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Application of swarm intelligence algorithms to multi-objective distributed local area network topology design problem

Application of swarm intelligence algorithms to multi-objective distributed local area network topology design problem

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Artificial bee colony (ABC) optimization, ant colony optimization (ACO), and particle swarm optimization (PSO) are well-known swarm intelligence algorithms. They have been widely used for solving many real-life optimization problems in various domains. This chapter presents how these algorithms can be used in optimizing the distributed local area network topology design. The problem has been modelled as a constrained multi-objective optimization problem using goal programming. In addition to adapting the three algorithms for the problem, a hybrid ABC algorithm has also been proposed. Performance of the algorithms has been evaluated through a simulation study, and the results indicate that the hybrid ABC algorithm outperforms ACO, PSO and ABC algorithms.

Chapter Contents:

  • Abstract
  • 25.1 Introduction
  • 25.2 Multi-objective optimization
  • 25.3 Approaches for handling multiple objectives
  • 25.4 Brief overview of ant colony optimization, particle swarm optimization, and artificial bee colony algorithms
  • 25.4.1 Ant colony optimization
  • 25.4.2 Particle swarm optimization
  • 25.4.3 Artificial bee colony
  • 25.5 Distributed local area network topology design problem
  • 25.5.1 Design objectives
  • 25.5.1.1 Network reliability
  • 25.5.1.2 Network availability
  • 25.5.1.3 Average link utilization
  • 25.5.1.4 Monetary cost
  • 25.5.1.5 Average network delay
  • 25.5.2 Constraints
  • 25.6 Goal programming approach for the DLAN topology design problem
  • 25.6.1 Defining the goals
  • 25.6.2 Calculation of membership functions
  • 25.6.3 Calculation of deviational variables
  • 25.6.4 Formulation of the fitness function
  • 25.7 Swarm intelligence algorithms for DLAN topology design problem
  • 25.7.1 Solution structure
  • 25.7.2 Goal programming based ant colony optimization algorithm
  • 25.7.2.1 Initialization
  • 25.7.2.2 Ants activity
  • 25.7.3 Goal programming based particle swarm optimization algorithm
  • 25.7.3.1 Initialization
  • 25.7.3.2 Particle activity
  • 25.7.4 Goal programming based artificial bee colony algorithm
  • 25.7.4.1 Initialization
  • 25.7.4.2 Parameter setting
  • 25.7.4.3 Employed bees phase
  • 25.7.4.4 Waggle dance
  • 25.7.4.5 Onlooker bees phase
  • 25.7.4.6 Scout bees phase
  • 25.7.5 Evolutionary artificial bee colony optimization
  • 25.8 Results and discussion
  • 25.9 Concluding remarks
  • Acknowledgement
  • References

Inspec keywords: telecommunication network topology; ant colony optimisation; particle swarm optimisation; local area networks; artificial bee colony algorithm

Other keywords: hybrid ABC algorithm; swarm intelligence algorithms; ant colony optimization; PSO; multiobjective distributed local area network topology design problem; artificial bee colony optimization; ABC algorithm; real-life optimization problems; goal programming; particle swarm optimization; multiobjective optimization problem; ACO algorithm

Subjects: Computer communications; Local area networks; Optimisation techniques; Optimisation techniques; Communication network design, planning and routing

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