Task optimization and scheduling of distributed cyber–physical system based on improved ant colony algorithm

Yi, Na; Xu, Jianjun; Yang, Limei; Huang, Lin

doi:10.1016/j.future.2020.03.051

Cited by 135 publications

(59 citation statements)

References 26 publications

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“…Yi et al [11] employed multiple pheromone change and heuristic functions methods. Moreover, they introduced mutation strategy and adaptive mechanism in order to reduce computational times of the ant colony (AC) algorithm, hasten up the convergence algorithm as well as increased the prediction accuracy.…”

Section: Improvement By Applying Construct Ant Solutionsmentioning

confidence: 99%

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Recent research in finding the optimal path by ant colony optimization

Sari

Mohamad

2021

Bulletin EEI

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The computation of the optimal path is one of the critical problems in graph theory. It has been utilized in various practical ranges of real world applications including image processing, file carving and classification problem. Numerous techniques have been proposed in finding optimal path solutions including using ant colony optimization (ACO). This is a nature-inspired metaheuristic algorithm, which is inspired by the foraging behavior of ants in nature. Thus, this paper study the improvement made by many researchers on ACO in finding optimal path solution. Finally, this paper also identifies the recent trends and explores potential future research directions in file carving.

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Section: Improvement By Applying Construct Ant Solutionsmentioning

confidence: 99%

“…• Expands the search space for the ants a well as diversifies the solutions searched. -Employed multiple pheromone change and heuristic function methods [11].…”

Section: Table 2 Summary Of Improvement In Aco Optimizationmentioning

confidence: 99%

Recent research in finding the optimal path by ant colony optimization

Sari

Mohamad

2021

Bulletin EEI

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“…This was formulated as a Markov decision process and Deep Q-Network was used as a solution. The scheduling problem was also researched in Yi et al (2020) for tasks in multi-processor distributed systems, but this time authors used an ant colony optimization algorithm to enhance the local search ability and improve the quality of the solution.…”

Section: Use Of Computational Intelligence In Cyber-physical Systemsmentioning

confidence: 99%

Computational Intelligence in the Context of Industry 4.0

Hosŏvský

Piteľ

Trojanová

et al. 2021

Implementing Industry 4.0 in SMEs

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Industry 4.0 is affecting almost every area of the industry, and as a result of its effects, systems, technologies, and the way information is processed are being transformed. Its typical feature is transmission of information in the system environment provided by the Internet of Things. All information should be stored and shared through cloud computing. As a result, access to information should be unrestricted. This chapter is focused on Computational Intelligence (CI) in the context of Industry 4.0. Each subchapter provides fundamentals of some paradigms, followed by the use of CI in the concrete paradigm. The ending part of the chapter is focused on connecting theory and practice in a case study, which lists industrial parts recognition by convolutional neural networks for assisted assembly.

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“…The MMAS model differs from AS; it provides stronger exploitation of the global best solution by setting boundaries for the pheromone intensity, and , and initializing the pheromone trails to . The pheromone value is calculated and adjusted by equation (15), where is calculated according to equation ( 8), with . If is found to surpass , it is set back to , and if it is found to go below , it is reset to , and only one single ant (best ant) is used at the end of each iteration to refresh the pheromone.…”

Section: Max-min Ant System (Mmas)mentioning

confidence: 99%

A Review of Implementing Ant System Algorithms on Scheduling Problems

Kashef

Elshaer

2021

The Egyptian International Journal of Engineering Sciences and

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The ant system (AS) and scheduling problem are well-known concepts in literature. Ant algorithms have been known to be an effective tool for solving combinatorial optimization problems. Elitist AS (EAS), rank-based AS (RAS), ant colony system (ACS), and max-min AS (MMAS) are the variants of the AS algorithm; they are triggered by the different ways of updating the pheromone trail τ, computing the visibility η, and/or other parameters in the basic AS model. The main contribution of this article is twofold. First, the basic AS and its controlled parameters are presented, the key variants of the ant algorithms are explained, and major changes of each variant from the basic model are tracked. Second, sixty papers are collected between 2015 and 2020 based on a search strategy for tracking the implementation of different AS variants in solving scheduling problems. Numerous findings based on a statistical analysis of the collected papers are reported and discussed. This study will allow the researcher to understand the essence of the ant algorithm, recognize the fundamental differences in its five systems, and determine how each of them can be implemented. Tracking a sample of articles that apply an ant algorithm for a specific case study gives researchers new ideas on how to adjust the original model to fit their problem.

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Task optimization and scheduling of distributed cyber–physical system based on improved ant colony algorithm

Cited by 135 publications

References 26 publications

Recent research in finding the optimal path by ant colony optimization

Recent research in finding the optimal path by ant colony optimization

Computational Intelligence in the Context of Industry 4.0

A Review of Implementing Ant System Algorithms on Scheduling Problems

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