Performance Evaluation by Ant Colony Algorithm

Chen, Jianfeng; Wu, Jinzhao

doi:10.1109/itcs.2010.40

Cited by 1 publication

(2 citation statements)

References 5 publications

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“…Currently, many algorithms, such as the PSO algorithm (Pan, Li, Zhou, Li, & Gao, 2013), the ant colony algorithm (Chen & Wu, 2010), the GA (Suzuki, 1995), the chicken swarm optimization algorithm (Wu, Xu, & Kong, 2016), and the bat algorithm (Chen, Peng, He, & Yang, 2018), use…”

Section: Global Convergence For the Slp Algorithmmentioning

confidence: 99%

“…Currently, many algorithms, such as the PSO algorithm (Pan, Li, Zhou, Li, & Gao, ), the ant colony algorithm (Chen & Wu, ), the GA (Suzuki, ), the chicken swarm optimization algorithm (Wu, Xu, & Kong, ), and the bat algorithm (Chen, Peng, He, & Yang, ), use Markov chain modelling to analyse convergence because it strongly analyses the convergence of random algorithms and probability processes (Wu et al, ). If the SLP algorithm is the random search algorithm, the convergence rule of random search algorithm (Solis & Wets, ) is used to verify the global convergence of the SLP algorithm based on Markov chain modelling.…”

Section: Slp Convergence Verificationmentioning

confidence: 99%

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A novel optimal PID controller autotuning design based on the SLP algorithm

Pongfai

Zhang

et al. 2020

Expert Systems

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A novel optimal proportional integral derivative (PID) autotuning controller design based on a new algorithm approach, the “swarm learning process” (SLP) algorithm, is proposed. It improves the convergence and performance of the autotuning PID parameter by applying the swarm and learning algorithm concepts. Its convergence is verified by two methods, global convergence and characteristic convergence. In the case of global convergence, the convergence rule of a random search algorithm is employed to judge, and Markov chain modelling is used to analyse. The superiority of the proposed method, in terms of characteristic convergence and performance, is verified through the simulation based on the automatic voltage regulator and direct current motor control system. Verification is performed by comparing the results of the proposed model with those of other algorithms, that is, the ant colony optimization with a new constrained Nelder–Mead algorithm, the genetic algorithm (GA), the particle swarm optimization (PSO) algorithm, and a neural network (NN). According to the global convergence analysis, the proposed method satisfies the convergence rule of the random search algorithm. With respect to the characteristic convergence and performance, the proposed method provides a better response than the GA, the PSO, and the NN for both control systems.

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