Cost optimization of feed mixes by genetic algorithms

Şahman, Mehmet Akif; Çunkaş, Mehmet; İnal, Şeref; İnal, Fatma; Çoşkun, Behiç; Taşkıran, Uğur

doi:10.1016/j.advengsoft.2009.04.003

Cited by 32 publications

(17 citation statements)

References 8 publications

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“…Şahman et al [21] produced a good solution for a cattle feed problem with a few constraints, which aimed to obtain a zero value for their EA penalty function. Şahman et al [21] also worked on the feed formulation problem for poultry, which had many nutrient constraints.…”

Section: Heuristic and Metaheuristic Approachesmentioning

confidence: 99%

“…Şahman et al [21] also worked on the feed formulation problem for poultry, which had many nutrient constraints. Altun and Şahman [30] explored a PSO (Particle Swarm Optimization) approach to solve nonlinear constraints of nutrients.…”

Section: Heuristic and Metaheuristic Approachesmentioning

confidence: 99%

“…In comparison to a Genetic Algorithm, the PSO solution is superior, with respect to achieving lower penalty values. However, both [21,30] did not consider a ratio constraint in their animal feed formulation problems.…”

Section: Heuristic and Metaheuristic Approachesmentioning

confidence: 99%

“…The problem dataset has several new constraints in addition to those currently experienced by the DOF. Previous studies [20,21] have obtained a ratio of ingredients based on a set of preselected ingredients. However, the combination of some ingredients is sometimes unable to return a feasible solution as each ingredient has a minimum quantity requirement.…”

Section: Introductionmentioning

confidence: 99%

“…However, the combination of some ingredients is sometimes unable to return a feasible solution as each ingredient has a minimum quantity requirement. Therefore, in order to choose only the appropriate ingredients, a semirandom initialization operation [21] acts as a way of precontrolling the nutrient requirement. Our work utilises this semirandom initialization, improving upon it with a new alternative initialization operator based on the work in [22].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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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.

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Section: Heuristic and Metaheuristic Approachesmentioning

confidence: 99%

Section: Heuristic and Metaheuristic Approachesmentioning

confidence: 99%

Section: Heuristic and Metaheuristic Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations