Virus-evolutionary genetic algorithm for a self-organizing manufacturing system

Kubota, Naoyuki; Fukuda, Toshio; Shimojima, Koji

doi:10.1016/0360-8352(96)00049-6

Cited by 50 publications

(36 citation statements)

References 4 publications

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“…It was discussed other times like in VEGA (Kubota et al, 1996) and GAVI (Guedes et al, 2005). In both methods is used another population composed by virus, called viral population and infection of chromosomes called transcription.…”

Section: Genetic Algorithm With Viral Infection (Related Work)mentioning

confidence: 99%

“…However, as the virus has no fitness value, because it doesn't have a complete solution to be evaluated, it is used a parameter of infection, called fitvirus (Kubota et al, 1996). This is an indicator of how well the virus has acted.…”

Section: Genetic Algorithm With Viral Infection (Related Work)mentioning

confidence: 99%

“…The main difference between VEGA and GAVI is because GAVI uses viral infection as operator, ignoring the operator of mutation, and VEGA is a complete GA (Guedes et al, 2005;Kubota et al, 1996).…”

Section: Genetic Algorithm With Viral Infection (Related Work)mentioning

confidence: 99%

“…This is an indicator of how well the virus has acted. After selecting the virus that will perform the infection (in a particular chromosome), the transcription is made in this moment, which consists in the modification of infected chromosome by the viral information that contains a section similar to the one represented by the infecting agent Guedes et al (2005); Kubota et al (1996).…”

Section: Genetic Algorithm With Viral Infection (Related Work)mentioning

confidence: 99%

See 3 more Smart Citations

Using Retroviral Iterative Genetic Algorithm to Solve Constraint Global Optimization Problems

Moreira¹,

Teixeira²,

Lobato³

et al. 2012

Genetic Diversity in Microorganisms

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Section: Genetic Algorithm With Viral Infection (Related Work)mentioning

confidence: 99%

Section: Genetic Algorithm With Viral Infection (Related Work)mentioning

confidence: 99%

Section: Genetic Algorithm With Viral Infection (Related Work)mentioning

confidence: 99%

Section: Genetic Algorithm With Viral Infection (Related Work)mentioning

confidence: 99%

See 2 more Smart Citations

Using Retroviral Iterative Genetic Algorithm to Solve Constraint Global Optimization Problems

Moreira¹,

Teixeira²,

Lobato³

et al. 2012

Genetic Diversity in Microorganisms

View full text Add to dashboard Cite

“…It is interesting to note that an application of the biological virus concept was developed by Kubota et al (1996) as a genetic operator of a genetic algorithm applied to a self-organizing manufacturing system.…”

Section: Introductionmentioning

confidence: 99%

A viral system algorithm to optimize the car dispatching in elevator group control systems of tall buildings

Cortés

Onieva

Muñuzuri

et al. 2013

Computers & Industrial Engineering

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Abstract.-Nowadays is very common the presence of tall buildings in the business centres of the main cities of the world. Such buildings require the installation of numerous lifts that are coordinated and managed under a unique control system. Population working in the buildings follows a similar traffic pattern generating situations of traffic congestion. The problem arises when a passenger makes a hall call wishing to travel to another floor of the building. The dispatching of the most suitable car is the optimization problem we are tackling in this paper. We develop a viral system algorithm which is based on a bio-inspired virus infection analogy to deal with it. The viral system algorithm is compared to genetic algorithms, and tabu search approaches that have proven efficiency in the vertical transportation literature. The experiments undertaken in tall buildings from 10 to 24 floors, and several car configurations from 2 to 6 cars, provide valuable results and show how viral system outperforms such soft computing algorithms.Keywords: Elevator; lift; viral system; bio-inspired algorithms; elevator group control system; vertical transportation AcknowledgmentsThe authors acknowledge the financial support provided by the Spanish Plan for Research and Development Technology and Innovation Plan through its Programme Industrial Production and Design, project Ref. DPI2010-15352. A viral system algorithm to optimize the car dispatching in elevator group control systems of tall buildings Abstract.-Nowadays is very common the presence of tall buildings in the business centres of the main cities of the world. Such buildings require the installation of numerous lifts that are coordinated and managed under a unique control system. Population working in the buildings follows a similar traffic pattern generating situations of traffic congestion. The problem arises when a passenger makes a hall call wishing to travel to another floor of the building. The dispatching of the most suitable car is the optimization problem we are tackling in this paper. We develop a viral system algorithm which is based on a bio-inspired virus infection analogy to deal with it. The viral system algorithm is compared to genetic algorithms, and tabu search approaches that have proven efficiency in the vertical transportation literature. The experiments undertaken in tall buildings from 10 to 24 floors, and several car configurations from 2 to 6 cars, provide valuable results and show how viral system outperforms such soft computing algorithms.

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Health monitoring sensor placement optimization for Canton Tower using immune monkey algorithm

Zhang

2014

Struct. Control Health Monit.

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SUMMARYSensor placement is an integral component in the design of an effective and reliable structural health monitoring system for large-scale civil engineering structures. In this paper, a novel hybrid optimization algorithm, that is, immune monkey algorithm (IMA), by combining the monkey algorithm with the immune algorithm, is proposed to solve the optimal sensor placement problem. In the proposed algorithm, the dual-structure coding method is designed and adopted for the representation of design variables and a chaos-based approach is presented to initialize the monkey population. In addition, some improved strategies such as the advanced climb process, immune selection, clonal proliferation, and immune renewal are defined to maintain population diversity and inhibit degeneracy during evolution. The effectiveness and reliability of the proposed IMA is demonstrated by designing the sensor arrays of the Canton Tower, the second highest TV tower in the world. Numerical investigations presented clearly suggest that the proposed hybrid IMA outperforms the original monkey algorithm implementations both in terms of generating optimal solutions as well as faster convergence. The proposed algorithm can easily be extended to other multiple global optimization problems.

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Virus-evolutionary genetic algorithm for a self-organizing manufacturing system

Cited by 50 publications

References 4 publications

Using Retroviral Iterative Genetic Algorithm to Solve Constraint Global Optimization Problems

Using Retroviral Iterative Genetic Algorithm to Solve Constraint Global Optimization Problems

A viral system algorithm to optimize the car dispatching in elevator group control systems of tall buildings

Health monitoring sensor placement optimization for Canton Tower using immune monkey algorithm

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