A mathematical model for crop pattern coupled with economic and environmental factors of agricultural production constructed with remote sensing and metaheuristic based algorithm is considered in this work. The model is expected to serve as a support system for farm managers' decision making process. Geographic data showing soil properties of major cities in Benue State, Nigeria using remote sensing, was integrated to the model to obtain analyzed suitability information for selected crops. A class of modern optimization algorithms was thereafter used to find optimum cropland pattern. A net production value of $1,592,107,000.00$ was obtained by using the model compared to an initial production value of $1,364,460,000.00$ recorded in the study area. The study suggests that soil properties must be considered along side with economic factors before choosing the types of crop to be planted on a piece of land. This study has shown the efficacy of optimization tools which should be dully employed by farmers in decision making process. The data used to support the findings of this study are included within the article.
An agricultural model for allocation of crops is considered in this work using Pollination Intelligence Method. The model was constructed to solve farmer’s decision making in allocating crops to a piece of land using market price, known yield of crops, cost incurred during planting, and the total amount of land available. A new class of metaheuristic method called Flower Pollinated Algorithm is also presented in this work to solve the designed model. An improved version of the Flower Pollinated Algorithm called Pollination Intelligence Algorithm using an iterative scheme to override the switch parameter in Flower Pollinated Algorithm is also presented and used in solving the designed model. A case study of a farmer in Ife, Osun State, Nigeria, was used to implement the model, and the results obtained suggested that instead of allocating crops to land randomly based on farmer’s intuition, cost of planting, yield of crops, and market price were factors that must be considered by farmers for optimal profit before planting crops.
In this paper, a new metaheuristic algorithm named refined heuristic intelligence swarm (RHIS) algorithm is developed from an existing particle swarm optimization (PSO) algorithm by introducing a disturbing term to the velocity of PSO and modifying the inertia weight, in which the comparison between the two algorithms is also addressed.
Inequalities conditions for certain new classes of univalent functions were determined. Also,the distortion inequalities for the the new class of univalent functions was established.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.