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FWA for Multiobjective Optimization

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Fireworks Algorithm

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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.

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Notes

  1. 1.

    For the fields with the same or similar soil conditions, their equations can have the same coefficients. This is also the case for some coefficients in the following computational equations.

  2. 2.

    If the soil conditions vary greatly from the different fields, we may need to define the coefficients \(\mu _{\textit{ij}}\) and \(\nu _{\textit{ij}}\) for not only different kinds of fertilizer but also different fields. However, the goal is to improve the homogeneity of fertilizer content among the fields with the same or similar soil conditions.

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  1. Peking University, Beijing, China

    Ying Tan

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  1. Ying Tan
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Tan, Y. (2015). FWA for Multiobjective Optimization. In: Fireworks Algorithm. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46353-6_11

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  • DOI: https://doi.org/10.1007/978-3-662-46353-6_11

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