based solver for nonlinear mixed integer programs. Inf J Comput 22:555–567
Belotti P, Kirches C, Leyffer S, Linderoth J, Luedtke J, Mahajan A (2013) Mixed-integer nonlinear optimization. Acta Num. 22:1–131
Liberti L, Cafieri S, Tarissan F (2009) Reformulations in mathematical programming: a computational approach. In: Abraham A, Hassanien AE, Siarry P (eds) Foundations on computational intelligence, studies in computational intelligence, vol 203. Springer, New York, pp 153–234
D’Ambrosio C, Lodi A (2011) Mixed integer nonlinear programming tools: a practical overview. In: 4OR 9, No. 4, 2011, pp. 329–349 (cit. on p. 13)
Trespalacios F, Grossmann IE (2014) Review of mixed-integer nonlinear and generalized disjunctive programming Methods. Chem Ing Tech 86:991–1012
Burer S, Letchford AN (2012) Non-convex mixed-integer nonlinear programming: a survey. Surveys Oper Res Manag Sci 17(2):97–106
Grossmann IE (2002) Review of non-linear mixed integer and disjunctive programming techniques. Optim Eng 3:227–252
Cardoso MF, Salcedo RL, Feyo de Azevedo S, Barbosa D (1997) A simulated annealing approach to the solution of minlp problems. Comput Chem Eng 21(12):1349–1364
Rosen SL, Harmonosky CM (2005) An improved simulated annealing simulation optimization method for discrete parameter stochastic systems. Comput Oper Res 32:343–358
Glover F (2006) Parametric tabu-search for mixed integer programs. Comput Oper Res 33(9):2449–2494
Hua Z, Huang F (2006) A variable-grouping based genetic algorithm for large-scale integer programming. Inf Sci 176(19):2869–2885
Kesen SE, Das SK, Güngör Z (2010) A genetic algorithm based heuristic for scheduling of virtual manufacturing cells (VMCs). Comput Oper Res 37(6):1148–1156
Turkkan N (2003) Discrete optimization of structures using a floating-point genetic algorithm. In: Annual conference of the Canadian society for civil engineering, Moncton, Canada
Yokota T, Gen M, Li YX (1996) Genetic algorithm for non-linear mixed integer programming problems and its applications. Comput Ind Eng 30:905–917
Wasanapradit T, Mukdasanit N, Chaiyaratana N, Srinophakun T (2011) Solving mixed-integer nonlinear programming problems using improved genetic algorithms. Korean J Chem Eng 28(1):32–40
Deep K, Singh KP, Kansal ML, Mohan C (2009) A real coded genetic algorithm for solving integer and mixed integer optimization problems. Appl Math Comput 212(2):505–518
Cai J, Thierauf G (1996) Evolution strategies for solving discrete optimization problems. Adv Eng Softw 25:177–183
Costa L, Oliveira P Evolutionary algorithms approach to the solution of mixed integer non-linear programming problems. Comput Chem Eng, 25(2–3):257-266
Cao YJ, Jiang L, Wu QH (2000) An evolutionary programming approach to mixed-variable optimization problems. Appl Math Model 24:931–942
Mohan C, Nguyen HT (1999) A controlled random search technique incorporating the simulating annealing concept for solving integer and mixed integer global optimization problems. Comput Opti Appl 14:103–132
Woon SF, Rehbock V (2010) A critical review of discrete filled function methods in solving nonlinear discrete optimization problems. Appl Math Comput 217(1):25–41
Yongjian Y, Yumei L (2007) A new discrete filled function algorithm for discrete global optimization. J Comput Appl Math 202(2):280–291
Socha K (2004) ACO for continuous and mixed-variable optimization. Ant colony, optimization and swarm intelligence. Springer, Berlin, pp 25–36
Schlüter M, Egea JA, Banga JR (2009) Extended ant colony optimization for non-convex mixed integer nonlinear programming. Comput Oper Res 36(7):2217–2229
Yiqing L, Xigang Y, Yongjian L (2007) An improved PSO algorithm for solving non-convex NLP/MINLP problems with equality constraints. Comput Chem Eng 31(3):153–162
Yue T, Guan-zheng T, Shu-guang D (2014) Hybrid particle swarm optimization with chaotic search for solving integer and mixed integer programming problems. J Central South Univ 21:2731–2742
Gao Y, Ren Z, Gao Y (2011) Modified differential evolution algorithm of constrained nonlinear mixed integer programming problems. Inf Technol J 10(11):2068–2075
Lin YC, Hwang KS, Wang FS (2004) A mixed-coding scheme of evolutionary algorithms to solve mixed-integer nonlinear programming problems. Comput Math Appl 47(8–9):1295–1307
Li H, Zhang L (2014) A discrete hybrid differential evolution algorithm for solving integer programming problems. Eng Optim 46(9):1238–1268
Mohamed AW (2015) An improved differential evolution algorithm with triangular mutation for global numerical optimization. Comput Ind Eng 85:359–375
Storn R, Price K (1995) Differential evolution- a simple and efficient adaptive scheme for global optimization over continuous spaces”, Technical Report TR-95-012, ICSI http://http.icsi.berkeley.edu/~storn/litera.html
Storn R, Price K (1997) Differential Evolution- a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359
Engelbrecht AP (2005) Fundamentals of computational swarm intelligence. Wiley, Hoboken
Das S, Suganthan PN (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evol Comput 15(1):4–31
Deb K (2000) An efficient constraint handling method for genetic algorithms. Comput Methods Appl Mech Eng 186:311–338
Venkatraman S, Yen GG (2005) A generic framework for constrained optimization using genetic algorithms. IEEE Trans Evol Comput 9(4):424–435
Storn R, Price K (1997) Differential evolution a simple and efficient heuristic for global optimization over continuous spaces. J Global Optim 11(4):341–359
Fan HY, Lampinen J (2003) A trigonometric mutation operation to differential evolution. J Global Optim 27(1):105–129
Price KV, Storn RM, Lampinen JA (2005) Differential evolution: a practical approach to global optimization, 1st edn. Springer, New York
Qin AK, Huang VL, Suganthan PN (2009) Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans Evol Comput 13(2):398–417
Mallipeddi R, Suganthan PN, Pan QK, Tasgetiren MF (2011) Differential evolution algorithm with ensemble of parameters and mutation Strategies. Appl Soft Comput 11(2):1679–1696
Wang Y, Cai Z, Zhang Q (2011) Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans Evol Comput 15(1):55–66
Zhang JQ, Sanderson AC (2009) JADE: adaptive differential evolution with optional external archive. IEEE Trans Evol Comput 13(5):945–958
Feoktistov V (2006) Differential evolution. Berlin, Germany, Springer-verlag, In Search of Solutions
Mezura-Montes E, Coello CAC (2005) A simple multimembered evolution strategy to solve constrained optimization problems. IEEE Trans Evol Comput 9(1):1–17
Dong N, Wang Y (2014) A memetic differential evolution algorithm based on dynamic preference for constrained optimization problems. J Appl Math, Article ID 606019, p 15. doi:10.1155/2014/606019
Lampinen J, Zelinka I (1999) Mixed integer-discrete-continuous optimization by differential evolution, Part 1: the optimization method. In: Ošmera P (ed.) (1999). Proceedings of Mendel 99, 5th international Mendel conference on soft computing, Brno, Czech Republic
Omran MGH, Engelbrecht AP (2007) Differential evolution for integer programming problems, IEEE congress on evolutionary computation, pp. 2237–2242
Li Y, Gen M (1996) Nonlinear mixed integer programming problems using genetic algorithm and penalty function. IEEE Int Conf Syst Man Cybernet 4:2677–2682
Lu H, Chen W (2008) Self-adaptive velocity particle swarm optimization for solving constrained optimization problems. J Global Optim 41:427–445
Conley W (1984) Computer optimization techniques. Petrocelli Books, Princeton
Author information
Authors and Affiliations
Corresponding author
Appendix
Appendix
Corrections on problems 6, 16 and 17.
Based on self check, there are few differences in problems 6, 16 and 17 in Ref. [40] from the original documents. Thus, the corrections on these problems are as follows.
Rights and permissions
About this article
Cite this article
Mohamed, A.W. An efficient modified differential evolution algorithm for solving constrained non-linear integer and mixed-integer global optimization problems. Int. J. Mach. Learn. & Cyber. 8, 989–1007 (2017). https://doi.org/10.1007/s13042-015-0479-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13042-015-0479-6