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

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  • 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
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    References listed on IDEAS

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    Cited by:

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    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.

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