Multi-Objective Optimization of Solar Powered Irrigation System by Using Genetic Algorithm

Irrigation system is synonym with agriculture. Conventional way of supplying source of energy to work the water pumping system is through fuel combustion such as diesel. Nowadays fuel combustion is not an attractive and feasible approach in a long run due to hiking fuel price and it is also not environmentally friendly which it may lead to pollution. The development of renewable energy such as solar energy as an external heat source rather be more attractive. However, this complex system needs to be optimized by using suitable metaheuristic technique in order to make the design to be economically and practically efficient. Thus, Genetic Algorithm is applied to solve multiple objective solar-irrigation system optimization. It is identified that the best setting should be input to get an optimal solution. Initial range of [1; 2] and crossover fraction of 1.0 have majorly contributed to the optimal search parameters. After some tuning to get the best setting, the simulation shows that the fitness function of 3 objectives resulted with 17.4303 kW power output, 15.2355% efficiency and $143,533.10 fiscal savings. This set of optimal solution is not as closed as other technique to the desired design objectives. Genetic Algorithm is a common technique and easy to work with but it has yet to be the best metaheuristic technique for this engineering problem due to some drawback

Multi-Objective Optimization of Solar Powered Irrigation System by Using Genetic Algorithm

Irrigation system is synonym with agriculture. Conventional way of supplying source of energy to work the water pumping system is through fuel combustion such as diesel. Nowadays fuel combustion is not an attractive and feasible approach in a long run due to hiking fuel price and it is also not environmentally friendly which it may lead to pollution. The development of renewable energy such as solar energy as an external heat source rather be more attractive. However, this complex system needs to be optimized by using suitable metaheuristic technique in order to make the design to be economically and practically efficient. Thus, Genetic Algorithm is applied to solve multiple objective solar-irrigation system optimization. It is identified that the best setting should be input to get an optimal solution. Initial range of [1; 2] and crossover fraction of 1.0 have majorly contributed to the optimal search parameters. After some tuning to get the best setting, the simulation shows that the fitness function of 3 objectives resulted with 17.4303 kW power output, 15.2355% efficiency and $143,533.10 fiscal savings. This set of optimal solution is not as closed as other technique to the desired design objectives. Genetic Algorithm is a common technique and easy to work with but it has yet to be the best metaheuristic technique for this engineering problem due to some drawback