Research Publications Authored by SLIIT Staff

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This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.

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Start date: 2020Subject: search.filters.subject.genetic algorithm

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    PublicationOpen Access
    Adaptive Path Planning for Mobile Robots Using a Hybrid PRM–GA Optimization Approach
    (John Wiley and Sons Ltd, 2026-04-10) Jathunga, T; Rajapaksha, S; Jayasinghe, S; Abeygunawardena, N
    This study addresses the challenge of path planning in mobile robots, that requires efficient navigation in complex environments. Traditional approaches often struggle to meet the increasing demands of modern multi-robot systems operating in dynamic environments. To address these limitations, this study proposes an improved path planning technique by combining the probabilistic roadmap (PRM) with the genetic algorithm (GA), forming a hybrid PRM–GA approach designed to optimize the routes of mobile robots. Experiments were carried out for scenarios involving 2, 3, and 9 robots to analyze the performance of the proposed method under increasing complexity. The proposed PRM–GA method was compared with widely used path planning algorithms including (Formula presented.), Rapidly exploring random tree (RRT), and conventional PRM. Performance of each method was evaluated focusing on path efficiency and energy consumption. The enhanced fitness function within the GA evaluates robot paths based not only on distance but also on smoothness and turn count, promoting routes with fewer directional changes. The proposed PRM–GA method reduces robot energy consumption while improving navigation efficiency. Experimental results demonstrate that the PRM–GA hybrid method outperforms (Formula presented.), RRT, and PRM by encouraging smoother paths with fewer turns, thereby enhancing the operational efficiency of multi-robot systems. The effectiveness of the proposed approach highlights its potential for practical applications in sectors where efficient mobile robot navigation is essential.genetic algorithm
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    PublicationOpen Access
    Optimization of VISSIM Driver Behavior Parameter Values Using Genetic Algorithm
    (Creative Commons Attribution, 2023-02-13) Gunarathne, D; Amarasingha, N; Kulathunga, A; Wicramasighe, V
    Modeling effective vehicular traffic is a highly contested topic, especially in developing countries like Sri Lanka, which has a wide range of driving conditions. VISSIM microsimulation software is currently used by Road Development Authority (RDA) and relevant authorities to perform traffic management solutions in Sri Lanka. However, it is required to do modifications to the existing driver behavior parameter values to effectively reflect the realistic traffic conditions observed in the real-world in the simulated model. The main purpose of this study is to calibrate the VISSIM driver behavior parameter values using a genetic algorithm (GA). The methodology and results of the VISSIM model’s sensitivity analysis and calibration, which was developed for the Malabe three-legged signalized intersection, are presented in this study. A sensitivity analysis was used to find the most sensitive driver behavior parameters. Using the multi-objective GA optimization tool in the MATLAB software's optimization toolbox, the optimum driver behavior parameter values for these identified most sensitive driver behavior parameters were determined. The findings revealed that GA optimization is effective in reducing the difference between observed and simulated results.