A genetic algorithm with switch-device encoding for optimal partition of switched industrial Ethernet networks

Carro-Calvo, L.; Salcedo‐Sanz, Sancho; Portilla-Figueras, Antonio; Ortiz-García, Emilio G.

doi:10.1016/j.jnca.2010.03.003

Cited by 15 publications

(11 citation statements)

References 19 publications

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“…The Pareto frontier solutions got by the proposed algorithm in Fig. 5 is as follows in Table I. Obviously the solutions obtained by the proposed algorithm can dominate the results got by Zhang and Zhang [16] and Carro-Calvo et al [17] weakly. Furthermore from Fig.…”

Section: A Benchmark Testmentioning

confidence: 93%

“…Carro-Calvo et al [17] presented a novel GA and switch-device encoding method to solve the industrial Ethernet network partition problem. The good performance of the approach was proved by the comparison with many genetic operators.…”

Section: Related Workmentioning

confidence: 99%

“…Carro-Calvo et al [17] updated the solutions got by Zhang and Zhang, and according to the Pareto non-dominated solution definition the final solution sets are as follows: S1: 1 = 1022, 2 = 21; S2: 1 = 1046, 2 = 7. The Pareto frontier solutions got by the proposed algorithm in Fig.…”

Section: A Benchmark Testmentioning

confidence: 99%

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Multiobjective Topology Optimization Based on Mapping Matrix and NSGA-II for Switched Industrial Internet of Things

Chen

2016

IEEE Internet Things J.

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With the deep integration of Information and Communication Technology (ICT), the network performance of existing switched Ethernet cannot meet the growing control and management demands of the industrial production. To solve the time-delay and sub-network transmission balance problem, a multi-objective Non-dominated Sorting Genetic Algorithm (NSGA-Ⅱ) based on mapping matrix is proposed, in which a new topology model is established to reflect the flexible Ethernet topology format. Meanwhile an adjacency mapping matrix is introduced as genetic code to embody the actual topology structure and several special properties of the mapping matrix caused by the industrial topology characteristics are proved. On the basis of these properties, an asexual crossover and double swap mutation operation is proposed to achieve the population evolution. Besides, in the genetic procedure of the proposed algorithm a self-adaptive measure and an elite reservation strategy are applied to accelerate the convergence speed. The validity of proposed algorithm is demonstrated by benchmark test and the case verification shows that the comprehensive performance of the switched Ethernet is improved. Index Terms-Internet of Things (IoT);

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Section: A Benchmark Testmentioning

confidence: 93%

Section: Related Workmentioning

confidence: 99%

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Multiobjective Topology Optimization Based on Mapping Matrix and NSGA-II for Switched Industrial Internet of Things

Chen

2016

IEEE Internet Things J.

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“…Example of scenarios corresponding to the same E. investigations for optimization problems resolution in almost all domains. With regard to fieldbus systems, GAs were used in Georges et al (2006), Zhang and Zhang (2007) and Carro-Calvo et al (2010) to optimize the partition of switched Ethernet networks into sub-networks where the intra sub-network communication traffic is maximized whereas the inter sub-networks communication is minimized. They were used in Lee et al (2004) to help the designer to select the optimal timers in a Profibus Token Passing Protocol fieldbus so as to satisfy the maximum allowable delays of the real time data on one hand and maximize the non real time data transfer on the other hand.…”

Section: Genetic Algorithmsmentioning

confidence: 99%

Genetic algorithms for delays evaluation in networked automation systems

Addad

Amari

Lesage

2011

Engineering Applications of Artificial Intelligence

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a b s t r a c tIn this paper, we present an approach to evaluate end-to-end delays in packets switching networked automation systems. Since Client-Server paradigm is considered for communication between the field devices, the existing methods of network delays evaluation are hardly applicable to assess realistic upper bounds of these delays. In an effort to enhance these delays evaluation, we propose an alternative method. Two algorithms, usually used for optimization problems, exhaustive and genetic algorithms, are then developed to achieve this purpose. While a formal proof about the capacity of the former one to ensure the worst delay overestimation is given, the latter proves to provide faster and more accurate results at the same time. This is shown on a practical case study while comparing the results of the two methods.

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“…A vast majority of well-known optimization problems like(Arora et al, 2012;El Dein, 2014;Selvaraj and Anand, 2012;Carro-Calvo et al, 2010;Lim et al, 2012;Gonz alez-Torres et al, 2013;Qin et al, 2013; …”

mentioning

confidence: 99%

Job scheduling in the Expert Cloud based on genetic algorithms

et al. 2014

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Purpose -Expert Cloud as a new class of Cloud computing systems enables its users to request the skill, knowledge and expertise of people by employing internet infrastructures and Cloud computing concepts without any information of their location. Job scheduling is one of the most important issue in Expert Cloud and impacts on its efficiency and customer satisfaction. The purpose of this paper is to propose an applicable method based on genetic algorithm for job scheduling in Expert Cloud. Design/methodology/approach -Because of the nature of the scheduling issue as a NP-Hard problem and the success of genetic algorithm in optimization and NP-Hard problems, the authors used a genetic algorithm to schedule the jobs on human resources in Expert Cloud. In this method, chromosome or candidate solutions are represented by a vector; fitness function is calculated based on response time; one point crossover and swap mutation are also used. Findings -The results indicate that the proposed method can schedule the received jobs in appropriate time with high accuracy in comparison to common methods (First Come First Served, Shortest Process Next and Highest Response Ratio Next). Also the proposed method has better performance in term of total execution time, service þ wait time, failure rate and Human Resource utilization rate in comparison to common methods. Originality/value -In this paper the job scheduling issue in Expert Cloud is pointed out and the approach to resolve the problem is applied into a practical example.

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A genetic algorithm with switch-device encoding for optimal partition of switched industrial Ethernet networks

Cited by 15 publications

References 19 publications

Multiobjective Topology Optimization Based on Mapping Matrix and NSGA-II for Switched Industrial Internet of Things

Multiobjective Topology Optimization Based on Mapping Matrix and NSGA-II for Switched Industrial Internet of Things

Genetic algorithms for delays evaluation in networked automation systems

Job scheduling in the Expert Cloud based on genetic algorithms

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