Ant colony optimization for routing and load-balancing: survey and new directions

By Kwang Mong Sim, Weng Hong Sun. IEEE Trans. Syst. Man Cybern. Part A, 2003

548 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/f77f1d1d274042105cceebf17a57e680dfe1ce03

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Citations in the corpus

Ant colony optimization (2006, unknown): “Ant-based algorithms have given rise to several other routing algorithms, enhancing performance in a variety of wired network scenarios; see [71], [72] for a survey.”
Ant Colony Optimization: Overview and Recent Advances (2018, background): “Ant-based algorithms have given rise to several other routing algorithms, enhancing performance in a variety of wired network scenarios, see [44, 133] for a survey.”
Chemical-Reaction-Inspired Metaheuristic for Optimization (2010, background): “For example, it is natural to apply ACO to routing-related problems [26].”
Swarm intelligence based routing protocol for wireless sensor networks: Survey and future directions (2011, background): “Sim and Sun [80] presented a review of ACO approaches for routing and load balancing in wired networks.”
Darwin or Lamarck? Future Challenges in Evolutionary Algorithms for Knowledge Discovery and Data Mining (2014, background): “Ant Colony Optimization (ACO) models the collective intelligence of ants, which are transformed into optimization techniques [59].”
Smart Charging Strategy for Electric Vehicle Charging Stations (2018, background): “Ant Colony Optimization (ACO) is a category of swarm intelligence that analyzes the survival behavior pattern of the insects in solving complex optimization problems [38].”
AntHocNet: an adaptive nature-inspired algorithm for routing in mobile ad hoc networks (2005, background): “... features a multi-agent organization, stigmergic communication among the agents, distributed operations, use of a stochastic decision policy to construct solutions, stigmergic learning of the parameters of the decision policy, and so on. It has been applied with success to a variety of combinator”
Computational Intelligence in Wireless Sensor Networks: A Survey (2011, background): “Introductions to ant inspired algorithms and brief overviews of ant-based routing methods in WSNs are given in [82] and [83].”