Multi-Objective Evolutionary Optimization of Biological Pest Control with Impulsive Dynamics in Soybean Crops

Cardoso, Rodrigo T. N.; Cruz, Andre Rodrigues da; Wanner, Elizabeth F.; Takahashi, Ricardo H. C.

doi:10.1007/s11538-009-9409-7

Cited by 7 publications

(10 citation statements)

References 18 publications

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“…Interestingly, the optimal strategies presented in Figs. 3-5 of Cardoso et al (2009) have a clear resemblance to a periodic control except at the beginning and the end of season. Based on this observation and our own results, we conjecture that effective strategies of biological pest control can be obtained by combining one-off control and periodic control.…”

Section: Discussionmentioning

confidence: 96%

“…Apart from the different choice of study system, a few additional differences are worth noting: First, Cardoso et al (2009) do not compare the impulsive-control strategies that they derive with simple one-off control and periodic-control strategies as considered here. Second, whereas we develop and motivate a non-linear measure of pest-control efficacy, Cardoso et al (2009) uses a measure of the number of released natural enemies as their second objective. Third, we explicitly consider the dynamics of the crop and move beyond the traditional Lotka-Volterra framework by incorporating functional responses.…”

Section: Discussionmentioning

confidence: 99%

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Pareto-efficient biological pest control enable high efficacy at small costs

Lundström

Zhang

Brännström

2017

Ecological Modelling

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Biological pest control is increasingly used in agriculture as a an alternative to traditional chemical pest control. In many cases, this involves a one-off or periodic release of naturally occurring and/or genetically modified enemies such as predators, parasitoids, or pathogens. As the interaction between these enemies and the pest is complex and the production of natural enemies potentially expensive, it is not surprising that both the efficacy and economic viability of biological pest control are debated. Here, we investigate the performance of very simple control strategies. In particular, we show how Paretoefficient one-off or periodic release strategies, that optimally trade off between efficacy and economic viability, can be devised and used to enable high efficacy at small economic costs. We demonstrate our method on a pest-pathogen-crop model with a tunable immigration rate of pests. By analyzing this model, we demonstrate that simple Pareto-efficient one-off and periodic release strategies are efficacious and simultaneously have profits that are close to the theoretical maximum obtained by strategies optimizing only the profit. When the immigration rate of pests is low to intermediate, one-off control strategies are sufficient, and when the immigration of pests is high, periodic release strategies are preferable. The methods presented here can be extended to more complex scenarios and be used to identify promising biological pest control strategies in many circumstances.

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Section: Discussionmentioning

confidence: 96%

Section: Discussionmentioning

confidence: 99%

Pareto-efficient biological pest control enable high efficacy at small costs

Lundström

Zhang

Brännström

2017

Ecological Modelling

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“…Many strong hypotheses are usually used in order to facilitate the resolution of such problems. Moreover, such approaches usually lead to continuous-time control laws that must be discretized in some way to be applied to integrated pest management (Cardoso et al 2009). …”

Section: Approaches Using Optimal Control Formulationsmentioning

confidence: 99%

“…Similarly, in Cardoso et al (2009), the authors used the high-performance and well-known Non-dominated Sorting Genetic Algorithm II for the design of biological pest control strategies. They modeled the system as a nonlinear impulsive dynamic programming problem and considered both the cost of application of the control action and the cost of economic damages.…”

Section: Approaches For More Elaborated Formulationsmentioning

confidence: 99%

Model-based design of integrated production systems: a review

Ould-Sidi¹,

Lescourret²

2011

Agronomy Sust. Developm.

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Pest resistance and water pollution are major issues caused by the excessive use of pesticides in intensive agriculture. The concept of integrated production system (IPS) has been thus designed to solve those issues and also to meet the need for better food quality and production. Methodologies such as agronomic diagnosis-based design, prototyping, and model-based design have been developed.Here we review the model-based design of IPS. We identify tools for the development of comprehensive models. Once comprehensive models have been developed, model-based design of IPS can be formulated as an optimization problem to be solved using different approaches. Thus, we review the choice of corresponding criteria, constraints, and mathematical formulations found in the literature. We analyze successful model-based design in transportation and drug development. We learn from these areas to overcome the inherent difficulties involved in the modelbased design of IPS. We recommend the following major points: IPS model-based design should use integrative modeling platforms, process-oriented modeling, and object-oriented techniques to improve the genericity, modularity, reuse of crop models, and the data sharing. The spatial dimension of IPS has to be accounted for by making crop models spatially explicit. The design evaluation criteria have to be standardized and carefully chosen by promoting stakeholders involvement. The design of IPS should be formulated as a multiobjective optimization problem, for which non-aggregative approaches should be preferred. Finally, it is necessary to establish a true dialogue between prototyping and model-based design practitioners in order to test, and if necessary to improve, the proposed systems.

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“…A wide range of difficult real-world multiobjective problems have been dealt with by means of evolutionary computation. CARDOSO et al (2009) develop an evolutionary approach to control biological pests in soybeans crop. EVER-SON and FIELDSEND (2006) apply an evolutionary strategy in safety related systems, which monitor the location of airplanes by radars and send alert messages to the flight controllers.…”

Section: Introductionmentioning

confidence: 99%

Virtual interpolation of discrete multi-objective programming solutions with probabilistic operation

Silva¹,

Arruda²,

Ourique³

2011

Sba Controle & Automação

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This work presents a novel framework to address the long term operation of a class of multi-objective programming problems. The proposed approach considers a stochastic operation and evaluates the long term average operating costs/profits. To illustrate the approach, a two-phase method is proposed which solves a prescribed number of K mono-objective problems to identify a set of K points in the Pareto-optimal region. In the second phase, one searches for a set of non-dominated probability distributions that define the probability that the system operates at each point selected in the first phase, at any given operation period. Each probability distribution generates a vector of average long-term objectives and one solves for the Pareto-optimal set with respect to the average objectives. The proposed approach can generate virtual operating points with average objectives that need not have a feasible solution with an equal vector of objectives. A few numerical examples are presented to illustrate the proposed method.

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Multi-Objective Evolutionary Optimization of Biological Pest Control with Impulsive Dynamics in Soybean Crops

Cited by 7 publications

References 18 publications

Pareto-efficient biological pest control enable high efficacy at small costs

Pareto-efficient biological pest control enable high efficacy at small costs

Model-based design of integrated production systems: a review

Virtual interpolation of discrete multi-objective programming solutions with probabilistic operation

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