IDEAS home Printed from https://ideas.repec.org/a/hin/complx/7053710.html
   My bibliography  Save this article

Shrimp Feed Formulation via Evolutionary Algorithm with Power Heuristics for Handling Constraints

Author

Listed:
  • Rosshairy Abd. Rahman
  • Graham Kendall
  • Razamin Ramli
  • Zainoddin Jamari
  • Ku Ruhana Ku-Mahamud

Abstract

Formulating feed for shrimps represents a challenge to farmers and industry partners. Most previous studies selected from only a small number of ingredients due to cost pressures, even though hundreds of potential ingredients could be used in the shrimp feed mix. Even with a limited number of ingredients, the best combination of the most appropriate ingredients is still difficult to obtain due to various constraint requirements, such as nutrition value and cost. This paper proposes a new operator which we call Power Heuristics, as part of an Evolutionary Algorithm (EA), which acts as a constraint handling technique for the shrimp feed or diet formulation. The operator is able to choose and discard certain ingredients by utilising a specialized search mechanism. The aim is to achieve the most appropriate combination of ingredients. Power Heuristics are embedded in the EA at the early stage of a semirandom initialization procedure. The resulting combination of ingredients, after fulfilling all the necessary constraints, shows that this operator is useful in discarding inappropriate ingredients when a crucial constraint is violated.

Suggested Citation

  • Rosshairy Abd. Rahman & Graham Kendall & Razamin Ramli & Zainoddin Jamari & Ku Ruhana Ku-Mahamud, 2017. "Shrimp Feed Formulation via Evolutionary Algorithm with Power Heuristics for Handling Constraints," Complexity, Hindawi, vol. 2017, pages 1-12, November.
    • Handle: RePEc:hin:complx:7053710
    DOI: 10.1155/2017/7053710
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/8503/2017/7053710.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/8503/2017/7053710.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2017/7053710?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Rehman, Tahir & Romero, Carlos, 1984. "Multiple-criteria decision-making techniques and their role in livestock ration formulation," Agricultural Systems, Elsevier, vol. 15(1), pages 23-49.
    2. Leonard W. Swanson & James G. Woodruff, 1964. "A Sequential Approach to the Feed-Mix Problem," Operations Research, INFORMS, vol. 12(1), pages 89-109, February.
    3. Castrodeza, Carmen & Lara, Pablo & Pena, Teresa, 2005. "Multicriteria fractional model for feed formulation: economic, nutritional and environmental criteria," Agricultural Systems, Elsevier, vol. 86(1), pages 76-96, October.
    4. Frederick V. Waugh, 1951. "The Minimum-Cost Dairy FeedAn Application of "Linear Programming"," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 33(3), pages 299-310.
    5. Lara, Pablo & Romero, Carlos, 1994. "Relaxation of nutrient requirements on livestock rations through interactive multigoal programming," Agricultural Systems, Elsevier, vol. 45(4), pages 443-453.
    6. Lara, Pablo, 1993. "Multiple objective fractional programming and livestock ration formulation: A case study for dairy cow diets in Spain," Agricultural Systems, Elsevier, vol. 41(3), pages 321-334.
    7. Joyce T. Chen, 1973. "Quadratic Programming for Least-Cost Feed Formulations under Probabilistic Protein Constraints," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 55(2), pages 175-183.
    8. Sabir A. Rahman & F. E. Bender, 1971. "Linear Programming Approximation of Least-Cost Feed Mixes with Probability Restrictions," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 53(4), pages 612-618.
    9. John J. Glen, 1980. "A Mathematical Programming Approach to Beef Feedlot Optimization," Management Science, INFORMS, vol. 26(5), pages 524-535, May.
    10. Mohr, Graeme M., 1972. "The Bulk Constraint and Computer Formulations of Least-Cost Feed Mixes," Review of Marketing and Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 40(01), pages 1-14, March.
    11. F Polimeno & T Rehman & H Neal & C M Yates, 1999. "Integrating the use of linear and dynamic programming methods for dairy cow diet formulation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(9), pages 931-942, September.
    12. Munford, Alan G., 1996. "The use of iterative linear programming in practical applications of animal diet formulation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 42(2), pages 255-261.
    13. Cadenas, Jose M. & Pelta, David A. & Pelta, Hector R. & Verdegay, Jose L., 2004. "Application of fuzzy optimization to diet problems in Argentinean farms," European Journal of Operational Research, Elsevier, vol. 158(1), pages 218-228, October.
    14. Rehman, Tahir & Romero, Carlos, 1987. "Goal programming with penalty functions and livestock ration formulation," Agricultural Systems, Elsevier, vol. 23(2), pages 117-132.
    15. C. van de Panne & W. Popp, 1963. "Minimum-Cost Cattle Feed Under Probabilistic Protein Constraints," Management Science, INFORMS, vol. 9(3), pages 405-430, April.
    16. T Peña & P Lara & C Castrodeza, 2009. "Multiobjective stochastic programming for feed formulation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(12), pages 1738-1748, December.
    17. Munford, Alan G., 1989. "A microcomputer system for formulating animal diets which may involve liquid raw materials," European Journal of Operational Research, Elsevier, vol. 41(3), pages 270-276, August.
    18. Tozer, P. R. & Stokes, J. R., 2001. "A multi-objective programming approach to feed ration balancing and nutrient management," Agricultural Systems, Elsevier, vol. 67(3), pages 201-215, March.
    19. Poojari, Chandra A. & Varghese, Boby, 2008. "Genetic Algorithm based technique for solving Chance Constrained Problems," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1128-1154, March.
    20. Eleanor Eckstein, 1970. "Communication to the Editor--Is the "Diet Problem" Identical to the "Menu Planning Problem"?," Management Science, INFORMS, vol. 16(9), pages 527-528, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Weiqin Ying & Bin Wu & Yu Wu & Yali Deng & Hainan Huang & Zhenyu Wang, 2019. "Efficient Conical Area Differential Evolution with Biased Decomposition and Dual Populations for Constrained Optimization," Complexity, Hindawi, vol. 2019, pages 1-18, February.
    2. Ahmad Al Eissa & Peng Chen & Paul B. Brown & Jen‐Yi Huang, 2022. "Effects of feed formula and farming system on the environmental performance of shrimp production chain from a life cycle perspective," Journal of Industrial Ecology, Yale University, vol. 26(6), pages 2006-2019, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Babic, Zoran & Peric, Tunjo, 2011. "Optimization of livestock feed blend by use of goal programming," International Journal of Production Economics, Elsevier, vol. 130(2), pages 218-223, April.
    2. Torres-Rojo, J. M., 2001. "Risk management in the design of a feeding ration: a portfolio theory approach," Agricultural Systems, Elsevier, vol. 68(1), pages 1-20, April.
    3. T Peña & P Lara & C Castrodeza, 2009. "Multiobjective stochastic programming for feed formulation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(12), pages 1738-1748, December.
    4. J. Žgajnar & L. Juvančič & S. Kavčič, 2009. "Combination of linear and weighted goal programming with penalty function in optimisation of a daily dairy cow ration," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 55(10), pages 492-500.
    5. Castrodeza, Carmen & Lara, Pablo & Pena, Teresa, 2005. "Multicriteria fractional model for feed formulation: economic, nutritional and environmental criteria," Agricultural Systems, Elsevier, vol. 86(1), pages 76-96, October.
    6. Zgajnar, Jaka & Kavcic, Stane, 2011. "Weighted Goal Programming and Penalty Functions: Whole-farm Planning Approach Under Risk," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 118033, European Association of Agricultural Economists.
    7. François Dubeau & Pierre-Olivier Julien & Candido Pomar, 2011. "Formulating diets for growing pigs: economic and environmental considerations," Annals of Operations Research, Springer, vol. 190(1), pages 239-269, October.
    8. J. Contreras & H. Lara & G. Nouel-Borges, 2019. "A mixed integer nonlinear programming model for biomass production," Operational Research, Springer, vol. 19(1), pages 39-57, March.
    9. Andrés Weintraub & Carlos Romero, 2006. "Operations Research Models and the Management of Agricultural and Forestry Resources: A Review and Comparison," Interfaces, INFORMS, vol. 36(5), pages 446-457, October.
    10. Jaka ZGAJNAR & Stane KAVCIC, 2016. "Optimal allocation of production resources under uncertainty: Application of the muliticriteria approach," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 62(12), pages 556-565.
    11. Notte, Gastón & Pedemonte, Martín & Cancela, Héctor & Chilibroste, Pablo, 2016. "Resource allocation in pastoral dairy production systems: Evaluating exact and genetic algorithms approaches," Agricultural Systems, Elsevier, vol. 148(C), pages 114-123.
    12. De Matteis, Maria C. & Yu, T. Edward & Boyer, Christopher N. & DeLong, Karen L. & Smith, Jason, 2018. "Economic and environmental implications of incorporating distillers’ dried grains with solubles in feed rations of growing and finishing swine in Argentina," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 21(6), July.
    13. Tozer, P. R. & Stokes, J. R., 2001. "A multi-objective programming approach to feed ration balancing and nutrient management," Agricultural Systems, Elsevier, vol. 67(3), pages 201-215, March.
    14. Lim, Teng & Massey, Ray & McCann, Laura & Canter, Timothy & Omura, Seabrook & Willett, Cammy & Roach, Alice & Key, Nigel & Dodson, Laura, 2023. "Increasing the Value of Manure for Farmers," USDA Miscellaneous 333552, United States Department of Agriculture.
    15. Giada Spaccapanico Proietti & Mariagiulia Matteucci & Stefania Mignani & Bernard P. Veldkamp, 2024. "Chance-Constrained Automated Test Assembly," Journal of Educational and Behavioral Statistics, , vol. 49(1), pages 92-120, February.
    16. Zgajnar, Jaka & Kavcic, Stane, 2012. "Farm management support based on mathematical programming; an example of fertilization planning," Agroeconomia Croatica, Croatian Society of Agricultural Economists, vol. 2(1), pages 1-8, March.
    17. Miguel A. Lejeune & François Margot, 2016. "Solving Chance-Constrained Optimization Problems with Stochastic Quadratic Inequalities," Operations Research, INFORMS, vol. 64(4), pages 939-957, August.
    18. Xu, M. & Zhuan, X., 2013. "Optimal planning for wind power capacity in an electric power system," Renewable Energy, Elsevier, vol. 53(C), pages 280-286.
    19. Zare M., Yahia & Daneshmand, Ahmad, 1995. "A linear approximation method for solving a special class of the chance constrained programming problem," European Journal of Operational Research, Elsevier, vol. 80(1), pages 213-225, January.
    20. White, Robin R. & Brady, Michael, 2014. "Can consumers’ willingness to pay incentivize adoption of environmental impact reducing technologies in meat animal production?," Food Policy, Elsevier, vol. 49(P1), pages 41-49.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:complx:7053710. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.