Search Results - (( developing agent graph algorithm ) OR ( java implication based algorithm ))

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  1. 1

    Modeling of static and dynamic components of bio-nanorobotic systems by Gavgani, Hamidreza Khataee

    Published 2012
    “…The first modeling technique applies graph algorithms to compute a new set of optimal weighted structural properties of C60 and C70 fullerenes. …”
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    Thesis
  2. 2

    Recent research in cooperative path planning algorithms for multi-agent using mixed- integer linear programming by Che Ku, Nor Azie Hailma, Omar, Rosli, Sabudin Elia Nadira, Sabudin Elia Nadira

    Published 2016
    “…Path planning is one of the issues to be handled in the development of autonomous systems. For a group of agents, cooperative path planning is crucial to ensure that a given mission is accomplished in the shortest time possible with optimal solution. …”
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  3. 3

    Nonlinear convergence algorithm: structural properties with doubly stochastic quadratic operators for multi-agent systems by Abdulghafor, Rawad Abdulkhaleq Abdulmolla, Turaev, Sherzod, Zeki, Akram M., Adamu, Abubakar Ibrahim

    Published 2018
    “…We develop two algorithms: 1) the nonlinear algorithm of extreme doubly stochastic quadratic operator (NLAEDSQO) to generate all the convergent EDSQOs and 2) the nonlinear convergence algorithm (NLCA) of EDSQOs to investigate the optimal consensus for MAS. …”
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    Using genetic algorithms to optimise land use suitability by Pormanafi, Saeid

    Published 2012
    “…In this study, under environmentfriendliness objective, based on multi-agent genetic algorithms, was developed a geospatial model for the land use allocation. …”
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    Thesis
  8. 8

    Path planning methods for autonomous vehicles at intersections: A review by Vinayak, Akhil, Muhammad Aizzat, Zakaria, Younus, Maryam, Mohamad Heerwan, Peeie, Muhammad Izhar, Ishak

    Published 2026
    “…Furthermore, the review highlights key challenges such as handling dynamic multi-agent environments, managing interactions with human-driven vehicles, and balancing computational efficiency with path optimality and discusses potential solutions through adaptive, real-time algorithms, cooperative planning, and predictive modelling. …”
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