Large Scale Optimization based on self-directed Local Search

Faculty Computer Science Year: 2011
Type of Publication: ZU Hosted Pages:
Authors:
Eman samir hasan sayed
Journal: ASOR Bulletin ASOR Bulletin Volume:
Keywords : Large Scale Optimization based , self-directed Local    
Abstract:
In this paper we propose, a Memetic Algorithm (MA) that is based on a self-directed Local Search (MA-sd-LS), for solving large scale problems using a random grouping decomposition technique. To overcome the large dimensionality difficulties
    
     
 
       

Author Related Publications

  • Eman samir hasan sayed, "Using Hybrid Dependency Identification with a Memetic Algorithm for Large Scale Optimization Problems", Springer Berlin Heidelberg, 2012 More
  • Eman samir hasan sayed, "Decision Making Assessment for Site Selection Using the AHP and TOPSIS Methods", Statistical studies institution, Cairo University, Egypt, 2007 More
  • Eman samir hasan sayed, "Dependency Identification technique for large scale optimization problems", IEEE, 2012 More
  • Eman samir hasan sayed, "A Decomposition-based Algorithm for Dynamic Economic Dispatch Problems", IEEE, 2014 More
  • Eman samir hasan sayed, "Decomposition-based evolutionary algorithm for large scale constrained problems", Elsevier Inc, 2014 More

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  • Saber Mohamed, "A Constraint Consensus Memetic Algorithm for Solving Constrained Optimization Problems", Taylor & Francis, 2013 More
  • Saber Mohamed, "Self-Adaptive Differential Evolution Incorporating a Heuristic Mixing of Operators", Springer, 2012 More
  • Asmaa Atef Hassan El Sayed, "Project Scheduling: Survey and Research Potentials", International Journal of Computer Applications Technology and Research Volume 4– Issue 4, 235 - 241, 2015, ISSN:- 2319–8656, 2015 More
  • Saber Mohamed, "Configuring Two-algorithm-based Evolutionary Approach for Solving Dynamic Economic Dispatch Problems", Elsevier, 2016 More
  • Laila Abdel Fattah Shawqi Ibrahim, "An improved nature inspired meta-heuristic algorithm for 1-D bin packing problems", Springer‏, 2018 More
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