Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Sarswat, A.

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    Fast Convergence to Near Optimal Solution for Job Shop Scheduling Using Cat Swarm Optimization
    (Springer Verlag service@springer.de, 2017) Dani, V.; Sarswat, A.; Swaroop, V.; Domanal, S.; Guddeti, G.R.M.
    Job Shop Scheduling problem has wide range of applications. However it being a NP-Hard optimization problem, always finding an optimal solution is not possible in polynomial amount of time. In this paper we propose a heuristic approach to find near optimal solution for Job Shop Scheduling Problem in predetermined amount of time using Cat Swarm Optimization. Novelty in our approach is our non-conventional way of representing position of cat in search space that ensures advantage of spatial locality is taken. Further while exploring the search space using randomization, we never explore an infeasible solution. This reduces search time. Our proposed approach outperforms some of the conventional algorithms and achieves nearly 86% accuracy, while restricting processing time to one second. © 2017, Springer International Publishing AG.
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    A novel overlapping community detection using parallel CFM and sequential nash equilibrium
    (Institute of Electrical and Electronics Engineers Inc., 2018) Sarswat, A.; Guddeti, R.M.
    Detecting Overlapping Community in Social Networks is one of the challenging and complex problem. Several approaches based on heuristic, modularity & modularity density, graph partitioning and game theory are available for community detection. However getting an optimum and stable solution with less computation cost for large datasets is not possible using these existing approaches. Hence, in this work, we propose a novel overlapping community detection algorithm based on parallel community forest model and sequential Nash Equilibrium for large datasets. In this paper, community forest model (CFM) is implemented in parallel using Spark framework to get the initial community structure and then a Nash Equilibrium is computed to find a stable overlapping community structure. We conducted experiments on the benchmark LFR dataset with different sizes like 500, 1000, 2000 upto 10,000 nodes to evaluate the proposed method. Our experimental results clearly demonstrate that the proposed approach outperforms the existing works in terms of quality, scalability, stability and less computation time. © 2018 IEEE.