Computer Science

The Ant Colony Optimization Algorithm Applied in Transport Logistics

Krzysztof Ostrowski — 2024-10-03

The Vehicle Routing Problem belongs to graph optimization and its goal is to find shortest routes visiting a given set of customers with additional constraints present. The article presents the ant colony optimization metaheuristic which solves vehicle routing problems and its real-life application in transport logistics (finding routes for delivery companies). The metaheuristic generated high-quality solutions (superior to compared methods). Our tool is flexible and enables us to solve various variants of routing problems so it is well suited to specific needs of transportation companies.

FORMALIZATION AND ANALYSIS OF UML 2.0 INTERACTION OVERVIEW DIAGRAM USING MAUDE REWRITING LOGIC LANGUAGE

Chafika Djaoui — 2024-10-03

The visual modeling language UML embodies object-oriented design principles.
It provides a standard way to visualize the design of a system. It exploits a rich
set of well-defined graphical notations for creating abstract models. However,
the power of UML is lessened through partially specified formal semantics. Indeed, UML notations are semi-formal and do not lead to fully formalized and
executable semantics. Fortunately, UML diagrams are prone to early formalization. Formal methods are a valuable tool that can help overcome the UML
constructs’ shortage of firm semantics. It is a powerful way to ascribe precise semantics to the graphical notations used in UML diagrams and models.
Our work aims to support the semantics of the UML Interaction Overview Diagram. It introduces an approach to leveraging the strengths of the Maude
Rewriting Logic language as a formal specification language. The proposal relies on a model-driven engineering approach. It aims to automate the UML
Interaction Overview Diagram’s mapping to a Maude language specification.
The Maude language and its linked tools, including the Maude Model Checker,
are used to analyze and verify the resulting Maude specification. Finally, an
application example shows the feasibility and benefits of the proposed approach.

ENHANCED BONOBO OPTIMIZER FOR OPTIMIZING DYNAMIC PHOTOVOLTAIC MODELS

Heba Eid — 2024-10-03

Bonobo optimizer (BO) is a novel metaheuristic algorithm motivated by
the social behaviour of the bonobos. This paper presents a quantum behaved bonobo optimization algorithm (QBOA) employing an innovative metaheuristic based on the reproductive strategies and social behavior of bonobos.
Whereby, the quantum mechanics are embedded into the bonobo optimizer
to direct the search agents through the search space. Accordingly, under this
quantum-behaved movement, the proposed QBOA’s exploitation capability is
promoted. The performance of the proposed QBOA is exhibited on CEC2005
and CEC2019 benchmarks. Moreover, the QBOA algorithm was adapted to
optimize the dynamic photovoltaic models parameters. QBOA exhibits the
efficiency and adequacy to solve various optimization problems based on experimental and comparison findings, as well as its ability to implement competitive
and promising results optimizing dynamic photovoltaic models

Biometrics-Based Generation of Diffie-Hellman Key Exchange Parameters

Aldin Kovačević — 2024-10-03

When two parties need to securely communicate over an insecure channel, Diffie-Hellman is often employed as the key exchange algorithm. This paper presents two novel approaches to generating Diffie-Hellman parameters for key exchange based on user biometrics, namely their fingerprint data. Fingerprint templates are extracted as bit strings via a fingerprint scanner and later used as inputs. In one approach, the whole fingerprint template is utilized as a user’s private key. In the second approach, fingerprint data is scrambled into smaller chunks and rearranged into two strings that serve as the user’s private key and the basis for prime p. Both approaches were implemented and tested experimentally. After analysis, the second approach that uses scrambled fingerprint data shows better execution times and improved security and usability considerations.

CV19T, A NOVEL BIO-SOCIALLY INSPIRED METHOD, BELONGING TO A NEW NATURE-INSPIRED METAHEURISTICS CLASS

Bouthina Saib — 2024-10-03

The paper presents CV19T, a novel bio-socially inspired meta-heuristic, where
the cornerstone on which rests is the relationship between humans crowding
density, on one side, influenced by their mobility, mutual attractiveness to each
other and individual consciousness, and on the other side, the amazing speed
of COVID-19 propagation. CV19T originality resides in the fact of combining features from two completely distinct and famous classes, namely: swarm intelligence and Evolutionary Algorithms. Moreover, CV19T extends elitism
concept (i.e. survival of the most powerful), on which are based courant evolutionist approaches to the survival of the most beneficial one. Also, CV19T shows that additional parameters can increase control of its behaviour, in many
cases, leading to rise in its results relevance. To validate CV19T, it was tested
on benchmarks set, including 23 functions (unimodal, multimodal and fixeddimensional multimodal) and 4 real-world problems.

A NOVEL HYBRID DEEP LEARNING APPROACH FOR 3D OBJECT DETECTION AND TRACKING IN AUTONOMOUS DRIVING

Dheepika PS — 2024-10-03

Recently Object detection and tracking using fusion of LiDAR and RGB camera for the autonomous vehicle environment is a challenging task. The existing
works initiates several object detection and tracking frameworks using Artificial
Intelligence (AI) algorithms. However, they were limited with high false positives and computation time issues thus lacking the performance of autonomous
driving environment. The existing issues are resolved by proposing Hybrid
Deep Learning based Multi Object Detection and Tracking (HDL-MODT) using sensor fusion methods. The proposed work performs fusion of solid state
LiDAR, Pseudo LiDAR, and RGB camera for improving detection and tracking
quality. At first, the multi-stage preprocessing is done in which noise removal is
performed using Adaptive Fuzzy Filter (A-Fuzzy). The pre-processed fused image is then provided for instance segmentation to reduce the classification and
tracking complexity. For that, the proposed work adopts Lightweight General
Adversarial Networks (LGAN). The segmented image is provided for object
detection and tracking using HDL. For reducing the complexity, the proposed
work utilized VGG-16 for feature extraction which forms the feature vectors.
The features vectors are then provided for object detection using YOLOv4.
Finally, the detected objects were tracked using Improved Unscented Kalman
Filter (IUKF) and mapping the vehicles using time based mapping by considering their RFID, velocity, location, dimension and unique ID. The simulation of
the proposed work is carried out using MATLAB R2020a simulation tool and
performance of the proposed work is compared with several metrics that show
that the proposed work outperforms than the existing works.