Parameter Adaptation for Ant Colony System Algorithm using Hidden Markov Model for TSP Problems

Bouzbita, Safae; Afia, Abdellatif El; Faizi, Rdouan

doi:10.1145/3230905.3230962

Cited by 11 publications

(1 citation statement)

References 22 publications

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“…The heuristic algorithms such as Ant Colony Optimization ( Dorigo and Caro, 1999 ), Particle Swarm Optimization ( Wang et al, 2003 ), Bat Algorithm ( YANG, 2010 ), Pigeon Swarm Algorithm ( Duan and Qiu, 2019 ) are all inspired by swarm intelligence and used to solve the NP-hard problem effectively. The heuristic algorithms generally appear to fall into local optima and slow convergence as the scale of the problem continues to expand ( Bouzbita et al, 2018 ; de S. Alves et al, 2018 ). Many improved methods have been proposed to optimize traditional accurate applications.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Swarming Algorithm With Van Der Waals Force

Zhang

Hongda

Sun

et al. 2022

Front. Bioeng. Biotechnol.

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This paper proposes a hybrid swarming algorithm based on Ant Colony Optimization and Physarum Polycephalum Algorithm. And the Van Der Waals force is first applied to the pheromone update mechanism of the hybrid algorithm. The improved method can prevent premature convergence into the local optimal solution. Simulation results show the proposed approach has excellent in solving accuracy and convergence time. We also compare the improved algorithm with other advanced algorithms and the results show that our algorithm is more accurate than the literature algorithms. In addition, we use the capitals of 35 Asian countries as an example to verify the robustness and versatility of the hybrid algorithm.

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