Self-adaptive Mix of Particle Swarm Methodologies for Constrained Optimization

Faculty Computer Science Year: 2014
Type of Publication: ZU Hosted Pages: 216-233
Authors:
Journal: Information Sciences ELSEVIER Volume:
Keywords : Self-adaptive , , Particle Swarm Methodologies , Constrained Optimization    
Abstract:
In recent years, many different variants of the particle swarm optimizer (PSO) for solving optimization problems have been proposed. However, PSO has an inherent drawback in handling constrained problems, mainly because of its complexity an
   
     
 
       

Author Related Publications

  • Saber Mohamed, "Training and Testing a Self-Adaptive Multi-Operator Evolutionary Algorithm for Constrained Optimization", ELSEVEIR, 2015 More
  • Saber Mohamed, "An Improved Self-Adaptive Differential Evolution Algorithm for Optimization Problems", IEEE, 2013 More
  • Saber Mohamed, "Differential Evolution with Dynamic Parameters Selection for Optimization Problems", IEEE, 2014 More
  • Saber Mohamed, "A Self-Adaptive Combined Strategies Algorithm for Constrained Optimization using Differential Evolution", ELSEVIER, 2014 More
  • Saber Mohamed, "Adaptive Configuration of Evolutionary Algorithms for Constrained Optimization", ELSEVIER, 2013 More

Department Related Publications

  • Mohammed Abdel Basset Metwally Attia, "The role of single valued neutrosophic sets and rough sets in smart city: Imperfect and incomplete information systems", Elsevier‏, 2018 More
  • Mai Mohammed Abdul Sattar Jaafar, "The role of single valued neutrosophic sets and rough sets in smart city: Imperfect and incomplete information systems", Elsevier‏, 2018 More
  • 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
  • Saber Mohamed, "Configuring Two-algorithm-based Evolutionary Approach for Solving Dynamic Economic Dispatch Problems", Elsevier, 2016 More
Tweet