Indicator Based Ant Colony Optimization for Multi-objective Knapsack Problem

AbstractThe use of metaheuristics to solve multi-objective optimization problems (MOP) is a very active research topic. Ant Colony Optimization (ACO) has received a growing interest in the last years for such problems. Many algorithms have been proposed in the literature to solve different MOP. This paper presents an indicator-based ant colony optimization algorithm called IBACO for the multi-objective knapsack problem (MOKP). The IBACO algorithm proposes a new idea that uses binary quality indicators to guide the search of artificial ants. These indicators were initially used by Zitzler and Künzli in the selection process of their evolutionary algorithm IBEA. In this paper, we use the indicator optimization principle to reinforce the best solutions by rewarding pheromone trails. We carry out a set of experiments on MOKP benchmark instances by applying the two binary indicators: epsilon indicator and hypervolume indicator. The comparison of the proposed algorithm with IBEA, ACO and other state-of-the-art evolutionary algorithms shows that IBACO is significantly better on most instances

Optimisation par colonies de fourmis pour le problème du sac-à-dos multi-dimensionnel

National audienceWe propose an algorithm based on the Ant Colony Optimization (ACO) meta-heuristic for solving the Multidimensional Knapsack Problem (MKP), the goal of which is to find a subset of objects that maximizes a given objective function while satisfying some resource constraints. The proposed ACO algorithm is generic, and we propose three different instantiations, corresponding to three different ways of laying and exploiting pheromone trails. We experimentally compare these three different instanciations. We then experimentally compare our ACO algorithm with two state-of-the-art genetic algorithms, showing that it obtains competitive results.Dans cet article, nous proposons d'utiliser la métaheuristique d'optimisation par colonies de fourmis (Ant Colony Optimization / ACO) pour résoudre le problème du sac à dos multidimensionnel. L'objectif est de sélectionner un sous-ensemble d'objets qui maximise une fonction utilité donnée tout en respectant certaines contraintes de ressources. Nous proposons un algorithme ACO générique pour ce problème. L'idée est de construire des solutions de façon incrémentale, par ajouts successifs d'objets à une solution partielle. A chaqueitération, l'objet à ajouter est choisi selon une probabilité dépendant de traces de ``phéromone'' et d'une information heuristique locale. On étudie trois façons de déposer (et d'exploiter) les traces de phéromone : sur les objetssélectionnés, sur les couples d'objets sélectionnés consécutivement ou sur tous les couples de sommets sélectionnés. On compare le comportement de ces trois variantes sur un ensemble d'instances ``benchmarks'' et on étudie l'influence de la phéromone sur le processus de résolution. On compare enfin l'algorithme ACO proposé avec d'autres approches

A posteriori diagnosis of DRESS syndrome induced by diazoxide in a patient with an insulinoma: a case report and review of the literature

The Drug Rash with Eosinophilia and Systemic Symptoms (DRESS) syndrome can be potentially life-threatening. The diagnosis is sometimes difficult since the clinical manifestations may be incomplete or non-specific. Insulinoma is a rare functioning neuroendocrine tumor (NET) of the pancreas. Medical therapy may be needed when surgery is contraindicated, delayed or refused. Diazoxide is widely used to control hypoglycemia in patients with insulinoma. We report a clinical case of an insulinoma in a 85-year-old patient treated with diazoxide with a fatal outcome due to a delayed diagnosis of a DRESS syndrome. This is the first case of DRESS syndrome reported after using diazoxide for insulinoma treatment in our knowledge

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Ant algorithm for the multidimensional knapsack problem

International audienceWe propose a new algorithm based on the Ant Colony Optimization (ACO) meta-heuristic for the Multidimensional Knapsack Problem, the goal of which is to find a subset of objects that maximizes a given objective function while satisfying some resource constraints. We show that our new algorithm obtains better results than two other ACO algorithms on most instances

Optimisation par colonies de fourmis pour le problème du sac-à-dos multi-dimensionnel

National audienceWe propose an algorithm based on the Ant Colony Optimization (ACO) meta-heuristic for solving the Multidimensional Knapsack Problem (MKP), the goal of which is to find a subset of objects that maximizes a given objective function while satisfying some resource constraints. The proposed ACO algorithm is generic, and we propose three different instantiations, corresponding to three different ways of laying and exploiting pheromone trails. We experimentally compare these three different instanciations. We then experimentally compare our ACO algorithm with two state-of-the-art genetic algorithms, showing that it obtains competitive results.Dans cet article, nous proposons d'utiliser la métaheuristique d'optimisation par colonies de fourmis (Ant Colony Optimization / ACO) pour résoudre le problème du sac à dos multidimensionnel. L'objectif est de sélectionner un sous-ensemble d'objets qui maximise une fonction utilité donnée tout en respectant certaines contraintes de ressources. Nous proposons un algorithme ACO générique pour ce problème. L'idée est de construire des solutions de façon incrémentale, par ajouts successifs d'objets à une solution partielle. A chaqueitération, l'objet à ajouter est choisi selon une probabilité dépendant de traces de ``phéromone'' et d'une information heuristique locale. On étudie trois façons de déposer (et d'exploiter) les traces de phéromone : sur les objetssélectionnés, sur les couples d'objets sélectionnés consécutivement ou sur tous les couples de sommets sélectionnés. On compare le comportement de ces trois variantes sur un ensemble d'instances ``benchmarks'' et on étudie l'influence de la phéromone sur le processus de résolution. On compare enfin l'algorithme ACO proposé avec d'autres approches

Algorithme fourmi avec différentes stratégies phéromonales pour le sac à dos multidimensionnel

National audienceAlgorithme fourmi avec différentes stratégies phéromonales pour le sac à dos multidimensionne

Algorithme fourmi avec différentes stratégies phéromonales pour le sac à dos multidimensionnel

National audienceAlgorithme fourmi avec différentes stratégies phéromonales pour le sac à dos multidimensionne

Ant Colony Optimization for Multi-objective Optimization Problems

International audienceWe propose in this paper a generic algorithm based on Ant ColonyOptimization metaheuristic (ACO) to solve multi-objectiveoptimization problems (PMO). The proposed algorithm isparameterized by the number of ant colonies and the number ofpheromone trails. We compare different variants of this algorithmon the multi-objective knapsack problem. We compare also theobtained results with other evolutionary algorithms from theliterature