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An Efficient Evolutionary Optimizer for Solving Complex Dairy Feed Optimization Problems by Rajeev Das; Kedar Nath Das; Saurabh Mallik is a Agricultural and Biological Sciences article available to read on EtoBox.

What is An Efficient Evolutionary Optimizer for Solving Complex Dairy Feed Optimization Problems about?

Dairy feed ration optimization is one of the most debated problems of the present time. The presence of linear and non-linear constraints, along with their various potential blends, turns the problem into Non-deterministic Polynomial (NP) hard. This triggers the need for efficient evolutionary algorithms to figure out feed mix at minimum cost and maximum shelf life for dairy cattle. Despite its exclusive importance in a real-life scenario, few papers have developed algorithms to solve this problem. Therefore, an attempt is made in this study to introduce a Self-adaptive multi-population approach with the recently proposed Quadratic Approximation based Jaya (JaQA) algorithm (Das et al., 2022), where care has been taken for the maintenance of diversity in each of its sub-population along with enhancement of convergence. However, considering the difficulty in setting the size of the sub-population, the Self-adaptive Multi-Population Quadratic Approximation guided Jaya (SMP-JaQA) follows an automated learning approach based on the past data to step in the next generation, hence making it a user-friendly approach. The performance of this approach is tested on problems in the benchmark s

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Author

Rajeev Das; Kedar Nath Das; Saurabh Mallik

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Agricultural and Biological Sciences (Physical Sciences)