Hidden Markov Model Classifier for the Adaptive Particle Swarm Optimization

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

doi:10.1007/978-3-319-58253-5_1

Cited by 21 publications

(9 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, recent studies have studied how hidden Markov models improve the optimization algorithms. In [75], authors studied the relation of particle distances to determine the state of the particle swarm optimizer. The states were inspired by [38,76].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Therefore, we apply a discretization process to the evolutionary factor f of each inner-phase of the PSO. The discretization process used in this work is defined in [75] and corresponds to identifying the interval in which the calculated evolutionary factor belongs. The seven defined intervals are:…”

Section: Hmm-pso Integrationmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

show abstract

“…As a result, the RSM is integrating with the FEA to build the mathematical models in the present study. Then the PSO algorithm [36][37][38][39] is utilized. PSO is a multi-objective optimization technique that gives high precision results and fast convergence capabilities for linear and weak non-linear models.…”

Section: Introductionmentioning

confidence: 99%

Efficient Hybrid Method of FEA‐Based RSM and PSO Algorithm for Multi‐Objective Optimization Design for a Compliant Rotary Joint for Upper Limb Assistive Device

Chau

Dao

et al. 2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

This paper proposes an efficient hybrid methodology for multi-objective optimization design of a compliant rotary joint (CRJ). A combination of the Taguchi method (TM), finite element analysis (FEA), the response surface method (RSM), and particle swarm optimization (PSO) algorithm is developed to solving the optimization problem. Firstly, the TM is applied to determine the number of numerical experiments. And then, 3D models of the CRJ is built for FEA simulation, and mathematical models are formed using the RSM. Subsequently, the suitability of the regression equation is assessed. At the same time, the calculation of weight factors is identified based on the series of statistical equations. Based on the well-established equations, a minimum mass and a maximum rotational angle are simultaneously optimized through the PSO algorithm. Analysis of variance is used to analyze the contribution of design variables. The behavior of the proposed method is compared to the adaptive elitist differential evolution and cuckoo search algorithm through the Wilcoxon signed rank test and Friedman test. The results determined the weight factors of the mass and rotational angle are about 0.4983 and 0.5017, respectively. The results found that the optimum the mass and rotational angle are 0.0368 grams and 59.1928 degrees, respectively. It revealed that the maximum stress of 335 MPa can guarantee a long working time. The results showed that the proposed hybrid method outperforms compared to other evolutionary algorithms. The predicted results are close to the validation results. The proposed method is useful for related engineering fields.

show abstract

“…Especially, the Hidden Markov Model (HMM) [18]. HMMs success is due to ability to deal with the variability by means of stochastic modeling.…”

Section: Introductionmentioning

confidence: 99%

“…The main idea is to predict the best cooling law parameter based on history of the run. To do that, first we train the HMM model by updating its parameters [18]. Then we proceed to a classification process through the Viterbi algorithm [18] which gives the most probable cooling law parameter.…”

Section: Introductionmentioning

confidence: 99%

A Self-Tuned Simulated Annealing Algorithm Using Hidden Markov Model

Limam¹,

Afia²,

Chiheb³

2018

IJECE

View full text Add to dashboard Cite

Simulated Annealing algorithm (SA) is a well-known probabilistic heuristic. It mimics the annealing process in metallurgy to approximate the global minimum of an optimization problem. The SA has many parameters which need to be tuned manually when applied to a specific problem. The tuning may be difficult and time-consuming. This paper aims to overcome this difficulty by using a self-tuning approach based on a machine learning algorithm called Hidden Markov Model (HMM). The main idea is allowing the SA to adapt his own cooling law at each iteration, according to the search history. An experiment was performed on many benchmark functions to show the efficiency of this approach compared to the classical one.

show abstract

Hidden Markov Model Classifier for the Adaptive Particle Swarm Optimization

Cited by 21 publications

References 28 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

Efficient Hybrid Method of FEA‐Based RSM and PSO Algorithm for Multi‐Objective Optimization Design for a Compliant Rotary Joint for Upper Limb Assistive Device

A Self-Tuned Simulated Annealing Algorithm Using Hidden Markov Model

Contact Info

Product

Resources

About