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<p>Optimizing animal feed rations is crucial for enhancing farm profitability, animal health, and production efficiency. Earlier review studies lacked a focus on feed optimization across diverse animal types, including farm animals, poultry, and aquaculture. To address this gap, this literature review examines prominent publications from the past four decades, sourced from multiple electronic databases. The study explores the application of classical techniques, primarily linear programming, and metaheuristic algorithms for ration formulation. Strengths, limitations, and suitability of these methods are analysed, along with emerging trends such as single/multi-objective optimization (considering factors beyond cost, like animal welfare and environmental impact) and species-specific models. Additionally, the integration of machine learning with optimization techniques is reviewed. The study highlights the evolution of these approaches and associated challenges, providing insights into future research directions for improving feed efficiency by balancing economic viability, animal health, and environmental sustainability.</p>

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Recent advancements of classical and metaheuristic techniques in ration feed optimization for livestock, poultry, and aquaculture: a review

  • 期刊论文

  • 发表日期：

    2025年5月27日

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  • Rajeev Das,
  • Sukriti Patty,
  • Debashish Das,
  • Kedar Nath Das

摘要

Optimizing animal feed rations is crucial for enhancing farm profitability, animal health, and production efficiency. Earlier review studies lacked a focus on feed optimization across diverse animal types, including farm animals, poultry, and aquaculture. To address this gap, this literature review examines prominent publications from the past four decades, sourced from multiple electronic databases. The study explores the application of classical techniques, primarily linear programming, and metaheuristic algorithms for ration formulation. Strengths, limitations, and suitability of these methods are analysed, along with emerging trends such as single/multi-objective optimization (considering factors beyond cost, like animal welfare and environmental impact) and species-specific models. Additionally, the integration of machine learning with optimization techniques is reviewed. The study highlights the evolution of these approaches and associated challenges, providing insights into future research directions for improving feed efficiency by balancing economic viability, animal health, and environmental sustainability.