Conference Papers

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

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Subject: search.filters.subject.Ant Colony Optimization

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    Ant-CAMP: Ant based congestion adaptive multi-path routing protocol for wireless networks
    (2011) Raval, C.; Hegde, S.; Tahiliani, M.P.
    The advent of mobile computing devices and wide deployment of wireless networks have led to an exponential increase in the internet traffic. Long congestion epochs and frequent link failures in wireless network lead to more number of packets being dropped and incur high end-to-end delay, thereby degrading the overall performance of the network. Congestion control, though mainly incorporated at the transport layer, if coupled with the routing protocols, can significantly improve overall performance of the network. In this paper we propose Ant based Congestion Adaptive Multipath (Ant-CAMP) routing protocol that aims to avoid congestion by proactively sending congestion notification to the sender. The proposed Ant-CAMP routing protocol is implemented in Network Simulator-2 (NS-2) and its performance is compared with Ad-hoc On Demand Multi-Path Distance Vector (AOMDV) in terms of Packet Drops due to Congestion, Packet Delivery Fraction and Average End-to-End Delay. © Springer-Verlag 2011.
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    A modified Ant Colony optimization algorithm with load balancing for job shop scheduling
    (IEEE Computer Society help@computer.org, 2013) Chaukwale, R.; Kamath S․, S.S.
    The problem of efficiently scheduling jobs on several machines is an important consideration when using Job Shop scheduling production system (JSP). JSP is known to be a NP-hard problem and hence methods that focus on producing an exact solution can prove insufficient in finding an optimal resolution to JSP. Hence, in such cases, heuristic methods can be employed to find a good solution within reasonable time. In this paper, we study the conventional ACO algorithm and propose a Load Balancing ACO algorithm for JSP. We also present the observed results, and discuss them with reference to the conventional ACO. It is observed that the proposed algorithm gives better results when compared to conventional ACO. © 2013 IEEE.
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    An hybrid bio-inspired task scheduling algorithm in cloud environment
    (Institute of Electrical and Electronics Engineers Inc., 2014) Madivi, R.; Kamath S․, S.
    Cloud computing is currently a very popular computing paradigm as it provides ubiquitous, on-demand access as a service to computing resources via the Internet. In spite of offering marked advantages over the traditional style of computing, there are several issues related to load on the computing system and task scheduling to outperform the computation that need to be effectively solved in order to provide better quality of service to the service consumer. Task scheduling is a crucial research area since it affects the system load and performance; and there will always be scope for optimizing existing scheduling algorithms and propose efficient new task scheduling algorithms. Many task scheduling algorithms to resolve this problem have already been proposed - Particle Swarm Optimization, Ant Colony Optimization, Genetic algorithms, Artificial Bee Algorithm etc. In this paper, we propose a hybrid task scheduling algorithm that is based on combining the plus points of bio-inspired algorithms like Ant Colony Optimization and Artificial Bee Algorithm. We show were the strong points of both these algorithms can be utilized and incorporated in order to optimize task scheduling in the cloud algorithm. It is observed that the proposed algorithm gave an improvement of about 19% when compared to the default FCFS scheduling strategy, 11% better than ABC algorithm and performed 9% better than the conventional ACO based task scheduling. © 2014 IEEE.
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    An hybrid bio-inspired task scheduling algorithm in clouds environment
    (Institute of Electrical and Electronics Engineers Inc., 2014) Madivi, R.; Kamath S․, S.
    Cloud computing is currently a very popular computing paradigm as it provides ubiquitous, on-demand access as a service to computing resources via the Internet. In spite of offering marked advantages over the traditional style of computing, there are several issues related to load on the computing system and task scheduling to outperform the computation that need to be effectively solved in order to provide better quality of service to the service consumer. Task scheduling is a crucial research area since it affects the system load and performance; and there will always be scope for optimizing existing scheduling algorithms and propose efficient new task scheduling algorithms. Many task scheduling algorithms to resolve this problem have already been proposed - Particle Swarm Optimization, Ant Colony Optimization, Genetic algorithms, Artificial Bee Algorithm etc. In this paper, we propose a hybrid task scheduling algorithm that is based on combining the plus points of bio-inspired algorithms like Ant Colony Optimization and Artificial Bee Algorithm. We show were the strong points of both these algorithms can be utilized and incorporated in order to optimize task scheduling in the cloud algorithm. It is observed that the proposed algorithm gave an improvement of about 19% when compared to the default FCFS scheduling strategy, 11% better than ABC algorithm and performed 9% better than the conventional ACO based task scheduling. © 2014 IEEE.
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    Hybridization Approach to Optimize the Expected Loss of Demand in Drone Delivery Scheduling
    (Institute of Electrical and Electronics Engineers Inc., 2024) Harsha, S.S.; Muddi, K.S.; Jindrali, S.S.; Reji, S.; Das, M.; Mohan, B.R.
    This paper explores a hybrid-optimization approach for reducing the expected loss of delivery in drone delivery.This paper aims to give a deep knowledge about drone scheduling using machine learning and bio-optimized approaches. Using hybridization of K-Mean Clustering algorithms and Genetic algorithms, the paper makes a comparison between the performance of the above algorithm with the hybridization of hierarchical agglomerative clustering algorithms and ant colony optimization algorithms, resulting in valuable insights into drone delivery efficiency and reliability. © 2024 IEEE.