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Novel Metaheuristic Approach: Integration of Variables Method (IVM) and Human-Machine Interaction for Subjective Evaluation

  • Umer AsgherEmail author
  • Rolando Simeón
  • Riaz Ahmad
  • José Arzola-Ruiz
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 953)

Abstract

Metaheuristics is recognized as the most practical approach in simulation based global optimization and during investigation of the state-of-the-art optimization approaches from local searches over evolutionary computation methods to estimation of distribution algorithms. In this study a new evolutionary metaheuristic approach “The Integration of Variables Method (IVM)” is devised for global optimization, that methodology is characterized by making vide solutions codes in an objective population aided by any composition of operators. The Genetic Algorithms (GA) are incorporated in this methodology; where genetic operators are used to evolve populations classifiers in creation of new concrete heuristics solutions for decision making tasks. In this work, the main consideration is devoted to the exploration of Extremes value distribution of Functions with multi Variables Codes in brain decision making process and Brain Computer Interface (BCI) tasks. The properties of Extremes value distribution algorithm are characterized by the diversity of measurements in populations of available solutions. Additionally, while generating adequate solutions of the basic objectives, subjective indicators of human-machine interaction are used to differentiate the characteristics of the different solutions in a population. The results obtained from this metaheuristic approach are compared with results of Genetic Algorithms (GA) in case study related with Optimal Multiple Objective Design and Progressive Cutting Dies implemented in a CAD system.

Keywords

Decision making Human–machine interaction Genetic Algorithms (GA) Product design Metaheuristics Brain computer interface (BCI) CAD systems 

Notes

Acknowledgments

The authors would like to acknowledge School of Mechanical & Manufacturing Engineering (SMME)- National University of Sciences and Technology (NUST), Islamabad - Pakistan, CAD/CAM Study Center, Holguin University, Holguín - Cuba and the Technological University of Habana, José Antonio Echeverría, Cujae-CEMAT, Habana - Cuba for providing collaborative research support and opportunity to the authors in data collection, modelling and analysis of the Proposed Algorithms.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Umer Asgher
    • 1
    Email author
  • Rolando Simeón
    • 2
  • Riaz Ahmad
    • 1
    • 3
  • José Arzola-Ruiz
    • 4
  1. 1.School of Mechanical and Manufacturing Engineering (SMME)National University of Sciences and Technology (NUST)IslamabadPakistan
  2. 2.CAD/CAM Study CenterHolguin UniversityHolguínCuba
  3. 3.Quality Assurance DirectorateNational University of Sciences and Technology (NUST)IslamabadPakistan
  4. 4.Technological University of Havana, José Antonio Echeverría, Cujae-CEMATHabanaCuba

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