Tuesday, March 27, 2018

Solving a Multiobjective Mixed Integer Nonlinear Fractional Programming Problem Involving 13 Big Integer Variables, Nonlinear Numerators and Denominators, and Fuzzy Weights

Jsun Yui Wong

The computer program listed below uses a technique in Murty, [36, pp. 277-279], to solve the immediately following problem:

Maximize 

 ((X(1) + 2 * X(2) ^ 3 + 3 * X(3) + X(4) + 2 * X(5) + 7 * X(6) + 3 * X(7) + 3 * X(8) + 11 * X(9) + 7 * X(10) + 3 * X(11)) / (X(2) ^ 3 + 5 * X(3) + X(6) + 2 * X(8) + 9 * X(10) + 3)) ^ 3,

 ((X(1) + 7 * X(3) + 4 * X(4) ^ 3 + 3 * X(5) + 5 * X(7) + 13 * X(9) + 11 * X(10) + X(12)) / (X(2) + 3 * X(4) ^ 3 + X(6) + 4 * X(8) + 6 * X(10) + 1)) ^ 2,

 ((X(1) + 3 * X(3) + 5 * X(4) + 6 * X(5) + 7 * X(7) + 4 * X(9) ^ 5 + 7 * X(10) + X(13) + 2) / (2 * X(1) + X(2) + 8 * X(3) + 6 * X(4) + 2 * X(5) + X(6) + 4 * X(7) + 3 * X(8) + 3 * X(9) ^ 5 + 7 * X(10) + 2)) ^ 3,

which are three objective functions

subject to
 
         X(1) + X(2) + 7 * X(3) + X(4) + 3 * X(5) + 6 * X(6) + 3 * X(7) + 5 * X(8) + 14 * X(9) + 3 * X(10) + X(11) + 7 * X(12) + X(13)<=70000

        4 * X(1) + X(2) + 2 * X(3) + 9 * X(4) + 7 * X(5) + 9 * X(6) + 4 * X(7) + 7 * X(8) + 7 * X(9) + 11 * X(10) + X(11) + 6 * X(12) + X(13)<=90000

       .3*X(1) ^ 3+ X(2) + .1 * X(3) + .1 * X(4) ^ 3 + .1 * X(5) + .1 * X(6) + .5 * X(7) ^ 5 + .1 * X(8) + .2 * X(9) ^ 2 + .1 * X(10) + X(11) + X(12) + 3 * X(13)<=500000

         0<=X(i) <= 1000, i=1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13       
       
X(1) through X(13) are general integer variables.

The formulation above is loosely based on the formulation in Sharma [42, p.149].

The decision makers have agreed that the weights X(14) through X(16) are fuzzy as follows:
                                     
  .45 + RND * .1

  .25 + RND * .1

  .15 + RND * .1, respectively.

X(17) through X(19) below are slack variables. 

One notes the following line 441, which includes the nonlinear numerators and denominators and the fuzzy weights--X(14), X(15), and X(16).     

One also notes the nonlinear constraint .3*X(1) ^ 3+ X(2) + .1 * X(3) + .1 * X(4) ^ 3 + .1 * X(5) + .1 * X(6) + .5 * X(7) ^ 5 + .1 * X(8) + .2 * X(9) ^ 2 + .1 * X(10) + X(11) + X(12) + 3 * X(13)<=500000.


0 DEFDBL A-Z

2 DEFINT K

3 DIM B(99), N(99), A(99), H(99), L(99), U(99), X(1111), D(111), P(111), PS(33)

12 FOR JJJJ = -32000 TO 32111 STEP .01

    14 RANDOMIZE JJJJ


    16 M = -1D+37
    51 FOR J40 = 1 TO 13
        55 A(J40) = RND * 1000


    56 NEXT J40

    61 A(14) = .45 + RND * .1

    63 A(15) = .25 + RND * .1

    65 A(16) = .15 + RND * .1


    128 FOR I = 1 TO 6000


        129 FOR KKQQ = 1 TO 16


            130 X(KKQQ) = A(KKQQ)
        131 NEXT KKQQ
        133 FOR IPP = 1 TO (1 + FIX(RND * 3))


            181 j = 1 + FIX(RND * 16)


            183 r = (1 - RND * 2) * A(j)
            187 X(j) = A(j) + (RND ^ (RND * 10)) * r
        222 NEXT IPP
        233 X(16) = 1 - X(14) - X(15)

        246 FOR J47 = 1 TO 13
            247 X(J47) = INT(X(J47))
            248 IF X(J47) < 0 THEN 1670
            249 IF X(J47) > 1000 THEN 1670
        251 NEXT J47


        268 IF X(14) < .45 THEN 1670
        269 IF X(14) > .55 THEN 1670
       

        272 IF X(15) < .25 THEN 1670
        273 IF X(15) > .35 THEN 1670

        274 IF X(16) < .15 THEN 1670
        275 IF X(16) > .25 THEN 1670


        301 X(17) = 70000 - X(1) - X(2) - 7 * X(3) - X(4) - 3 * X(5) - 6 * X(6) - 3 * X(7) - 5 * X(8) - 14 * X(9) - 3 * X(10) - X(11) - 7 * X(12) - X(13)
        303 X(18) = 90000 - 4 * X(1) - X(2) - 2 * X(3) - 9 * X(4) - 7 * X(5) - 9 * X(6) - 4 * X(7) - 7 * X(8) - 7 * X(9) - 11 * X(10) - X(11) - 6 * X(12) - X(13)
        305 X(19) = 500000 - .3 * X(1) ^ 3 - X(2) - .1 * X(3) - .1 * X(4) ^ 3 - .1 * X(5) - .1 * X(6) - .5 * X(7) ^ 5 - .1 * X(8) - .2 * X(9) ^ 2 - .1 * X(10) - X(11) - X(12) - 3 * X(13)


        401 FOR J47 = 17 TO 19

            402 IF X(J47) < 0 THEN X(J47) = X(J47) ELSE X(J47) = 0


        403 NEXT J47


        441 POBA = X(14) * ((X(1) + 2 * X(2) ^ 3 + 3 * X(3) + X(4) + 2 * X(5) + 7 * X(6) + 3 * X(7) + 3 * X(8) + 11 * X(9) + 7 * X(10) + 3 * X(11)) / (X(2) ^ 3 + 5 * X(3) + X(6) + 2 * X(8) + 9 * X(10) + 3)) ^ 3 + X(15) * ((X(1) + 7 * X(3) + 4 * X(4) ^ 3 + 3 * X(5) + 5 * X(7) + 13 * X(9) + 11 * X(10) + X(12)) / (X(2) + 3 * X(4) ^ 3 + X(6) + 4 * X(8) + 6 * X(10) + 1)) ^ 2 + X(16) * ((X(1) + 3 * X(3) + 5 * X(4) + 6 * X(5) + 7 * X(7) + 4 * X(9) ^ 5 + 7 * X(10) + X(13) + 2) / (2 * X(1) + X(2) + 8 * X(3) + 6 * X(4) + 2 * X(5) + X(6) + 4 * X(7) + 3 * X(8) + 3 * X(9) ^ 5 + 7 * X(10) + 2)) ^ 3 + 90000000000 * (X(17) + X(18) + X(19))


        466 P = POBA

        1111 IF P <= M THEN 1670


        1450 M = P
        1454 FOR KLX = 1 TO 19

            1455 A(KLX) = X(KLX)
        1456 NEXT KLX
        1557 GOTO 128

    1670 NEXT I
    1889 IF M < 86811100000 THEN 1999

    1900 PRINT A(1), A(2), A(3), A(4), A(5)

    1907 PRINT A(6), A(7), A(8), A(9), A(10), A(11), A(12), A(13), M, JJJJ
    1909 PRINT A(14), A(15), A(16)

    1910 PRINT A(17), A(18), A(19)

1999 NEXT JJJJ


This BASIC computer program was run with QB64v1000-win [49]. The complete output through JJJJ = -31999.37000000001 is shown below:

70        0          0        121         1000
0        8         0       1000         0
1000         0           0        86845999407.83077
-31999.96000000001
.550000041723147         .2500011811805727         .1999987770962803
0        0         0

66     0       0     126       1000
0     7     0     1000     0
1000     997      57     86813867914.30338
-31999.52000000008
.5500000415514993     .2500037636007895     .1999961948477112
0   0   0

70      0       0     121       1000
0     8     0    1000     0
1000     0     0      86845999406.17258
-31999.4300000001
.5500000417126456      .25000150443893        .1999984538484245
0   0   0

68      0       0     123       1000
0      8      0      1000       0
1000     994     0      86845999407.77126
-31999.3700000001
.55000004172277       .2500020029690194        .1999979553082107
0     0     0

Above there is no rounding by hand; it is just straight copying by hand from the monitor screen. On a personal computer with a Pentium Dual-Core CPU E5200 @2.50GHz, 2.50 GHz, 960 MB of RAM and QB64v1000-win [49], the wall-clock time for obtaining the output through
JJJJ = -31999.37000000001 was 30 seconds, not including the time for “Creating .EXE file.”             
                                               
Acknowledgment

I would like to acknowledge the encouragement of Roberta Clark and Tom Clark.

References

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Saturday, March 24, 2018

Solving a Multiobjective Integer Nonlinear Fractional Programming Problem Involving 10 General Integer Variables and Nonlinear Numerators and Denominators

Jsun Yui Wong

The computer program listed below uses a technique in Murty, [36, pp. 277-278], to solve the immediately following problem:

Maximize 

((X(1) + 2 * X(2) ^ 3 + 3 * X(3) + X(4) + 2 * X(5) + 7 * X(6) + 3 * X(7) + 3 * X(8) + 11 * X(9) + 7 * X(10)) / (X(2) ^ 3 + 5 * X(3) + X(6) + 2 * X(8) + 9 * X(10) + 3)) ^ 3,

((X(1) + 7 * X(3) + 4 * X(4) ^ 3 + 3 * X(5) + 5 * X(7) + 13 * X(9) + 11 * X(10)) / (X(2) + 3 * X(4) ^ 3 + X(6) + 4 * X(8) + 6 * X(10) + 1)) ^ 2,

((X(1) + 3 * X(3) + 5 * X(4) + 6 * X(5) + 7 * X(7) + 4 * X(9) ^ 5 + 7 * X(10) + 2) / (2 * X(1) + X(2) + 8 * X(3) + 6 * X(4) + 2 * X(5) + X(6) + 4 * X(7) + 3 * X(8) + 3 * X(9) ^ 5 + 7 * X(10) + 2)) ^ 3,

which are three objective functions

subject to

         X(1) + X(2) + 7 * X(3) + X(4) + 3 * X(5) + 6 * X(6) + 3 * X(7) + 5 * X(8) + 14 * X(9) + 3 * X(10)<=700
       
         4 * X(1) + X(2) + 2 * X(3) + 9 * X(4) + 7 * X(5) + 9 * X(6) + 4 * X(7) + 7 * X(8) + 7 * X(9) + 11 * X(10)<=900

        .3 * X(1) ^ 3 + X(2) + .1 * X(3) + .1 * X(4) ^ 3 + .1 * X(5) + .1 * X(6) + .5 * X(7) ^ 5 + .1 * X(8) + .2 * X(9) ^ 2 + .1 * X(10)<=5000

         0<=X(i) <= 200, i=1, 2, 3, 4, 5, 6, 7, 8, 9, 10       
       
X(1) through X(10) are general integer variables.

The formulation above is loosely based on the formulation in Sharma [42, p.149].
                                     
The decision makers have determined that the weights are .5, .3, and .2, respectively.

X(11) through X(13) below are slack variables. 

One notes the following line 441, which includes the nonlinear numerators and denominators.   

One also notes the nonlinear constraint .3 * X(1) ^ 3 + X(2) + .1 * X(3) + .1 * X(4) ^ 3 + .1 * X(5) + .1 * X(6) + .5 * X(7) ^ 5 + .1 * X(8) + .2 * X(9) ^ 2 + .1 * X(10)<=5000.
   

0 DEFDBL A-Z

2 DEFINT K

3 DIM B(99), N(99), A(99), H(99), L(99), U(99), X(1111), D(111), P(111), PS(33)

12 FOR JJJJ = -32000 TO 32000 STEP .01

    14 RANDOMIZE JJJJ


    16 M = -1D+37
    51 FOR J40 = 1 TO 10
        55 A(J40) = RND * 200


    56 NEXT J40
    128 FOR I = 1 TO 6000


        129 FOR KKQQ = 1 TO 10


            130 X(KKQQ) = A(KKQQ)
        131 NEXT KKQQ
        133 FOR IPP = 1 TO (1 + FIX(RND * 3))


            181 j = 1 + FIX(RND * 10)


            183 r = (1 - RND * 2) * A(j)
            187 X(j) = A(j) + (RND ^ (RND * 10)) * r
        222 NEXT IPP
        246 FOR J47 = 1 TO 10
            247 X(J47) = INT(X(J47))
            248 IF X(J47) < 0 THEN 1670
            249 IF X(J47) > 200 THEN 1670
        251 NEXT J47
        255 REM


        301 X(11) = 700 - X(1) - X(2) - 7 * X(3) - X(4) - 3 * X(5) - 6 * X(6) - 3 * X(7) - 5 * X(8) - 14 * X(9) - 3 * X(10)
        303 X(12) = 900 - 4 * X(1) - X(2) - 2 * X(3) - 9 * X(4) - 7 * X(5) - 9 * X(6) - 4 * X(7) - 7 * X(8) - 7 * X(9) - 11 * X(10)
        305 X(13) = 5000 - .3 * X(1) ^ 3 - X(2) - .1 * X(3) - .1 * X(4) ^ 3 - .1 * X(5) - .1 * X(6) - .5 * X(7) ^ 5 - .1 * X(8) - .2 * X(9) ^ 2 - .1 * X(10)


        401 FOR J47 = 11 TO 13

            402 IF X(J47) < 0 THEN X(J47) = X(J47) ELSE X(J47) = 0


        403 NEXT J47


        441 POBA = .5 * ((X(1) + 2 * X(2) ^ 3 + 3 * X(3) + X(4) + 2 * X(5) + 7 * X(6) + 3 * X(7) + 3 * X(8) + 11 * X(9) + 7 * X(10)) / (X(2) ^ 3 + 5 * X(3) + X(6) + 2 * X(8) + 9 * X(10) + 3)) ^ 3 + .3 * ((X(1) + 7 * X(3) + 4 * X(4) ^ 3 + 3 * X(5) + 5 * X(7) + 13 * X(9) + 11 * X(10)) / (X(2) + 3 * X(4) ^ 3 + X(6) + 4 * X(8) + 6 * X(10) + 1)) ^ 2 + .2 * ((X(1) + 3 * X(3) + 5 * X(4) + 6 * X(5) + 7 * X(7) + 4 * X(9) ^ 5 + 7 * X(10) + 2) / (2 * X(1) + X(2) + 8 * X(3) + 6 * X(4) + 2 * X(5) + X(6) + 4 * X(7) + 3 * X(8) + 3 * X(9) ^ 5 + 7 * X(10) + 2)) ^ 3 + 1000000 * (X(11) + X(12) + X(13))


        466 P = POBA

        1111 IF P <= M THEN 1670


        1450 M = P
        1454 FOR KLX = 1 TO 13

            1455 A(KLX) = X(KLX)
        1456 NEXT KLX
        1557 GOTO 128

    1670 NEXT I
    1889 IF M < 3330000 THEN 1999


    1900 PRINT A(1), A(2), A(3), A(4), A(5)

    1907 PRINT A(6), A(7), A(8), A(9), A(10), A(11), A(12), A(13), M, JJJJ
   
1999 NEXT JJJJ


This BASIC computer program was run with QB64v1000-win [49]. The complete output through JJJJ =   -31893.840000017 is shown below:

21      0      0      0      2 
0      5      0       47       0
0      0     0      3332032.155555523
-31971.12000000462

21      0      0      0      2
0      5      0       47       0
0      0     0      3332032.155555523
-31914.98000001361

21      0      0      0      2
0      5      0       47       0
0      0     0      3332032.155555523
-31893.840000017

Above there is no rounding by hand; it is just straight copying by hand from the monitor screen. On a personal computer with a Pentium Dual-Core CPU E5200 @2.50GHz, 2.50 GHz, 960 MB of RAM and QB64v1000-win [49], the wall-clock time for obtaining the output through
JJJJ=  -31893.840000017 was 7 minutes, total, including the time for “Creating .EXE file.”             
                                                
Acknowledgment

I would like to acknowledge the encouragement of Roberta Clark and Tom Clark.

References

[1] Yuichiro Anzai (1974). On Integer Fractional Programming. Journal of the Operations Research Society of Japan, Volume 17, No. 1, March 1974, pp. 49-66. http://www..orsj.or.jp/~archiv/pdf/e_mag/Vol.17_01_049.pdf.
[2] Sjirk Boon. Solving systems of nonlinear equations. Sci. Math. Num-Analysis,1992, Newsgroup Article 3529.
[3] S. S. Chadha (2002). Fractional programming with absolute-value functions. European Journal of Operational Research 141 (2002) pp. 233-238.
[4] Ching-Ter Chang (2002). On the posynomial fractional programming problems. European Journal of Operational Research 143 (2002) pp. 42-52.
[5] Ching-Ter Chang (2006). Formulating the mixed integer fractional posynomial programming, European Journal of Operational Research 173 (2006) pp. 370-386.

[6] Ching-Ter Chang (2017). Fuzzy linearization strategy for multiple objective linear fractional programming with binary utility functions. Computers and Industrial Engineering 112 (2017) 437-446.

[7] Piya Chootinan, Anthony Chen (2006). Constraint Handling in genetic algorithms using a gradient-based repair method. Computers and Operations Research 33 (2006) 2263-2281.
[8] Amir Hossein Gandomi, Xin-She Yang, Amir Hossein Alavi (2011). Mixed variable structural optimization using Firefly Algorithm, Computers and Structures 89 (2011) 2325-2336.
[9] Amir Hossein Gandomi, Xin-She Yang, Siamak Taratahari, Amir Hossein Alavi (2013). Firefly Algorithm with Chaos, Communications in Nonlinear Science and Numerical Simulation 18 (2013) 89-98.
[10] Amir Hossein Gandomi, Xin-She Yang, Amir Hossein Alavi (2013). Cuckoo search algorithm: a metaheuristicapproach to solve structural optimization problem. Engineering with Computers (2013) 29:17-35.
[11] Amir Hossein Gandomi, Xin-She Yang, Amir Hossein Alavi (2013). Erratum to: Cuckoo search algorithm: a metaheuristicapproach to solve structural optimization problem. Engineering with Computers (2013) 29:245.
[12] Chrysanthos E. Gounaris, Christodoulos A. Floudas. Tight convex underestimators for Csquare-continuous problems: II. multivariate functions. Journal of Global Optimization (2008) 42, pp. 69-89.

[13] E. L. Hannan (1981), Linear programming with multiple fuzzy goals, Fuzzy Sets and Systems 6 (1981) 235-246.     

[14] Xue-Ping Hou, Pei-Ping Shen, Chun-Feng Wang (2014). Global Minimization for Generalized Polynomial Fractional Program. Mathmatical Problems in Engineering, Volume 2014. Hindawi Publishing Company.

[15] James P. Ignizio,  Linear Programming in Single- and Multiple-Objective Systems.  Prentice-Hall, Englewood Cliffs, NJ, 1982.

[16] James P. Ignizio, Stephen C. Daniels (1983). Fuzzy multicriteria integer programming via fuzzy generalized networks.  Fuzzy Sets and Systems 10 (1983) 261-270.

[17] James P. Ignizio,  Tom M. Cavalier, Linear Programming.  Prentice-Hall, Englewood Cliffs, NJ, 1994.

[18] Majid Jaberipour, Esmaile Khorram (2010). Solving the sum of ratios problems by a harmony search algorithm. Journal of Computational and Applied Mathematics 234 (2010) 733-742.

[19] Sanjay Jain, Kailash Lachhwani (2010). Linear plus fractional multiobjective programming problem with homogeneous constraints using fuzzy approach. Iranian Journal of Operations Research, Vol. 2, No. 1, 2010, pp. 41-49.

[20] Hongwei Jiao, Zhankui Wang, Yongqiang Chen (2013). Global Optimization Algorithm for Sum of Generalized Polynomial Ratios Problems. Applied Mathematical Modelling 37 (2013) 187-197.

[21] Ali Husseinzadeh Kashan (2011). An effective algorithm for constrained global optimization and application to mechanical engineering design: League championship algorithm (LCA). Computer-Aided Design 43 (2011) 1769-1792.

[22] Ali Husseinzadeh Kashan (2015). An effective algorithm for constrained optimization based on optics inspired optimization (OIO). Computer-Aided Design 63 (2015) 52-71.

[23] K. Lachhwani (2012). Fuzzy Goal Programming Approach to Multi Objectve Quadratic Programming Problem. Proceedings of the National Academy of Sciences, India, Section A Physical Sciences (October-December 2012) 82 (4): 317-322.
https://link.springer.com/journal/40010

[24] K. Lachhwani (2014). FGP approach to multi objectve quadratic fractional programming problem. International Journal of Industrial Mathematics, vol. 6, No.1, pp. 49-57. Available online at http://ijim.srbiau.ac.ir/

[25] Han-Lin Li, Jung-Fa Tsai, Christodoulos A. Floudas (2008). Convex underestimating for posynomial functions of postive variables. Optimization Letters 2, 333-340 (2008).
[26] Han-Lin Li, Jung-Fa Tsai (2008). A distributed computational algorithm for solving portfolio problems with integer variables. European Journal of Operational Research 186 (2008) pp. 882-891.
[27] Han-Lin Li, Shu-Cherng Fang, Yao-Huei Huang, Tiantian Nie (2016). An enhanced logarithmic method for signomial programming with discrete variables. European Journal of Operational Research 255 (2016) pp. 922-934.
[28] Ming-Hua Lin, Jung-Fa Tsai (2011). Finding multiple optimal solutions of signomial discrete programming problems with free variables, Optimization and Engineering (2011) 12:425-443.
[29] Ming-Hua Lin, Jung-Fa Tsai (2014). A deterministic global approach for mixed-discrete structural optimization, Engineering Optimization (2014) 46:7, pp. 863-879.
[30] Hao-Chun Lu, Han-Lin Li, Chrysanthos E. Gounaris, Christodoulos A. Floudas (2010). Convex relaxation for solving posynomial problems. Journal of Global Optimization (2010) 46, pp. 147-154.
[31] Hao-Chun Lu (2012). An efficient convexification method for solving generalized geometric problems. Journal of Industrial and Management Optimization, Volume 8, Number 2, May 2012, pp. 429-455.
[32] Hao-Chun Lu (2017). Improved logarithnic linearizing method for optimization problems with free-sign pure discrete signomial terms. Journal of Global Optimization (2017) 68, pp. 95-123.

[33] Kenneth R. MacCrimmon (1973). An overview of multiple objective decision making. In James L. Cochrane, Milan Zeleny (editors), Multiple Criteria Decision Making. University of South Carolina Press, Columbia.
[34] Mathworks. Solving a mixed integer engineering design problem using the genetic algorithm – MATLAB & Simulink Example.
https://www.mathworks.com/help/gads/examples/solving-a-mixed-integer-engineering-design-problem-using-the-genetic-algorithm.html/
[35] Microsoft Corp., BASIC, Second Edition (May 1982), Version 1.10. Boca Raton, Florida: IBM Corp., Personal Computer, P. O. Box 1328-C, Boca Raton, Florida 33432, 1981.

[36] Katta Murty, Operations Research: Deterministic Optimization Models. Prentice-Hall, 1995.

[37] Sinan Melih Nigdeli, Gebrail Bekdas, Xin-She Yang (2016). Application of the Flower Pollination Algoritm in Structural Engineering. Springer International Publishing Switzerland 2016. http://www.springer.com/cda/content/document/cda…/
[38] Gideon Oron (1979) An algorithm for optimizing nonlinear contrained zero-one problems to improve wastewater treatment, Engineering Optimization, 4:2, 109-114.
[39] Osama Abdel Raouf, Ibraham M. Hezam (2014). Solving Fractional Programming Problems Based on Swarm Intelligence. Journal of Industrial Engineering International (2014) 10:56.
[40] H. S. Ryoo, N. V. Sahinidis (1995). Global optimization of nonconvex NLP and MINLP with applications in process design. Computers and Chemical Engineering Vol. 19 (5) (1995) pp. 551-566.
[41] C. R. Seshan, V. G. Tikekar (1980) Algorithms for Fractional Programming. Journal of the Indian Institute of Science 62 (B), Feb. 1980, Pp. 9-16.

[42] Vikas Sharma (2012), Multobjective integer nonlinear fractional programming problem: A cutting plane approach.  OPSEARCH  (Ap-June 2012)  49 (2) 133-153.

[43] Pei-Ping Shen, Yun-Peng Duan, Yong-Gang Pei. A simplicial branch and duality boundalgorithm for the sum of convex-convex ratios problem. Journal of Computational and Applied Mathematics 223 (2009) 145-158.
[44] P. B. Thanedar, G. N. Vanderplaats (1995). Survey of discrete variable optimization for structural design, Journal of Structural Engineering, 121 (2), 301-306 (1995).
[45] Jung-Fa Tsai (2005). Global optimization of nonlinear fractional programming problems in engineering design. Engineering Optimization (2005) 37:4, pp. 399-409.
[46] Jung-Fa Tsai, Ming-Hua Lin (2007). Finding all solutions of systems of nonlinear equations with free variables. Engineering Optimization (2007) 39:6, pp. 649-659
[47] Jung-Fa Tsai, Ming-Hua Lin, Yi-Chung Hu (2007). On generalized geometric programming problems with non-positive variables. European Journal of Operational Research 178 (2007) pp. 10-19.
[48] Jung-Fa Tsai, Ming-Hua Lin (2008). Global optimization of signomial mixed-integer nonlinear programming with free variables. Journal of Global Optimization (2008) 42 pp. 39-49.
[49] Wikipedia, QB64, https://en.wikipedia.org/wiki/QB64.
[50] Taeyong Yang, James P. Ignizio, and Hyun-Joon Kim, Fuzzy programming with nonlinear membership functions: Piecewise linear approximation. Fuzzy Sets and Systems 41 (1991) 39-53.

[51] H.-J. Zimmermann (1978), Fuzzy programming and linear programming with several objective functions. Fuzzy Sets and Systems 1 (1978) 45-55.

Saturday, March 3, 2018

Fuzzy Programming with Two Objective Functions Using the Product Operator


Jsun Yui Wong

Using the product operator, Zimmermann has converted his Example 2.1 problem with two goals [49, p. 46] to the following problem, Zimmermann [49, p. 52]:

Maximize   (1 / 238) * (-2 * X(1) ^ 2 + 3 * X(1) * X(2) + 13 * X(1) - 11 * X(2) + 2 * X(2) ^ 2 - 21)

subject to

        - X(1) + 3 * X(2)<=21,

         X(1) + 3 * X(2)<=27,

        4 * X(1) + 3 * X(2)<=45,

         3 * X(1) + X(2)<=30,

        X(1), X(2)>=0.

The computer program listed below seeks to solve the model immediately above.  X(3) through X(6) below are slack variables. 


0 DEFDBL A-Z

2 DEFINT K

3 DIM B(99), N(99), A(99), H(99), L(99), U(99), X(1111), D(111), P(111), PS(33)

12 FOR JJJJ = -32000 TO 32000 STEP .01


    14 RANDOMIZE JJJJ
    16 M = -1D+37
    51 FOR J40 = 1 TO 2
     
        55 A(J40) = RND * 2


    56 NEXT J40



    128 FOR I = 1 TO 1000


        129 FOR KKQQ = 1 TO 2



            130 X(KKQQ) = A(KKQQ)
        131 NEXT KKQQ
        133 FOR IPP = 1 TO (1 + FIX(RND * 2))


            181 j = 1 + FIX(RND * 2)


            183 r = (1 - RND * 2) * A(j)
            187 X(j) = A(j) + (RND ^ (RND * 10)) * r
        222 NEXT IPP


        246 FOR J47 = 1 TO 2

            247 IF X(J47) < 0 THEN 1670
           

        249 NEXT J47

       

        318 X(3) = 21 + X(1) - 3 * X(2)


        321 X(4) = 27 - X(1) - 3 * X(2)



        322 X(5) = 45 - 4 * X(1) - 3 * X(2)
        323 X(6) = 30 - 3 * X(1) - X(2)


        337 FOR j44 = 3 TO 6


            338 IF X(j44) < 0 THEN X(j44) = X(j44) ELSE X(j44) = 0


        339 NEXT j44
        433 POBA = (1 / 238) * (-2 * X(1) ^ 2 + 3 * X(1) * X(2) + 13 * X(1) - 11 * X(2) + 2 * X(2) ^ 2 - 21) + 1000000 * (X(3) + X(4) + X(5) + X(6))


        466 P = POBA

        1111 IF P <= M THEN 1670


        1450 M = P

        1454 FOR KLX = 1 TO 6

            1455 A(KLX) = X(KLX)
        1456 NEXT KLX
        1557 GOTO 128

    1670 NEXT I
    1889 IF M < .5556 THEN 1999

    1900 PRINT A(1), A(2), A(3), A(4), A(5)
    1901 PRINT A(6), M, JJJJ

1999 NEXT JJJJ

This BASIC computer program was run with QB64v1000-win [47]. The complete output through JJJJ =  -31999.87000000002 is shown below:

5.630904744562785      7.123031751812368      0
0      0
0      .555616548152143       -31999.99

5.683612687394603      7.105462437530837      0
0      0
0      .5556691346397322      -31999.98

5.730704803906347      7.089765065363786      0
0      0
0      .555661265347894       -31999.87000000002

Above there is no rounding by hand; it is just straight copying by hand from the monitor screen. On a personal computer with a Pentium Dual-Core CPU E5200 @2.50GHz, 2.50 GHz, 960 MB of RAM and QB64v1000-win [47], the wall-clock time for obtaining the output through
JJJJ=  -31999.87000000002 was 2 or 3 seconds, not including the time for “Creating .EXE file.” One can compare the computational results above to those on page 52 of Zimmermann [49].             

Acknowledgment

I would like to acknowledge the encouragement of Roberta Clark and Tom Clark.

References

[1] Yuichiro Anzai (1974). On Integer Fractional Programming. Journal of the Operations Research Society of Japan, Volume 17, No. 1, March 1974, pp. 49-66. http://www..orsj.or.jp/~archiv/pdf/e_mag/Vol.17_01_049.pdf.
[2] Sjirk Boon. Solving systems of nonlinear equations. Sci. Math. Num-Analysis,1992, Newsgroup Article 3529.
[3] S. S. Chadha (2002). Fractional programming with absolute-value functions. European Journal of Operational Research 141 (2002) pp. 233-238.
[4] Ching-Ter Chang (2002). On the posynomial fractional programming problems. European Journal of Operational Research 143 (2002) pp. 42-52.
[5] Ching-Ter Chang (2006). Formulating the mixed integer fractional posynomial programming, European Journal of Operational Research 173 (2006) pp. 370-386.

[6] Ching-Ter Chang (2017). Fuzzy linearization strategy for multiple objective linear fractional programming with binary utility functions. Computers and Industrial Engineering 112 (2017) 437-446.

[7] Piya Chootinan, Anthony Chen (2006). Constraint Handling in genetic algorithms using a gradient-based repair method. Computers and Operations Research 33 (2006) 2263-2281.
[8] Amir Hossein Gandomi, Xin-She Yang, Amir Hossein Alavi (2011). Mixed variable structural optimization using Firefly Algorithm, Computers and Structures 89 (2011) 2325-2336.
[9] Amir Hossein Gandomi, Xin-She Yang, Siamak Taratahari, Amir Hossein Alavi (2013). Firefly Algorithm with Chaos, Communications in Nonlinear Science and Numerical Simulation 18 (2013) 89-98.
[10] Amir Hossein Gandomi, Xin-She Yang, Amir Hossein Alavi (2013). Cuckoo search algorithm: a metaheuristicapproach to solve structural optimization problem. Engineering with Computers (2013) 29:17-35.
[11] Amir Hossein Gandomi, Xin-She Yang, Amir Hossein Alavi (2013). Erratum to: Cuckoo search algorithm: a metaheuristicapproach to solve structural optimization problem. Engineering with Computers (2013) 29:245.
[12] Chrysanthos E. Gounaris, Christodoulos A. Floudas. Tight convex underestimators for Csquare-continuous problems: II. multivariate functions. Journal of Global Optimization (2008) 42, pp. 69-89.

[13] E. L. Hannan (1981), Linear programming with multiple fuzzy goals, Fuzzy Sets and Systems 6 (1981) 235-246.     

[14] Xue-Ping Hou, Pei-Ping Shen, Chun-Feng Wang (2014). Global Minimization for Generalized Polynomial Fractional Program. Mathmatical Problems in Engineering, Volume 2014. Hindawi Publishing Company.

[15] James P. Ignizio,  Linear Programming in Single- and Multiple-Objective Systems.  Prentice-Hall, Englewood Cliffs, NJ, 1982.

[16] James P. Ignizio, Stephen C. Daniels (1983). Fuzzy multicriteria integer programming via fuzzy generalized networks.  Fuzzy Sets and Systems 10 (1983) 261-270.

[17] James P. Ignizio,  Tom M. Cavalier, Linear Programming.  Prentice-Hall, Englewood Cliffs, NJ, 1994.

[18] Majid Jaberipour, Esmaile Khorram (2010). Solving the sum of ratios problems by a harmony search algorithm. Journal of Computational and Applied Mathematics 234 (2010) 733-742.

[19] Sanjay Jain, Kailash Lachhwani (2010). Linear plus fractional multiobjective programming problem with homogeneous constraints using fuzzy approach. Iranian Journal of Operations Research, Vol. 2, No. 1, 2010, pp. 41-49.

[20] Hongwei Jiao, Zhankui Wang, Yongqiang Chen (2013). Global Optimization Algorithm for Sum of Generalized Polynomial Ratios Problems. Applied Mathematical Modelling 37 (2013) 187-197.

[21] Ali Husseinzadeh Kashan (2011). An effective algorithm for constrained global optimization and application to mechanical engineering design: League championship algorithm (LCA). Computer-Aided Design 43 (2011) 1769-1792.

[22] Ali Husseinzadeh Kashan (2015). An effective algorithm for constrained optimization based on optics inspired optimization (OIO). Computer-Aided Design 63 (2015) 52-71.

[23] K. Lachhwani (2012). Fuzzy Goal Programming Approach to Multi Objectve Quadratic Programming Problem. Proceedings of the National Academy of Sciences, India, Section A Physical Sciences (October-December 2012) 82 (4): 317-322.
https://link.springer.com/journal/40010

[24] K. Lachhwani (2014). FGP approach to multi objectve quadratic fractional programming problem. International Journal of Industrial Mathematics, vol. 6, No.1, pp. 49-57. Available online at http://ijim.srbiau.ac.ir/

[25] Han-Lin Li, Jung-Fa Tsai, Christodoulos A. Floudas (2008). Convex underestimating for posynomial functions of postive variables. Optimization Letters 2, 333-340 (2008).
[26] Han-Lin Li, Jung-Fa Tsai (2008). A distributed computational algorithm for solving portfolio problems with integer variables. European Journal of Operational Research 186 (2008) pp. 882-891.
[27] Han-Lin Li, Shu-Cherng Fang, Yao-Huei Huang, Tiantian Nie (2016). An enhanced logarithmic method for signomial programming with discrete variables. European Journal of Operational Research 255 (2016) pp. 922-934.
[28] Ming-Hua Lin, Jung-Fa Tsai (2011). Finding multiple optimal solutions of signomial discrete programming problems with free variables, Optimization and Engineering (2011) 12:425-443.
[29] Ming-Hua Lin, Jung-Fa Tsai (2014). A deterministic global approach for mixed-discrete structural optimization, Engineering Optimization (2014) 46:7, pp. 863-879.
[30] Hao-Chun Lu, Han-Lin Li, Chrysanthos E. Gounaris, Christodoulos A. Floudas (2010). Convex relaxation for solving posynomial problems. Journal of Global Optimization (2010) 46, pp. 147-154.
[31] Hao-Chun Lu (2012). An efficient convexification method for solving generalized geometric problems. Journal of Industrial and Management Optimization, Volume 8, Number 2, May 2012, pp. 429-455.
[32] Hao-Chun Lu (2017). Improved logarithnic linearizing method for optimization problems with free-sign pure discrete signomial terms. Journal of Global Optimization (2017) 68, pp. 95-123.

[33] Kenneth R. MacCrimmon (1973). An overview of multiple objective decision making. In James L. Cochrane, Milan Zeleny (editors), Multiple Criteria Decisiom Making. University of South Carolina Press, Columbia.
[34] Mathworks. Solving a mixed integer engineering design problem using the genetic algorithm – MATLAB & Simulink Example.
https://www.mathworks.com/help/gads/examples/solving-a-mixed-integer-engineering-design-problem-using-the-genetic-algorithm.html/
[35] Microsoft Corp., BASIC, Second Edition (May 1982), Version 1.10. Boca Raton, Florida: IBM Corp., Personal Computer, P. O. Box 1328-C, Boca Raton, Florida 33432, 1981.
[36] Sinan Melih Nigdeli, Gebrail Bekdas, Xin-She Yang (2016). Application of the Flower Pollination Algoritm in Structural Engineering. Springer International Publishing Switzerland 2016. http://www.springer.com/cda/content/document/cda…/
[37] Gideon Oron (1979) An algorithm for optimizing nonlinear contrained zero-one problems to improve wastewater treatment, Engineering Optimization, 4:2, 109-114.
[38] Osama Abdel Raouf, Ibraham M. Hezam (2014). Solving Fractional Programming Problems Based on Swarm Intelligence. Journal of Industrial Engineering International (2014) 10:56.
[39] H. S. Ryoo, N. V. Sahinidis (1995). Global optimization of nonconvex NLP and MINLP with applications in process design. Computers and Chemical Engineering Vol. 19 (5) (1995) pp. 551-566.
[40] c. R. Seshan, V. G. Tikekar (1980) Algorithms for Fractional Programming. Journal of the Indian Institute of Science 62 (B), Feb. 1980, Pp. 9-16.
[41] Pei-Ping Shen, Yun-Peng Duan, Yong-Gang Pei. A simplicial branch and duality boundalgorithm for the sum of convex-convex ratios problem. Journal of Computational and Applied Mathematics 223 (2009) 145-158.
[42] P. B. Thanedar, G. N. Vanderplaats (1995). Survey of discrete variable optimization for structural design, Journal of Structural Engineering, 121 (2), 301-306 (1995).
[43] Jung-Fa Tsai (2005). Global optimization of nonlinear fractional programming problems in engineering design. Engineering Optimization (2005) 37:4, pp. 399-409.
[44] Jung-Fa Tsai, Ming-Hua Lin (2007). Finding all solutions of systems of nonlinear equations with free variables. Engineering Optimization (2007) 39:6, pp. 649-659
[45] Jung-Fa Tsai, Ming-Hua Lin, Yi-Chung Hu (2007). On generalized geometric programming problems with non-positive variables. European Journal of Operational Research 178 (2007) pp. 10-19.
[46] Jung-Fa Tsai, Ming-Hua Lin (2008). Global optimization of signomial mixed-integer nonlinear programming with free variables. Journal of Global Optimization (2008) 42 pp. 39-49.
[47] Wikipedia, QB64, https://en.wikipedia.org/wiki/QB64.
[48] Taeyong Yang, James P. Ignizio, and Hyun-Joon Kim, Fuzzy programming with nonlinear membership functions: Piecewise linear approximation. Fuzzy Sets and Systems 41 (1991) 39-53.
[49] H.-J. Zimmermann (1978), Fuzzy programming and linear programming with several objective functions. Fuzzy Sets and Systems 1 (1978) 45-55.