New hybrid genetic operators for real coded genetic algorithm to compute optimal control of a class of hybrid systems

Arumugam, M. Senthil; Rao, M. V. C.; Palaniappan, Ramaswamy

doi:10.1016/j.asoc.2004.11.001

Cited by 77 publications

(31 citation statements)

References 6 publications

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“…In previous studies on real-number encoding, Arumugam et al (2005) proposed the arithmetic crossover (AMXO) and average convex crossover methods (ACXO) with dynamic mutation (DM). Note that ACXO is a special case of AMXO.…”

Section: Spanning Tree-based Genetic Algorithmmentioning

confidence: 99%

A possibilistic approach to the modeling and resolution of uncertain closed-loop logistics

Wang

Hsu

2012

Fuzzy Optim Decis Making

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Closed-loop logistics planning is an important tactic for the achievement of sustainable development. However, the correlation among the demand, recovery, and landfilling makes the estimation of their rates uncertain and difficult. Although the fuzzy numbers can present such kinds of overlapping phenomena, the conventional method of defuzzification using level-cut methods could result in the loss of information. To retain complete information, the possibilistic approach is adopted to obtain the possibilistic mean and mean square imprecision index (MSII) of the shortage and surplus for uncertain factors. By applying the possibilistic approach, a multi-objective, closed-loop logistics model considering shortage and surplus is formulated. The two objectives are to reduce both the total cost and the root MSII. Then, a non-dominated solution can be obtained to support decisions with lower perturbation and cost. Also, the information on prediction interval can be obtained from the possibilistic mean and root MSII to support the decisions in the uncertain environment. This problem is non-deterministic polynomial-time hard, so a new algorithm based on the spanning tree-based genetic algorithm has been developed. Numerical experiments have shown that the proposed algorithm can yield comparatively efficient and accurate results.

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Section: Spanning Tree-based Genetic Algorithmmentioning

confidence: 99%

A possibilistic approach to the modeling and resolution of uncertain closed-loop logistics

Wang

Hsu

2012

Fuzzy Optim Decis Making

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“…There is no best heuristic, performance varies according to the constraints of problem. A big number of proposals for hybrid or customized adaptations is found in recent literature (Deep and Thakur, 2007) (Arumugama et al, 2005) (Kumar et al, 2007) (Lee and Geem, 2005) (Mishra et al, 2005).…”

Section: Metaheuristic Algorithms For Solving Optimization Problemsmentioning

confidence: 99%

AUTOMATIC PARAMETERIZATION FOR EXPEDITIOUS MODELLING OF VIRTUAL URBAN ENVIRONMENTS - A New Hybrid Metaheuristic

Cruz

Coelho

Reis

2008

Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics Service

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Abstract:Expeditious modelling of virtual urban environments consists of generating realistic 3d models from limited information. It has several practical applications but typically suffers from a lack of accuracy in the parameter values that feed the modeller. By gathering small amounts of information about certain key urban areas, it becomes possible to feed a system that automatically compares and adjusts the input parameter values of modelling rules to find optimal parameter combinations. These optimum parameterized rules can then be reapplied in the generation of virtual urban models of areas with missing data but similar characteristics to the referenced area. Based on several nature inspired meta heuristic algorithms such as genetic algorithms, simulated annealing and harmony search, this paper presents a new hybrid meta heuristic algorithm capable of optimizing functions with both discrete and continuous parameters and offer competitive results in a highly neglected field of application.

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“…Thus the parents are as defined by equation (4,5). The offspring are arithmetically the following [21] (4)…”

Section: Genetic Algorithm Proceduresmentioning

confidence: 99%

Novel Multi-Gen Multi Parameter Genetic Algorithm Representation for Attributes Selection and Porosity Prediction

Saleh¹,

Tuama²

2016

IJCA

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Many applications require a careful selection of attributes or features from a much larger set of data. This attributes selection problem need to optimized. In order to tackle this problem this paper proposes a binary-real code multi-gen multi-parameter genetic algorithm for attributes selection from large seismic data and prediction of effective porosity. Genetic Algorithm (GA) uses three selection methods for this purpose, mean square error and correlation coefficient are two witness criteria to choose the best subset of attributes that minimize the error and give high prediction of porosity.

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New hybrid genetic operators for real coded genetic algorithm to compute optimal control of a class of hybrid systems

Cited by 77 publications

References 6 publications

A possibilistic approach to the modeling and resolution of uncertain closed-loop logistics

A possibilistic approach to the modeling and resolution of uncertain closed-loop logistics

AUTOMATIC PARAMETERIZATION FOR EXPEDITIOUS MODELLING OF VIRTUAL URBAN ENVIRONMENTS - A New Hybrid Metaheuristic

Novel Multi-Gen Multi Parameter Genetic Algorithm Representation for Attributes Selection and Porosity Prediction

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