FWA for Multiobjective Optimization

Abstract

This chapter is to present some research works of FWA for multiobjective optimization, of which this is a successful instance like the multiobjective fireworks algorithm (MOFWA) proposed by Zheng et al. (2013) Applied Soft Computing 13(11):4253–4263, [1] for oil crop fertilization, which takes into consideration not only crop yield and quality but also energy consumption and environmental effects. The variable-rate fertilization (VRF) is a key aspect of prescription generation in precision agriculture, which typically involves multiple criteria and objectives. To solve the problem efficiently, a hybrid multiobjective fireworks optimization algorithm (MOFWA) is proposed to evolve a set of solutions to the Pareto optimal front by mimicking the explosion of fireworks. Especially, MOFWA 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 proposed MOFWA outperforms some state-of-the-art methods on a set of real-world VRF problems.