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## 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
• 4.6.1 Slow exploitation of GSA
• 4.6.2 Improving the exploitation of 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

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