Particle Swarm Optimization and Tabu Search Hybrid Algorithm for Flexible Job Shop Scheduling Problem – Analysis of Test Results

Toshev, Asen Lyubomirov

doi:10.2478/cait-2019-0034

Cited by 14 publications

(13 citation statements)

References 33 publications

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“…Findings of the study suggested that the iSSA was not only able to produce lower RMSE, but also capable to converge faster with lower value. In the future, the study on the dengue outbreak prediction will be extended by referring to the methods applied in [32,33,34,35].…”

Section: Discussionmentioning

confidence: 99%

Predictive Analysis of Dengue Outbreak Based on an Improved Salp Swarm Algorithm

Mustaffa

Sulaiman

Rosli

et al. 2020

Cybernetics and Information Technologies

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The purpose of this study is to enhance the exploration capability of conventional Salp Swarm Algorithm (SSA) with the inducing of Levy Flight. With such modification, it will assist the SSA from trapping in local optimum. The proposed approach, which is later known as an improved SSA (iSSA) is employed in monthly dengue outbreak prediction. For that matter, monthly dataset of rainfall, humidity, temperature and number of dengue cases were employed, which render prediction information. The efficiency of the proposed algorithm is evaluated using Root Mean Square Error (RMSE), and compared against the conventional SSA and Ant Colony Optimization (ACO). The obtained results suggested that the iSSA was not only able to produce lower RMSE, but also capable to converge faster at lower rate as well.

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Section: Discussionmentioning

confidence: 99%

Predictive Analysis of Dengue Outbreak Based on an Improved Salp Swarm Algorithm

Mustaffa

Sulaiman

Rosli

et al. 2020

Cybernetics and Information Technologies

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“…Another modified metaheuristic is presented in Luan et al (2019) , where the whale algorithm is adapted for the FJSP and proved with 15 test problems. A HA that combines PSO and TS is described in Toshev (2019) and the performance of the algorithm is analyzed in 12 benchmark problems. A distributed approach for implementing a PSO method is explained in Caldeira & Gnanavelbabu (2019) and proved with 6 different benchmark datasets.…”

Section: State Of the Art Of Fjspmentioning

confidence: 99%

A global-local neighborhood search algorithm and tabu search for flexible job shop scheduling problem

Serna¹,

Seck-Tuoh-Mora²,

Medina-Marín³

et al. 2021

PeerJ Computer Science

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The Flexible Job Shop Scheduling Problem (FJSP) is a combinatorial problem that continues to be studied extensively due to its practical implications in manufacturing systems and emerging new variants, in order to model and optimize more complex situations that reflect the current needs of the industry better. This work presents a new metaheuristic algorithm called the global-local neighborhood search algorithm (GLNSA), in which the neighborhood concepts of a cellular automaton are used, so that a set of leading solutions called smart-cells generates and shares information that helps to optimize instances of the FJSP. The GLNSA algorithm is accompanied by a tabu search that implements a simplified version of the Nopt1 neighborhood defined in Mastrolilli & Gambardella (2000) to complement the optimization task. The experiments carried out show a satisfactory performance of the proposed algorithm, compared with other results published in recent algorithms, using four benchmark sets and 101 test problems.

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“…Server consolidation aims to minimize the number of servers required for placing Virtual Machines [17]. The changing shape strengthens the particles that move toward the clustering vector of local and global optimal solutions [18]. The basic idea is to find the particle's potential position according to its own experience and that of its neighbours.…”

Section: Related Workmentioning

confidence: 99%

Virtual Machine Placement Using Energy Efficient Particle Swarm Optimization in Cloud Datacenter

Madhumala

Tiwari²,

2021

Cybernetics and Information Technologies

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Efficient resource allocation through Virtual machine placement in a cloud datacenter is an ever-growing demand. Different Virtual Machine optimization techniques are constructed for different optimization problems. Particle Swam Optimization (PSO) Algorithm is one of the optimization techniques to solve the multidimensional virtual machine placement problem. In the algorithm being proposed we use the combination of Modified First Fit Decreasing Algorithm (MFFD) with Particle Swarm Optimization Algorithm, used to solve the best Virtual Machine packing in active Physical Machines to reduce energy consumption; we first screen all Physical Machines for possible accommodation in each Physical Machine and then the Modified Particle Swam Optimization (MPSO) Algorithm is used to get the best fit solution.. In our paper, we discuss how to improve the efficiency of Particle Swarm Intelligence by adapting the efficient mechanism being proposed. The obtained result shows that the proposed algorithm provides an optimized solution compared to the existing algorithms.

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Particle Swarm Optimization and Tabu Search Hybrid Algorithm for Flexible Job Shop Scheduling Problem – Analysis of Test Results

Cited by 14 publications

References 33 publications

Predictive Analysis of Dengue Outbreak Based on an Improved Salp Swarm Algorithm

Predictive Analysis of Dengue Outbreak Based on an Improved Salp Swarm Algorithm

A global-local neighborhood search algorithm and tabu search for flexible job shop scheduling problem

Virtual Machine Placement Using Energy Efficient Particle Swarm Optimization in Cloud Datacenter

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