Design of Microelectronic Cooling Systems Using a Thermodynamic Optimization Strategy Based on Cuckoo Search

Cruz–Duarte, Jorge M.; García-Pérez, Arturo; Amaya, Iván; Correa-Cely, Rodrigo; Romero‐Troncoso, Rene J.; Aviña-Cervantes, Juan Gabriel

doi:10.1109/tcpmt.2017.2706305

Cited by 10 publications

(5 citation statements)

References 32 publications

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“…We implemented Cuckoo Search Algorithm (CSA) with a population size N of 200, a step size ζ of 1.0, a probability change p of 0.5, a best-fitness change tolerance ε of 10 −15 , and a maximum number of stagnating iterations of 2000. These values were obtained after performing a preliminary study, which is out of this work's scope but can be consulted in [43].…”

Section: Methodsmentioning

confidence: 99%

Alternative Thresholding Technique for Image Segmentation Based on Cuckoo Search and Generalized Gaussians

et al. 2021

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Object segmentation is a widely studied topic in digital image processing, as to it can be used for countless applications in several fields. This process is traditionally achieved by computing an optimal threshold from the image intensity histogram. Several algorithms have been proposed to find this threshold based on different statistical principles. However, the results generated via these algorithms contradict one another due to the many variables that can disturb an image. An accepted strategy to achieve the optimal histogram threshold, to distinguish between the object and the background, is to estimate two data distributions and find their intersection. This work proposes a strategy based on the Cuckoo Search Algorithm (CSA) and the Generalized Gaussian (GG) distribution to assess the optimal threshold. To test this methodology, we carried out several experiments in synthetic and practical scenarios and compared our results against other well-known algorithms from the literature. These practical cases comprise a medical image database and our own generated database. The results in a simulated environment show an evident advantage of the proposed strategy against other algorithms. In a real environment, this ranks among the best algorithms, making it a reliable alternative.

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Section: Methodsmentioning

confidence: 99%

Alternative Thresholding Technique for Image Segmentation Based on Cuckoo Search and Generalized Gaussians

et al. 2021

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“…MHs are widely recognized because of their capabilities and performance in optimally solving complex engineering models [35]. Some representative MHs include the Differential Evolution [12], Particle Swarm Optimization [15], Cuckoo Search Algorithm [36], Spiral Optimization Algorithm [37], and Grey-Wolf Optimization [14], among others [38]. Definition 7 formally describes the general scheme for a metaheuristic according to [32].…”

Section: Metaheuristicsmentioning

confidence: 99%

Automatic Design of Metaheuristics for Practical Engineering Applications

et al. 2023

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It is common to find multiple metaheuristics to solve continuous optimization problems. However, choosing what optimizer may obtain the best results for a given task requires exhaustive evaluations that are highly application-dependent. Besides, it is necessary to find sufficiently good tuning parameters to achieve satisfactory performance with the selected approach. In this context, the automatic design of algorithms, particularly those based on heuristics, has been increasing in popularity in the previous years due to its undoubted relevance nowadays. This paper explores a novel approach based on hyper-heuristics to carefully select population-based search operators and their tuning parameters to generate metaheuristics capable of dealing with a given practical engineering problem. The proposed strategy is assessed using three highly relevant and illustrative problems: training Artificial Neural Networks, designing PID controllers, and modeling a calorimetric phenomenon based on fractional calculus. In addition, we implement three well-known optimization metaheuristics to compare achieved solutions via the proposed hyper-heuristic strategy, namely Particle Swarm Optimization, Genetic Algorithm, and Cuckoo Search. Results from extensive numerical tests prove that the customized metaheuristics are generally superior to the three wellknown algorithms, taking only a few iterations to converge to an optimal solution. This is an excellent indicator of alleviating the effort and expertise required to choose the proper methodology when dealing with real-valued optimization problems.

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“…A mathematical formulation of the BPP [59] is shown in (3). In this formulation, N = n i i stands for the set of n pieces (or bins), while y i ∈ {0, 1} is a binary variable indicating whether the bin i ∈ N has been used, and x ij indicates whether the piece j ∈ N is packed into bin i.…”

Section: ) Bin Packingmentioning

confidence: 99%

“…Therefore, single-objective problems can be split, most simplistically, into two categories concerning the nature of their domains, i.e., continuous and discrete. The former are usually present in practical engineering scenarios, e.g., for finding the channel width that minimizes entropy generation rate in a heat sink for electronic thermal management applications [3], and for assessing the thermophysical properties of a sample from its electric field distribution [4]. Literature is prolific about these problems, and also about those that consider both kinds of variables, e.g., [5].…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of Hyper-Heuristics on Combinatorial Optimization Problems

et al. 2020

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Hyper-heuristics aim at interchanging different solvers while solving a problem. The idea is to determine the best approach for solving a problem at its current state. This way, every time we make a move it gets us closer to a solution. The problem changes; so does its state. As a consequence, for the next move, a different solver may be invoked. Hyper-heuristics have been around for almost 20 years. However, combinatorial optimization problems date from way back. Thus, it is paramount to determine whether the efforts revolving around hyper-heuristic research have been targeted at the problems of the highest interest for the combinatorial optimization community. In this work, we tackle such an endeavor. We begin by determining the most relevant combinatorial optimization problems, and then we analyze them in the context of hyper-heuristics. The idea is to verify whether they remain as relevant when considering exclusively works related to hyper-heuristics. We find that some of the most relevant problem domains have also been popular for hyper-heuristics research. Alas, others have not and few efforts have been directed towards solving them. We identify the following problem domains, which may help in furthering the impact of hyper-heuristics: Shortest Path, Set Cover, Longest Path, and Minimum Spanning Tree. We believe that focusing research on ways for solving them may lead to an increase in the relevance and impact that hyperheuristics have on combinatorial optimization problems.

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Design of Microelectronic Cooling Systems Using a Thermodynamic Optimization Strategy Based on Cuckoo Search

Cited by 10 publications

References 32 publications

Alternative Thresholding Technique for Image Segmentation Based on Cuckoo Search and Generalized Gaussians

Alternative Thresholding Technique for Image Segmentation Based on Cuckoo Search and Generalized Gaussians

Automatic Design of Metaheuristics for Practical Engineering Applications

A Systematic Review of Hyper-Heuristics on Combinatorial Optimization Problems

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