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Comparison of evolutionary and swarm intelligence-based approaches in the improvement of peach fruit quality

Abstract : The design of peach ideotypes that satisfy the requirement of high fruit quality and low sensitivity to fungal diseases in a given environment is a very challenging problem. In this paper, we propose a model-based design approach to deal with this challenge. First, we formulate it as a multi-objective optimization problem. Two well-known multi-objective optimization algorithms i.e. the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Multi-Objective Particle Swarm Optimization with the Crowding Distance (MOPSO-CD) were then used to find the best combinations of genetic resources and cultural practices adapted to, and respectful of specific environments. Statistically significant performance measures are employed to compare the two algorithms. The results obtained demonstrate that NSGA-II is able to yield a wide spread of solutions with good coverage and convergence to Pareto fronts.
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https://hal.inrae.fr/hal-02633705
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Submitted on : Wednesday, May 27, 2020 - 1:18:21 PM
Last modification on : Tuesday, February 9, 2021 - 11:48:04 AM

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  • HAL Id : hal-02633705, version 1
  • PRODINRA : 269883

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A. Kadrani, Bénédicte Quilot-Turion, Michel Génard, Francoise Lescourret, Mohamed Ould Sidi. Comparison of evolutionary and swarm intelligence-based approaches in the improvement of peach fruit quality. Annals of Management Science, 2014, 3 (1), pp.129-142. ⟨hal-02633705⟩

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