Overview of gravitational search algorithm

Overview of gravitational search algorithm

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Gravitational search algorithm (GSA) belongs to the nature-inspired metaheuristic optimization methods. A metaheuristic optimization method consists of a generalized set of rules that can be applied to solve a variety of optimization problems. Many metaheuristic optimization methods have been developed on the model of some well-known processes in nature. For example, well-known genetic algorithm is based on mimicking of the process of evolution in biology; simulated annealing emulates the physical process of annealing, etc.

Chapter Contents:

  • 4.1 Introduction
  • 4.2 Description of original GSA
  • 4.2.1 Parameters of GSA
  • 4.2.2 General remarks about GSA
  • 4.2.3 MATLAB® code of GSA
  • 4.2.4 Example usage of GSA
  • 4.3 Binary gravitational search algorithm
  • 4.4 Modified GSA
  • 4.5 Opposition-based GSA
  • 4.5.1 Current optimum opposition-based GSA
  • 4.6 Adaptive gbest-guided GSA
  • 4.6.1 Slow exploitation of GSA
  • 4.6.2 Improving the exploitation of GSA
  • 4.7 Self-adaptive GSA
  • 4.8 Nondominated sorting GSA
  • 4.8.1 Updating the external archive
  • 4.8.2 Updating the list of moving agents
  • 4.8.3 Updating the mass of moving agents
  • 4.8.4 Updating the acceleration of agents
  • 4.8.5 The use of mutation operator
  • 4.8.6 Update and mutate the position of agents
  • 4.9 Clustered-gravitational search algorithm
  • 4.10 Hybrid PSO and GSA algorithm
  • 4.11 Applications of GSA to power system problems—literature overview
  • 4.11.1 Optimal power flow
  • 4.11.2 Optimal reactive power dispatch
  • 4.11.3 Economic dispatch using GSA
  • 4.11.4 Optimal power flow in distribution networks
  • 4.11.5 Optimal DG placement and sizing in distribution networks
  • 4.11.6 Optimal energy and operation management of microgrids
  • 4.11.7 Optimal coordination of overcurrent relays
  • 4.12 Conclusion
  • References

Inspec keywords: search problems

Other keywords: GSA; nature-inspired metaheuristic optimization methods; genetic algorithm; optimization problems; gravitational search algorithm; simulated annealing

Subjects: Optimisation; Optimisation techniques; Optimisation techniques; Combinatorial mathematics; Combinatorial mathematics; Combinatorial mathematics

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