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Subject: search.filters.subject.Genetic Algorithm

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Now showing 1 - 7 of 7
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    Genetic Algorithm-Based Unmanned Aerial Vehicle (UAV) Path Planning in Dynamic Environments for Disaster Management
    (Institute of Electrical and Electronics Engineers Inc., 2025) Wijerathne V.R; Theekshana W.G.P; Prabhanga K.G.B.; De Silva K.P.C; Wijayasekara, S; Weerathunga, I; Hansika, M. M.D.J.T
    Unmanned Aerial Vehicles (UAVs) hold immense potential in disaster management by enabling rapid response, real-time aerial reconnaissance, and improved situational awareness without endangering human lives. This research proposes a real-time UAV path-planning system based on a Hierarchical Recursive Multiagent Genetic Algorithm (HR-MAGA). Unlike traditional methods that struggle with adaptability in dynamic 3D environments, our system employs localized waypoint updates to reduce the computational cost of full-path recalculations. A multi-objective fitness function guides the optimization process by balancing safety, energy efficiency, altitude smoothness, turbulence resistance, and travel time. Additionally, the system integrates a decoupled real-time collision avoidance module for immediate response to sudden threats. While obstacle detection is abstracted in this study, the framework is designed to be easily integrated with real-time sensing technologies such as LiDAR for dynamic obstacle awareness. Experimental evaluations show a 20-30% improvement in path efficiency and a 40% increase in convergence speed compared to conventional genetic algorithms, highlighting the system's adaptability and robustness in disaster response scenarios.
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    Corrigendum to “Meta-heuristic optimization based cost efficient demand-side management for sustainable smart communities” [Energy Build. (2024) 113599] (Energy & Buildings (2024) 303, (S0378778823008290),
    (Elsevier Ltd, 2024-04-15) Silva, B.N; Khan, M; Wijesinghe, R.E; Wijenayake, U
    The monetary value of grid electricity is inflating significantly due to the staggeringly broadening gap between electricity demand and supply, which arise from the unceasing growth of consumption demands. Although heuristic optimization based demand side management has its merits, incorporating Ant Colony Optimization remains disputable due to its tendency to converge at a local optimum. Therefore, this work presents a hybridized algorithm of Ant Colony Optimization and Genetic Algorithm, which alleviates the drawbacks of Ant Colony Optimization through Genetic Algorithm. The proposed work promotes sustainable energy utilization simultaneously with demand-side optimization. The performance of the proposed algorithm is compared with no scheduling instance, Ant Colony Optimization based energy management controller, and mutated Ant Colony Optimization based appliance scheduling. The proposed algorithm successfully curtails 35.4% from community peak load demand and achieves 33.67% cumulative cost saving for the community. In other words, comparative analysis confirms the supremacy of the proposed algorithm in terms of minimizing peak load, total cost, peak-to-average ratio, and waiting time, while providing prevailing insights about proposed algorithm as a sustainable solution approach.
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    Genetic Algorithm Based Hybrid Clustering Technique for the Retinal Blood Vessels Segmentation
    (IEEE, 2022-12-09) Dasanayake, D; Athuraliya, N; De Silva, H; Fernando, K; Haddela ., P.S
    Important details about the visual anomaly can be found in the retinal fundus imaging. The segmentation of the blood vessels is crucial and necessary for diagnosing different ocular fundus. The primary and most common causes of blindness are diabetic retinopathy and its effects on the retinal vascular structures. The study suggested a genetic algorithm combined with the K-means clustering technique for unsupervised retinal segmentation. An essential pre-processing step for vessel identification applications is vessel enhancement. The CLAHE filtering method is employed in this work as a preprocessing step for vessel improvement. The improved vessels were grouped together using a genetic approach, and K-means clustering was applied for superior clustering outcomes. DRIVE and IOSTAR databases that are accessible to the public are used to evaluate the suggested strategy. According to the experimental findings, the proposed algorithm successfully separates clusters that are more dense and well-separated than those of other previous findings. Both the Calinski-Harabasz I ndex S core and the Silhouette Index Score are used to validate the proposed algorithm.
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    Use of Interpretable Evolved Search Query Classifiers for Sinhala Documents
    (Springer, Cham, 2021-01) Haddela, P; Hirsch, L; Brunsdon, T; Gaudoin, J
    Document analysis is a well matured yet still active research field, partly as a result of the intricate nature of building computational tools but also due to the inherent problems arising from the variety and complexity of human languages. Breaking down language barriers is vital in enabling access to a number of recent technologies. This paper investigates the application of document classification methods to new Sinhalese datasets. This language is geographically isolated and rich with many of its own unique features. We will examine the interpretability of the classification models with a particular focus on the use of evolved Lucene search queries generated using a Genetic Algorithm (GA) as a method of document classification. We will compare the accuracy and interpretability of these search queries with other popular classifiers. The results are promising and are roughly in line with previous work on English language datasets.
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    Real-Time Decision Optimization Platform for Airline Operations
    (2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Weerasinghe, P.S.R.; Ranasinghe, R.A.M.D.K.; Mahanthe, M.M.V.R.B.; Samarakoon, P.G.C.B.; Rankothge, W.H.; Kasthurirathna, D.
    With close to 4 billion origin-destination passenger journeys worldwide, airline operations have become a crucial factor in the global economy. With the increasing number of journeys and passengers, managing the daily operations of airlines have become a complicated task. We have proposed a real-time decision optimization platform for airline operations with the following subsystems: (1) determine the optimum path for a flight, (2) optimum fleet assignment, (3) optimum gate allocation, (4) optimum crew allocation. We have used an approximation (heuristics) based optimization approach: Genetic Programming (GP) to implement the modules. The results of our proposed platform illustrate that, the decision-making process of Airline Operations Control Center (AOCC) can be optimized, and dynamic change requirements can be accommodated.
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    Incorporating strategy adoption into genetic algorithm enabled multi-agent systems
    (IEEE, 2020-07-19) Madushani, Y; Kasthurirathna, D
    Genetic Algorithm (GA) is a widely adopted optimization technique under evolutionary optimization. Inspired by the evolutionary operators of selection, crossover and mutation, Genetic Algorithms have been used to successfully solve myriad optimization problems in a wide range of domains, including in optimizing multi-agent systems. On the other hand, Evolutionary Game Theory (EGT) is used to model social-economic systems by mimicking social evolution by adopting neighborhood strategies in a stochastic manner. In this work, an extended GA is proposed for multi-agent systems, which incorporates the strategy adoption in EGT into GA enabled multi-agent systems. The proposed extended GA algorithm is applied to an example multi-robot navigation application. The proposed algorithm gives promising results in terms of the convergence time, compared to the GA based approach. Possible applications of the proposed algorithm are also discussed, while indicating potential future research directions.
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    Heuristic Approach to Solve Interval Transportation Problem
    (Faculty of Humanities and Sciences,SLIIT, 2021-09-25) Gunarathne, H.A.D.R; Juman, Z.A.M.S
    The transportation problem is a special type of linear programming problem in which commodities are transported from a set of sources to a set of destinations subject to the supply and demand quantities of sources and destinations respectively such that the total transportation cost is minimized. This plays an important role in logistics and supply-chain management for improving services, reducing cost, and optimizing the use of resources. Researchers have given considerable attention to the transportation problem with fixed demand and supply. Many algorithms are available to solve transportation problems with the above conditions. However, in realworld applications, demand and supply quantities may vary within a specific interval due to variations in the global economy. Finding an upper minimal total cost of interval transportation problem (ITP) is an NP-hard problem. Thus, less attention has been given to this type of transportation problem. Heuristic approaches are preferred to solve this type of problem. Genetic algorithm is a powerful algorithm to solve NP-hard problems because of its special characteristics. In this paper, a solution procedure based on the concept of a genetic algorithm is proposed to solve ITP.