An Evolutionary Hybrid Model linking Particle Swarm Optimization and Simulated Annealing Algorithms for Complex Optimization Problems

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    This paper describes an evolutionary hybrid model linking two algorithms: Particle Swarm Optimization (PSO) and Simulated Annealing (SA). The basic idea behind using a hybrid model is improving the reliability of the obtained results from our first model, namely MPSO (Modified PSO) based on PSO algorithm, by adding SA algorithm which is quite popular for its powerful feature of effective escaping from the trap of local minima. MPSO model uses the concept of evolutionary neighborhoods associated to parallel computation, to overcome to the two essential disadvantages of PSO: high running time and premature convergence.

    The presented algorithm has two essential operations: first running PSO algorithm in parallel using the new concept of evolutionary neighborhood to obtain a global best solution, then improving the results with SA algorithm to get the global optimal solution.

    By testing this hybrid algorithm (H-MPSO-SA) on a set of standard benchmark functions and according to the obtained results, the program have given satisfactory results of the hybrid model compared to the basic PSO and MPSO algorithms. 

     


  • Keywords


    Hybrid algorithm, Optimization, Metaheuristic, PSO, SA.

  • References


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Article ID: 25654
 
DOI: 10.14419/ijet.v8i1.6.25654




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