Hidden markov model control of inertia weight adaptation for Particle swarm optimization

Afia, Abdellatif El; Sarhani, Malek; Aoun, Oussama

doi:10.1016/j.ifacol.2017.08.2030

Cited by 20 publications

(4 citation statements)

References 22 publications

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“…In [38], four inner-phases (or states) through which PSO moves are defined: exploration, exploitation, convergence, and jumping-out. A previous work describes these states as a Markov chain [93] (detailed in Figure 4). This chain corresponds to the hidden chain that will be deduced using decoding and learning tasks.…”

Section: Hmm-pso Integrationmentioning

confidence: 99%

A Knowledge-Based Hybrid Approach on Particle Swarm Optimization Using Hidden Markov Models

et al. 2021

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Bio-inspired computing is an engaging area of artificial intelligence which studies how natural phenomena provide a rich source of inspiration in the design of smart procedures able to become powerful algorithms. Many of these procedures have been successfully used in classification, prediction, and optimization problems. Swarm intelligence methods are a kind of bio-inspired algorithm that have been shown to be impressive optimization solvers for a long time. However, for these algorithms to reach their maximum performance, the proper setting of the initial parameters by an expert user is required. This task is extremely comprehensive and it must be done in a previous phase of the search process. Different online methods have been developed to support swarm intelligence techniques, however, this issue remains an open challenge. In this paper, we propose a hybrid approach that allows adjusting the parameters based on a state deducted by the swarm intelligence algorithm. The state deduction is determined by the classification of a chain of observations using the hidden Markov model. The results show that our proposal exhibits good performance compared to the original version.

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Section: Hmm-pso Integrationmentioning

confidence: 99%

A Knowledge-Based Hybrid Approach on Particle Swarm Optimization Using Hidden Markov Models

et al. 2021

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“…We used SCIP 3.2.1 for the raison that is the best in open-source and free tools [23]. Moreover, for SVM algorithm and model selection, we used the package e1071 [23] of the language R 3.2.5 known to be among the most performant languages in implementation of SVM algorithms [13].…”

Section: Experimentation Configurationmentioning

confidence: 99%

“…Fourthly, when looking at model selection and the performance of algorithms, there are techniques used to tune parameters such as Fuzzy Logic controller for Ant Colony System (ACS) epsilon parameter [12]. Also, [13] and [14] used Hidden Markov Model (HMM) algorithm to tune the Particle Swarm optimization population size and acceleration factors parameters. Besides, authors in [15] used HMM to tune the inertia weight parameter of the Particle Swarm Optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

Adapted Branch-and-Bound Algorithm Using SVM With Model Selection

Kabbaj¹,

Abdellatif²

2019

IJECE

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<span lang="EN-US">Branch-and-Bound algorithm is the basis for the majority of solving methods in mixed integer linear programming. It has been proving its efficiency in different fields. In fact, it creates little by little a tree of nodes by adopting two strategies. These strategies are variable selection strategy and node selection strategy. In our previous work, we experienced a methodology of learning branch-and-bound strategies </span><span lang="EN-US">using regression-based support vector machine twice. That methodology allowed firstly to exploit information from previous executions of Branch-and-Bound algorithm on other instances. Secondly, it created information channel between node selection strategy and variable branching strategy. And thirdly, it gave good results in term of running time comparing to standard Branch-and-Bound algorithm. In this work, we will focus on increasing SVM performance by using cross validation coupled with model selection. </span>

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“…Beside the use of FLC as a controller of parameters, other machine learning algorithms have been proposed by several researchers for the same purpose. We can cite the following works [19][20][21][22][23][24][25][26][27][28][29] as examples.…”

mentioning

confidence: 99%

The behaviour of ACS-TSP algorithm when adapting both pheromone parameters using fuzzy logic controller

Bouzbita

Afia

Faizi

2020

IJECE

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In this paper, an evolved ant colony system (ACS) is proposed by dynamically adapting the responsible parameters for the decay of the pheromone trails 𝜉 and 𝜌 using fuzzy logic controller (FLC) applied in the travelling salesman problems (TSP). The purpose of the proposed method is to understand the effect of both parameters 𝜉 and 𝜌 on the performance of the ACS at the level of solution quality and convergence speed towards the best solutions through studying the behavior of the ACS algorithm during this adaptation. The adaptive ACS is compared with the standard one. Computational results show that the adaptive ACS with dynamic adaptation of local pheromone parameter 𝜉 is more effective compared to the standard ACS.

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Hidden markov model control of inertia weight adaptation for Particle swarm optimization

Cited by 20 publications

References 22 publications

A Knowledge-Based Hybrid Approach on Particle Swarm Optimization Using Hidden Markov Models

A Knowledge-Based Hybrid Approach on Particle Swarm Optimization Using Hidden Markov Models

Adapted Branch-and-Bound Algorithm Using SVM With Model Selection

The behaviour of ACS-TSP algorithm when adapting both pheromone parameters using fuzzy logic controller

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