A new pheromone updating strategy in ant colony optimization

Jun, Sun-Young; Xiong, Shengwu; Guo, Fu-Ming

doi:10.1109/icmlc.2004.1380766

Cited by 17 publications

References 4 publications

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An ACO Algorithm with Adaptive Volatility Rate of Pheromone Trail

Zhang

Huang

Qin

et al. 2007

Computational Science – ICCS 2007

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Ant colony optimization (ACO) has been proved to be one of the best performing algorithms for NP-hard problems as TSP. The volatility rate of pheromone trail is one of the main parameters in ACO algorithms. It is usually set experimentally in the literatures for the application of ACO. The present paper proposes an adaptive strategy for the volatility rate of pheromone trail according to the quality of the solutions found by artificial ants. The strategy is combined with the setting of other parameters to form a new ACO algorithm. Finally, the experimental results of computing traveling salesman problems indicate that the proposed algorithm is more effective than other ant methods.

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An ACO Algorithm with Adaptive Volatility Rate of Pheromone Trail

Zhang

Huang

Qin

et al. 2007

Computational Science – ICCS 2007

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Uncovering the social interaction network in swarm intelligence algorithms

et al. 2020

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Swarm intelligence is the collective behavior emerging in systems with locally interacting components. Because of their self-organization capabilities, swarm-based systems show essential properties for handling real-world problems, such as robustness, scalability, and flexibility. Yet, we fail to understand why swarm-based algorithms work well, and neither can we compare the various approaches in the literature. The absence of a common framework capable of characterizing these several swarm-based algorithms, transcending their particularities, has led to a stream of publications inspired by different aspects of nature without a systematic comparison over existing approaches. Here we address this gap by introducing a network-based framework—the swarm interaction network—to examine computational swarm-based systems via the optics of the social dynamics. We investigate the structure of social interaction in four swarm-based algorithms, showing that our approach enables researchers to study distinct algorithms from a common viewpoint. We also provide an in-depth case study of the Particle Swarm Optimization, revealing that different communication schemes tune the social interaction in the swarm, controlling the swarm search mode. With the swarm interaction network, researchers can study swarm algorithms as systems, removing the algorithm particularities from the analyses while focusing on the structure of the swarm social interaction.

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