Variable-rate fertilization (VRF) decision is a key aspect of prescription generation in precision agriculture, which typically involves multiple criteria and objectives. This paper presents a multiobjective optimization problem model for oil crop fertilization, which takes into consideration not only crop yield and quality but also energy consumption and environmental effects. For efficiently solving the problem, we propose a hybrid multiobjective fireworks optimization algorithm (MOFOA) that evolves a set of solutions to the Pareto optimal front by mimicking the explosion of fireworks. In particular, it uses the concept of Pareto dominance for individual evaluation and selection, and combines differential evolution (DE) operators to increase information sharing among the individuals. The experimental tests and real-world applications in oil crop production in east China demonstrate the effectiveness and practicality of the algorithm. the number of fields the number of types of fertilizer the (average) gradient of field i the (average) plant density of field i the unit price of fertilizer j the dosage of fertilizer j in field i the inherent quantity of fertilizer j in field i the residual of fertilizer j in field i the function for estimating the crop yield the function for evaluating the crop quality the function for estimating the cost of fertilization the function for estimating the energy consumption of fertilization the function for estimating the residual fertilizer 
Multiobjective fireworks optimization for variable-rate fertilization in oil crop production