Rosshairy Abd. Rahman
Shrimp feed formulation via evolutionary algorithm with power heuristics for handling constraints
Rahman, Rosshairy Abd.; Kendall, Graham; Ramli, Razamin; Jamari, Zainoddin; Ku-Mahamud, Ku Ruhana
Graham Kendall firstname.lastname@example.org
Ku Ruhana Ku-Mahamud
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.
|Journal Article Type||Article|
|Publication Date||Nov 26, 2017|
|Publisher||Hindawi Publishing Corporation|
|Peer Reviewed||Peer Reviewed|
|APA6 Citation||Rahman, R. A., Kendall, G., Ramli, R., Jamari, Z., & Ku-Mahamud, K. R. (2017). Shrimp feed formulation via evolutionary algorithm with power heuristics for handling constraints. Complexity, 2017, doi:10.1155/2017/7053710|
|Copyright Statement||Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by/4.0|
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by/4.0
You might also like
Is Evolutionary Computation evolving fast enough?
An iterated local search algorithm for the team orienteering problem with variable profits