Performance comparison of particle swarm optimization and genetic algorithm for inverse surface radiation problem

Lee, Kyun Ho; Kim, Ki Wan

doi:10.1016/j.ijheatmasstransfer.2015.04.075

Cited by 40 publications

(14 citation statements)

References 20 publications

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“…PSO is a stochastic and population-based adaptive optimization method inspired by social behavior of bird ocks. As one of the versatile and e cient swarm intelligence techniques, PSO has attracted increasing attention and been widely applied in various areas [26][27][28][29]. The outstanding feature of PSO is its new solution generation mechanism, which distinguishes it from other biologically inspired optimization techniques.…”

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

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Simultaneous Optimization of Joint Edge Geometry and Process Parameters in Gas Metal Arc Welding Using Integrated ANN - PSO Approach

2017

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Abstract. Gas Metal Arc Welding (GMAW) is one of the most extensively used processes in automated welding due to its high productivity. However, to simultaneously achieve several con icting objectives such as reducing production time, increasing product quality, full penetration, proper joint edge geometry, and optimal selection of process parameters, a multi-criteria optimization procedure must be used. The aim of this research is to develop a multi-criteria modeling and optimization procedure for GMAW process. To simultaneously predict Weld Bead Geometry (WBG) characteristics and Heat-A ected Zone (HAZ), a Back Propagation Neural Network (BPNN) has been proposed. The experimentally derived data sets are used in training and testing of the network. Results demonstrate that the nely tuned BPNN model can closely simulate actual GMAW process with less than 1% error. Next, to simultaneously optimize process characteristics, the BPNN model is inserted into a Particle Swarm Optimization (PSO) algorithm. The proposed technique determines a set of values for parameters and the workpiece groove angle in such a way that a pre-speci ed WBG is achieved while the HAZ of the weld joint is minimized. Optimal results are veri ed through additional experiments.

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Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

“…Using both the best and the worst particle positions in the improved PSO algorithm accelerates nding of the optimal solution. Particle positioning is accomplished by modifying the particle parameters, including speed and position (V i and X i ), de ned by the following expressions [27].…”

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Simultaneous Optimization of Joint Edge Geometry and Process Parameters in Gas Metal Arc Welding Using Integrated ANN - PSO Approach

2017

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“…Lee and Kim (2015) compared the performance of the genetic algorithm and particle swarm optimization approaches forinverse surface radiation problems.…”

Section: Literature Review;-mentioning

confidence: 99%

Comparison of Some Classical and Meta-Heuristic Optimization Techniques in the Estimation of the Logit Model Parameters.

Akkuş¹,

Demir²

2016

IJAR

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The aim of this study is to discuss the success of the Genetic Algorithm (GA) approach compared with the conventional Newton-Raphson (NR) and Nelder-Mead (NM) algorithms in the estimation of the Binary Logit Model (BLM) parameters over a simulated data and a real data set on Alopecia disease. NR algorithm requires restrictive assumptions such as the continuity of the objective function and determination of starting points for the model parameters in iterative process. As for the NM algorithm, it does not require differentiable objective function but still suffers from the starting point problem. In this study, the best set of parameters that maximize the likelihood function in BLM is found using both NR and NM algorithms. Then, considering the limitations of the conventional methods, the success of GA is investigated on condition that all the assumptions of the NR and NM methods are satisfied. The results show that when the assumptions of the classical techniques are valid, the GA approach can achieve to obtain very close result to NR and NM. This also implies that it is a good alternative to the NR and NM methods when the requirements of the classical methods cannot be satisfied. Model results of NR, NM and GA are compared in terms of the estimated values and the maximum likelihood function value.Copy Right, IJAR, 2016,. All rights reserved. …………………………………………………………………………………………………….... Introduction:-When we study with a categorical dependent variable, regression model parameters can be estimated using the Maximum Likelihood Estimator (MLE). The aim is to find the best estimation result which maximizes the likelihood or log-likelihood functions. Because the likelihood equations obtained by taking the first order derivatives of the likelihood function with respect to the parameters are not linear, some iterative procedures such as Gauss-Newton, NewtonRaphson, Levenberg-Marquardt, Direct Search and Steepest Descent are developed within the category of the classic optimization techniques for the solution of an unconstrained nonlinear optimization problem.These algorithms require restrictive assumptions such as the continuity of the likelihood function. They also have the risk of not finding the best solution due to the risk of getting stuck on local optimums relative to the selected starting points. Newton-Raphson (NR) algorithm is the most popular one among the classical techniques. Nelder-Mead (NM) algorithm can be seen as a good alternative to the NR algorithm. The most important advantage of NM algorithm is that it eliminates the requirement of the objective function (in our case, the likelihood function) to be differentiable.

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“…It is difficult to calculate the derivative information required by traditional optimization algorithms. Intelligent optimization algorithms, such as the genetic algorithm (GA) [12], particle swarm optimization (PSO) [13], differential evolution (DE) [14], ant colony optimization (ACO) [15], salp swarm algorithm (SSA) [16], artificial bee colony (ABC) [17], and cuckoo search (CS) [18], do not require any derivative information and can perform global search [19][20][21], so using them for finding the parameters in the empirical part is a viable alternative. Amongst these algorithms, CS is a comparatively new one, initially introduced by Yang and Deb [22].…”

Section: Introductionmentioning

confidence: 99%

Molten steel temperature prediction using a hybrid model based on information interaction-enhanced cuckoo search

Yang

Zhang

2020

Neural Comput & Applic

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This article presents a hybrid model for predicting the temperature of molten steel in a ladle furnace (LF). Unique to the proposed hybrid prediction model is that its neural network-based empirical part is trained in an indirect way since the target outputs of this part are unavailable. A modified cuckoo search (CS) algorithm is used to optimize the parameters in the empirical part. The search of each individual in the traditional CS is normally performed independently, which may limit the algorithm’s search capability. To address this, a modified CS, information interaction-enhanced CS (IICS), is proposed in this article to enhance the interaction of search information between individuals and thereby the search capability of the algorithm. The performance of the proposed IICS algorithm is first verified by testing on two benchmark sets (including 16 classical benchmark functions and 29 CEC 2017 benchmark functions) and then used in optimizing the parameters in the empirical part of the proposed hybrid prediction model. The proposed hybrid model is applied to actual production data from a 300 t LF at Baoshan Iron & Steel Co. Ltd, one of China's most famous integrated iron and steel enterprises, and the results show that the proposed hybrid prediction model is effective with comparatively high accuracy.

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Performance comparison of particle swarm optimization and genetic algorithm for inverse surface radiation problem

Cited by 40 publications

References 20 publications

Simultaneous Optimization of Joint Edge Geometry and Process Parameters in Gas Metal Arc Welding Using Integrated ANN - PSO Approach

Simultaneous Optimization of Joint Edge Geometry and Process Parameters in Gas Metal Arc Welding Using Integrated ANN - PSO Approach

Comparison of Some Classical and Meta-Heuristic Optimization Techniques in the Estimation of the Logit Model Parameters.

Molten steel temperature prediction using a hybrid model based on information interaction-enhanced cuckoo search

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