Simulated Annealing as an Intensification Component in Hybrid Population-based Metaheuristics

Anghinolfi, Davide; Paolucci, Massimo

doi:10.5772/5570

Cited by 3 publications

(3 citation statements)

References 76 publications

(84 reference statements)

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“…Metaheuristics such as SA, GA, ACO, and PSO are all stochastic in nature, with certain features that allow them to avoid confinement in local optima. SA differs from these other metaheuristics in that it is a trajectory based algorithm, relying on careful modifications of a single solution rather than modifying a population of solutions [65]. At each iteration of the SA algorithm, the cost function of two solutions are compared, the first being the solution from the previous iteration, and the second being the solution of the current iteration -which is in itself a slight modification to the solution from the previous iteration.…”

Section: Simulated Annealingmentioning

confidence: 99%

Thermal and mechanical analysis of a sodium-cooled solar receiver operating under a novel heliostat aiming point strategy

et al. 2018

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Section: Simulated Annealingmentioning

confidence: 99%

Thermal and mechanical analysis of a sodium-cooled solar receiver operating under a novel heliostat aiming point strategy

et al. 2018

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“…However, the use of a single method sometimes fails to optimize the problems when a huge number of features are applied or there is a high degree of epistasis between features [24]. For this reason, many researchers have proposed the hybrid approaches to combine complementary strengths and to overcome the drawbacks of single methods by embedding in them one or more steps involving alternative techniques [25]. In this study, we implemented the HSA similar to Yarpiz in Reference [26] by embedding a multi-layer perceptron neural network in SA as presented in Figure 6.…”

Section: Feature Selectionmentioning

confidence: 99%

Automated Diabetic Retinopathy Screening System Using Hybrid Simulated Annealing and Ensemble Bagging Classifier

et al. 2018

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Diabetic Retinopathy (DR) is the leading cause of blindness in working-age adults globally. Primary screening of DR is essential, and it is recommended that diabetes patients undergo this procedure at least once per year to prevent vision loss. However, in addition to the insufficient number of ophthalmologists available, the eye examination itself is labor-intensive and time-consuming. Thus, an automated DR screening method using retinal images is proposed in this paper to reduce the workload of ophthalmologists in the primary screening process and so that ophthalmologists may make effective treatment plans promptly to help prevent patient blindness. First, all possible candidate lesions of DR were segmented from the whole retinal image using a combination of morphological-top-hat and Kirsch edge-detection methods supplemented by pre- and post-processing steps. Then, eight feature extractors were utilized to extract a total of 208 features based on the pixel density of the binary image as well as texture, color, and intensity information for the detected regions. Finally, hybrid simulated annealing was applied to select the optimal feature set to be used as the input to the ensemble bagging classifier. The evaluation results of this proposed method, on a dataset containing 1200 retinal images, indicate that it performs better than previous methods, with an accuracy of 97.08%, a sensitivity of 90.90%, a specificity of 98.92%, a precision of 96.15%, an F-measure of 93.45% and the area under receiver operating characteristic curve at 98.34%.

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“…),Liao et al (2007),Pan et al (2008),Tseng et al (2008),Anghinolfi et al (2009),Correa et al (2006),Yin (2004), Jin et al (TNEP) (2007), Unler and Murat (…”

mentioning

confidence: 99%

New Hybrid Discrete PSO for Solving Non Convex Trim Loss Problem

Pant

Deep

Chauhan

2012

International Journal of Applied Evolutionary Computation

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Trim loss minimization is the most common problem that arises during the cutting process, when products with variable width or length are to be produced in bulk to satisfy customer demands from limited available/stocked materials. The aim is to minimize inevitable waste material. Under various environmental and physical constraints, the trim loss problem is highly constrained, non convex, nonlinear, and with integer restriction on all variables. Due to the highly complex nature of trim loss problem, it is not easy for manufacturers to select an appropriate method that provides a global optimal solution, satisfying all restrictions. This paper proposes a discrete variant of PSO, which embeds a mutation operator, namely power mutation during the position update stage. The proposed variant is named as Hybrid Discrete PSO (HDPSO). Binary variables in HDPSO are generated using sigmoid function with its domain derived from position update equation. Four examples with different levels of complexity are solved and results are compared with two recently developed GA and PSO variants. The computational studies indicate the competitiveness of proposed variant over other considered methods.

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Simulated Annealing as an Intensification Component in Hybrid Population-based Metaheuristics

Cited by 3 publications

References 76 publications

Thermal and mechanical analysis of a sodium-cooled solar receiver operating under a novel heliostat aiming point strategy

Thermal and mechanical analysis of a sodium-cooled solar receiver operating under a novel heliostat aiming point strategy

Automated Diabetic Retinopathy Screening System Using Hybrid Simulated Annealing and Ensemble Bagging Classifier

New Hybrid Discrete PSO for Solving Non Convex Trim Loss Problem

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