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lien vers l'abstract: https://www.mca2021.org/en/tools/view-abstract?code=1890; International audience; Diaphorina citri or Asian citrus psyllids (ACP) are small insects that live on citrus trees and feed onyoung stems, sprouts, and leaves during all stages of development. This insect is also the major vectorof the most serious citrus plant disease known as Huanglongbing (HLB or Citrus Greening) that affectscitrus production in various parts of the world [1]. At the moment, there is no cure for this infectiousplant disease, and the major control efforts are centered on controlling the local ACP populations.In this presentation, we propose a sex-structured mathematical model that encompasses only the adultpopulation of D. citri, even though the ACP life cycle also includes the immature phase (consisting ofeggs and five nymphal instars). Following [2], the model is based on the behavioral and biological featuresof this particular insect species, and special attention is paid to the ACP mating behavior.When seeking to mate, the female psyllids emit sex pheromones that attract the male insects. Pheromonetraps are considered as an eco-friendly component of integrated pest control, and our model readilyaccommodates this type of external intervention. Sticky pheromone traps are usually set up for pursuingtwo simultaneous goals of pest control: (1) mating disruption leading to the offspring reduction, and (2)mass trapping of male insects followed by their direct removal.We also outline the theoretical analysis of the model and revise several scenarios that accentuate itspractical value for controlling the ACP adult population by pheromone traps

Constraint Satisfaction Problems (CSP) belong to a kind of traditional NP-hard problems with a high impact on both research and industrial domains. The goal of these problems is to find a feasible assignment for a group of variables where a set of imposed restrictions is satisfied. This family of NP-hard problems demands a huge amount of computational resources even for their simplest cases. For this reason, different heuristic methods have been studied so far in order to discover feasible solutions at an affordable complexity level. This paper elaborates on the application of Ant Colony Optimization (ACO) algorithms with a novel CSP-graph based model to solve Resource-Constrained Project Scheduling Problems (RCPSP). The main drawback of this ACO-based model is related to the high number of pheromones created in the system. To overcome this issue we propose two adaptive Oblivion Rate heuristics to control the number of pheromones: the first one, called Dynamic Oblivion Rate, takes into account the overall number of pheromones produced in the system, whereas the second one inspires from the recently contributed Coral Reef Optimization (CRO) solver. A thorough experimental analysis has been carried out using the public PSPLIB library, and the obtained results have been compared to those of the most relevant contributions from the related literature. The performed experiments reveal that the Oblivion Rate heuristic removes at least 79% of the pheromones in the system, whereas the ACO algorithm renders statistically better results than other algorithmic counterparts from the literature.

Constraint Satisfaction Problems (CSP) belongs to this kind of traditional NP-hard problems with a high impact in both, research and industrial domains. However, due to the complexity that CSP problems exhibit, researchers are forced to use heuristic algorithms for solving the problems in a reasonable time. One of the most famous heuristic al- gorithms is Ant Colony Optimization (ACO) algorithm. The possible utilization of ACO algorithms to solve CSP problems requires the de- sign of a decision graph where the ACO is executed. Nevertheless, the classical approaches build a graph where the nodes represent the vari- able/value pairs and the edges connect those nodes whose variables are different. In order to solve this problem, a novel ACO model have been recently designed. The goal of this paper is to analyze the performance of this novelty algorithm when solving Multi-Mode Resource-Constraint Satisfaction Problems. Experimental results reveals that the new ACO model provides competitive results whereas the number of pheromones created in the system is drastically reduced.