Optimizing Laying Hen Diet using Multi-Swarm Particle Swarm Optimization

Formulating animal diet by accounting fluctuating cost, nutrient requirement, balanced amino acids, and maximum composition simultaneously is a difficult and complex task. Manual formulation and Linear Programming encounter difficulty to solve this problem. Furthermore, the complexity of laying hen diet problem is change through ingredient choices. Thus, an advanced technique to enhance formula quality is a vital necessity. This paper proposes the Multi-Swarm Particle Swarm Optimization (MSPSO) to enhance the diversity of particles and prevent premature convergence in PSO. MSPSO work cooperatively and competitively to optimize laying hen diet and produce improved and stable formula than Genetic Algorithm, Hybridization of Adaptive Genetic Algorithm and Simulated Annealing, and Standard Particle Swarm Optimization with less time complexity. In addition, swarm size, iteration, and inertia weight parameters are investigated and show that swarm size of 50 for each sub-swarm, total iteration of 16,000, and inertia weight of 6.0 should be used as a good parameter for MSPSO to optimize laying hen diet

Good Parameters for PSO in Optimizing Laying Hen Diet

Manual formulation of poultry diet by taking into account the fulfillment of all nutrients requirement with least cost is a difficult task. Particle Swarm Optimization (PSO) shows promising technique to solve this problem. However, there is a lack of studying a good parameter for PSO to solve feed formulation problem since PSO is sensitive to control parameter which depends on the problem. Therefore, this study investigates good swarm size, total iterations, acceleration coefficients, and inertia weight to produce a better formula. PSO with proposed good parameters is compared with other parameters. The obtained result shows that PSO with good parameters choice produces the highest fitness. Furthermore, good parameters of PSO can be used as a reference for a software developer and for further research to optimize poultry diet using PSO

Optimization of Poultry Feed Composition Using Hybrid Adaptive Genetic Algorithm and Simulated Annealing

The highest component in the production cost of the poultry industry is feed cost. The formation of an efficient feed composition is needed because of the increasing price of feed ingredients. Several types of software have been developed to help determine the feed composition, but the price of commercial feed formulation software is quite expensive for most organizations. Hybrid adaptive genetic algorithm and Simulated Annealing were used to calculate poultry feed formulations. This algorithm used a change mechanism of the control parameter in genetic algorithm adaptively to get better results. Simulated Annealing was applied to avoid a local optimum solution produced by the genetic algorithm. The results showed that hybrid adaptive genetic algorithm and Simulated Annealing is better than the classical genetic algorithm

Potential grouper feed formulation based on evolutionary algorithm concept with a unique selection operator

The problems of insufficient wild catch grouper fish and the high demand in the market have increased the need of farming the grouper fish.In order to farm the grouper fish, there is a need to have abundant of trash fish as their feeds since grouper fish is a carnivorous fish.But, trash fish is dramatically expensive and hard to store, hard to maintain the quality of nutrients as well as the quantity throughout the years.Therefore, turning to formulated feed mix is the alternative. However, the issue is on its cost while providing quality feed mix.That leads to the search for an approach which could provide the most suitable feed ingredients and nutrients.One potential approach is the Evolutionary Algorithm (EA) which has been used to solve the feed formulation problems in poultry, shrimp and cattle.Hence, in this study, an EA-based approach with a refinement on using the standard deviation in the strategy of tournament selection has been proposed to minimize the total cost in formulating the feed of grouper fish.Results show that the lowest cost can be accepted while satisfying the nutrient requirements of grouper fish

POTENTIAL GROUPER FEED FORMULATION BASED ON EVOLUTIONARY ALGORITHM CONCEPT WITH A UNIQUE SELECTION OPERATOR

The problems of insufficient wild catch grouper fish and the high demand in the market have increased the need of farming the grouper fish. In order to farm the grouper fish, there is a need to have abundant of trash fish as their feeds since grouper fish is a carnivorous fish. But, trash fish is dramatically expensive and hard to store, hard to maintain the quality of nutrients as well as the quantity throughout the years. Therefore, turning to formulated feed mix is the alternative. However, the issue is on its cost while providing quality feed mix. That leads to the search for an approach which could provide the most suitable feed ingredients and nutrients. One potential approach is the Evolutionary Algorithm (EA) which has been used to solve the feed formulation problems in poultry, shrimp and cattle. Hence, in this study, an EA-based approach with a refinement on using the standard deviation in the strategy of tournament selection has been proposed to minimize the total cost in formulating the feed of grouper fish. Results show that the lowest cost can be accepted while satisfying the nutrient requirements of grouper fish