Abstract
This paper analyzes more than 40 papers with a restricted area of application of Multi-Objective Genetic Algorithm, Non-Dominated Sorting Genetic Algorithm-II and Multi-Objective Differential Evolution (MODE) to solve the multi-objective problem in agricultural water management. The paper focused on different application aspects which include water allocation, irrigation planning, crop pattern and allocation of available land. The performance and results of these techniques are discussed. The review finds that there is a potential to use MODE to analyzed the multi-objective problem, the application is more significance due to its advantage of being simple and powerful technique than any Evolutionary Algorithm. The paper concludes with the hopeful new trend of research that demand effective use of MODE; inclusion of benefits derived from farm byproducts and production costs into the model.
Highlights
Water is an essential natural resource because the lives of living organisms depend on it
This paper focused on the application of the three Multi-Objective Evolutionary Algorithms (MOEA); Genetic Algorithm (GA), Non-Sorting Genetic Algorithm-II (NSGA-II) and Multi-Objective Differential Evolution (MODE)
The comparison showed that the NSGA-II model can significantly reduce the computation problem of the conjunctive use models over the use of Sequential Genetic Algorithms (SGA) optimization model
Summary
Water is an essential natural resource because the lives of living organisms depend on it. Under pressure from population explosion, urbanization, extravagant lifestyles, climate change, intensive agriculture and industrialization, water are fast becoming a scarce resource. This is evident from the fact that lack of water to meet the daily requirement is a reality for one in three people globally [1]. The water management challenge can be addressed in a different way, currently, global optimization techniques appeared to be promising methods for optimizing water consumption strategies The aim of these techniques in irrigation planning and crop production is to attain maximum crops productivity under deficit water supply within an irrigated land [3]. The articles were collected from the journals with the scope of water resource management, Mathematical optimization, numerical analysis, and multi-objective evolutionary algorithms.
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