Tabu Search: A Comparative Study

Pirim, Harun; Bayraktar, Engin; Ekşioğlu, Burak

doi:10.5772/5637

Cited by 26 publications

(13 citation statements)

References 79 publications

(49 reference statements)

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“…Simulating Annealing (SA) introduced by Kirkpatrick [40] has greater robustness as opposed to simple local search owing to the fact that it also accepts worse solutions with some probability [41,42]. SA has been popularly employed in the solution of hard combinatory problems.…”

Section: Simulated Annealing Algorithmmentioning

confidence: 99%

“…In this regard, SA could accept solutions, which, as opposed to past ones, are neither better nor much worse, which allows escape from local optimum and discovery of the global [43,44]. The authors in [41] provided a generic SA algorithm for problem of maximization. SA begins with best weight solution (s) picked out from the pool of population within the genetic algorithm.…”

Section: Simulated Annealing Algorithmmentioning

confidence: 99%

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Robust features extraction for general fish classification

Alsmadi

Tayfour

Alkhasawneh

et al. 2019

IJECE

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Image recognition process could be plagued by many problems including noise, overlap, distortion, errors in the outcomes of segmentation, and impediment of objects within the image. Based on feature selection and combination theory between major extracted features, this study attempts to establish a system that could recognize fish object within the image utilizing texture, anchor points, and statistical measurements. Then, a generic fish classification is executed with the application of an innovative classification evaluation through a meta-heuristic algorithm known as Memetic Algorithm (Genetic Algorithm with Simulated Annealing) with back-propagation algorithm (MA-B Classifier). Here, images of dangerous and non-dangerous fish are recognized. Images of dangerous fish are further recognized as Predatory or Poison fish family, whereas families of non-dangerous fish are classified into garden and food family. A total of 24 fish families were used in testing the proposed prototype, whereby each family encompasses different number of species. The process of classification was successfully undertaken by the proposed prototype, whereby 400 distinct fish images were used in the experimental tests. Of these fish images, 250 were used for training phase while 150 were used for testing phase. The back-propagation algorithm and the proposed MA-B Classifier produced a general accuracy recognition rate of 82.25 and 90% respectively.

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Section: Simulated Annealing Algorithmmentioning

confidence: 99%

Section: Simulated Annealing Algorithmmentioning

confidence: 99%

Robust features extraction for general fish classification

Alsmadi

Tayfour

Alkhasawneh

et al. 2019

IJECE

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show abstract

“…A heuristic algorithm searches for an optimal solution at every iteration. If it finds a better solution, it makes the current solution as the optimal one, otherwise, the algorithm keeps the most recently found solution [101]. The pseudocode for a heuristic algorithm is described in Algorithm 1.…”

Section: B Algorithmsmentioning

confidence: 99%

Container Placement and Migration in Edge Computing: Concept and Scheduling Models

Oleghe

2021

IEEE Access

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Containers are a form of software virtualization, rapidly becoming the de facto way of providing edge computing services. Research on container-based edge computing is plentiful, and this has been buoyed by the increasing demand for single digit, milliseconds latency computations. A container scheduler is part of the architecture that is used to manage and orchestrate multiple container-based applications on heterogenous computing nodes. The scheduler decides how incoming computing requests are allocated to containers, which edge nodes the containers are placed on, and where already deployed containers are migrated to. This paper aims to clarify the concept of container placement and migration in edge servers and the scheduling models that have been developed for this purpose. The study illuminates the frameworks and algorithms upon which the scheduling models are built. To convert the problem to one that can be solved using an algorithm, the container placement problem in mostly abstracted using multi-objective optimization models or graph network models. The scheduling algorithms are predominantly heuristic-based algorithms, which are able to arrive at sub-optimal solutions very quickly. There is paucity of container scheduling models that consider distributed edge computing tasks. Research in decentralized scheduling systems is gaining momentum and the future outlook is in scheduling containers for mobile edge nodes.

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“…This enabled the designer to manipulate various combination of the design variables and better understand different design scenarios [43,44]. The optimisation was performed using the Multi-Objective Tabu Search (MOTS) algorithm [67][68][69], chosen for its suitability for this type of complex aerodynamic design problem [68,70,71]. However, for confirmation, the authors used optimisation results for this design case to be compared with the leading multi-objective Genetic Algorithmic (GA), NSGA-II.…”

Section: Related Workmentioning

confidence: 99%

Artificial Intelligence to Enhance Aerodynamic Shape Optimisation of the Aegis UAV

Azabi

Chai

Kipouros

2019

MAKE

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This article presents an optimisation framework that uses stochastic multi-objective optimisation, combined with an Artificial Neural Network (ANN), and describes its application to the aerodynamic design of aircraft shapes. The framework uses the Multi-Objective Particle Swarm Optimisation (MOPSO) algorithm and the obtained results confirm that the proposed technique provides highly optimal solutions in less computational time than other approaches to the same design problem. The main idea was to focus computational effort on worthwhile design solutions rather than exploring and evaluating all possible solutions in the design space. It is shown that the number of valid solutions obtained using ANN-MOPSO compared to MOPSO for 3000 evaluations grew from 529 to 1006 (90% improvement) with a penalty of only 8.3% (11 min) in computational time. It is demonstrated that including an ANN, the ANN-MOPSO with 3000 evaluations produced a larger number of valid solutions than the MOPSO with 5500 evaluations, and in 33% less computational time (64 min). This is taken as confirmation of the potential power of ANNs when applied to this type of design problem.

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Tabu Search: A Comparative Study

Cited by 26 publications

References 79 publications

Robust features extraction for general fish classification

Robust features extraction for general fish classification

Container Placement and Migration in Edge Computing: Concept and Scheduling Models

Artificial Intelligence to Enhance Aerodynamic Shape Optimisation of the Aegis UAV

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