This study presents a comprehensive framework to enhance Wikidata as an open and collaborative knowledge graph by integrating Open Biological and Biomedical Ontologies (OBO) and Medical Subject Headings (MeSH) keywords from PubMed publications. The primary data sources include OBO ontologies and MeSH keywords, which were collected and classified using SPARQL queries for RDF knowledge graphs. The semantic alignment between OBO ontologies and Wikidata was evaluated, revealing significant gaps and distorted representations that necessitate both automated and manual interventions for improvement. We employed pointwise mutual information to extract biomedical relations among the 5000 most common MeSH keywords in PubMed, achieving an accuracy of 89.40 % for superclass-based classification and 75.32 % for relation type-based classification. Additionally, Integrated Gradients were utilized to refine the classification by removing irrelevant MeSH qualifiers, enhancing overall efficiency. The framework also explored the use of MeSH keywords to identify PubMed reviews supporting unsupported Wikidata relations, finding that 45.8 % of these relations were not present in PubMed, indicating potential inconsistencies in Wikidata. The contributions of this study include improved methodologies for enriching Wikidata with biomedical information, validated semantic alignments, and efficient classification processes. This work enhances the interoperability and multilingual capabilities of biomedical ontologies and demonstrates the critical role of MeSH keywords in verifying semantic relations, thereby contributing to the robustness and accuracy of collaborative biomedical knowledge graphs.

Crowd management and analysis (CMA) systems have gained a lot of interest in the vulgarization of unmanned aerial vehicles (UAVs) use. Crowd tracking using UAVs is among the most important services provided by a CMA. In this paper, we studied the periodic crowd-tracking (PCT) problem. It consists in using UAVs to follow-up crowds, during the life-cycle of an open crowded area (OCA). Two criteria were considered for this purpose. The first is related to the CMA initial investment, while the second is to guarantee the quality of service (QoS). The existing works focus on very specified assumptions that are highly committed to CMAs applications context. This study outlined a new binary linear programming (BLP) model to optimally solve the PCT motivated by a real-world application study taking into consideration the high level of abstraction. To closely approach different real-world contexts, we carefully defined and investigated a set of parameters related to the OCA characteristics, behaviors, and the CMA initial infrastructure investment (e.g., UAVs, charging stations (CSs)). In order to periodically update the UAVs/crowds and UAVs/CSs assignments, the proposed BLP was integrated into a linear algorithm called PCTs solver. Our main objective was to study the PCT problem from both theoretical and numerical viewpoints. To prove the PCTs solver effectiveness, we generated a diversified set of PCTs instances with different scenarios for simulation purposes. The empirical results analysis enabled us to validate the BLP model and the PCTs solver, and to point out a set of new challenges for future research directions.

The multiperiodic crowd tracking (MPCT) problem is an extension of the periodic crowd tracking (PCT) problem, recently addressed in the literature and solved using an iterative solver called PCTs solver. For a given crowded event, the MPCT consists of follow-up crowds, using unmanned aerial vehicles (UAVs) during different periods in a life-cycle of an open crowded area (OCA). Our main motivation is to remedy an important limitation of the PCTs solver called “PCTs solver myopia” which is, in certain cases, unable to manage the fleet of UAVs to cover all the periods of a given OCA life-cycle during a crowded event. The behavior of crowds can be predicted using machine learning techniques. Based on this assumption, we proposed a new mixed integer linear programming (MILP) model, called MILP-MPCT, to solve the MPCT. The MILP-MPCT was designed using linear programming technique to build two objective functions that minimize the total time and energy consumed by UAVs under a set of constraints related to the MPCT problem. In order to validate the MILP-MPCT, we simulated it using IBM-ILOG-CPLEX optimization framework. Thanks to the “clairvoyance” of the proposed MILP-MPCT model, experimental investigations show that the MILP-MPCT model provides strategic moves of UAVs between charging stations (CSs) and crowds to provide better solutions than those reported in the literature.

The knapsack problem is one of the most investigated and applicable combinatorial optimization problems. In this paper we consider a generalized problem called the Multiple Knapsack Problem with Setup (MKPS) in which a set of families of items and a set of knapsacks are available. Each item is characterized by a knapsack-dependent profit and each family is associated with a knapsack-dependent cost. We formally present a mixed-integer linear program of the MKPS and we propose a multi-level matheuristic to solve large size instances of the problem. The matheuristic takes advantage of the structure of the problem and the decomposition principle. Furthermore, we enhance our solution approach combining it with tabu search. We carry out a computational study to assess the performance of the proposed matheuristics on a set of instances from the Knapsack Problem with Setup (KPS) literature. The computational results show that the proposed matheuristic is competitive compared with the state-of-the-art methods. To better evaluate its performance, we generate a new testbed for the MKPS and we compare the results to exact solutions provided by a commercial solver. Computational experiments substantiate the good performance of the proposed methods as they provide new best known values for 185 instances out of 360 in a very competitive running time.

Knapsack Problems with Setups (KPS) have received increasing attention in recent research for their potential use in the modeling of various concrete industrial and financial problems, such as order acceptance and production scheduling. The KPS problem consists in selecting appropriate items, from a set of disjoint families of items, to enter a knapsack while maximizing its value. An individual item can be selected only if a setup is incurred for the family to which it belongs. In this paper, we propose a tree search heuristic to the KPS that generates compound moves by a strategically truncated form of tree search. We adopt a new avoid duplication technique that consists in converting a KPS solution to an integer index. The efficiency of the proposed method is evaluated by computational experiments involving a set of randomly generated instances. The results demonstrate the impact of the avoiding duplication technique in terms of enhancing solution quality and computation time. The efficiency of the proposed method was confirmed by its ability to produce optimal and near optimal solutions in a short computation time.

Real-world optimization problems are very complex and NP-hard. The modeling of such problems is in constant evolution in term of constraints and objectives and their resolution is expensive in computation time. With all this change, even metaheuristics, well known for their efficiency, begin to be overtaken by data explosion. Recently, Thanks to the publication of languages as OpenCL and CUDA, the development of parallel metaheuristics on GPU platform has a growing interest. Throughout this paper, we propose a parallelization in an iterative level of a local search. The contribution of this work is to propose a robust local search through two popular neighborhood structures. This contribution is applied to some combinatorial problems and adapted for the GPU platform. For this, several techniques have been proposed to accelerate the memory access, control the divergence and to maximize the parallelization. Many versions have been implemented with the OpenCL language to test parallelization on both GPU and CPU. Computational performance of this parallel local search are reported and compared to the sequential version.

The Knapsack Problem with Setup (KPS) is a generalization of the classical Knapsack problem (KP), where items are divided into families. An individual item can be selected only if a setup is incurred for the family to which it belongs. This paper provides a dynamic programming (DP) algorithm for the KPS that produces optimal solutions in pseudo-polynomial time. In order to reduce the storage requirements of the algorithm, we adopt a new technique that consists in converting a KPS solution to an integer index. Computational experiments on randomly generated test problems show the efficiency of the DP algorithm compared to the ILOG׳s commercial product CPLEX 12.5.

This paper proposes a new hybrid tree search algorithm to the Multidimensional Knapsack Problem MKP that effectively combines tabu search with a dynamic and adaptive neighborhood search procedure. The authors' heuristic, based on a filter-and-fan F&F procedure, uses a Linear Programming-based Heuristic to generate a starting solution to the F&F process. A tabu search procedure is used to try to enhance the best solution value provided by the F&F method that generates compound moves by a strategically truncated form of tree search. They report the first application of the F&F method to the MKP. Experimental results obtained on a wide set of benchmark problems clearly demonstrate the competitiveness of the proposed method compared to the state-of-the-art heuristic methods.