GSA: A Gravitational Search Algorithm
Information Sciences: an International Journal
Comparison among five evolutionary-based optimization algorithms
Advanced Engineering Informatics
BGSA: binary gravitational search algorithm
Natural Computing: an international journal
AEE'10 Proceedings of the 9th WSEAS international conference on Applications of electrical engineering
Quantum-inspired evolutionary algorithm for a class of combinatorial optimization
IEEE Transactions on Evolutionary Computation
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This paper presents a combinational quantum-inspired binary gravitational search algorithm (QBGSA) for solving the optimal power quality monitor (PQM) placement problem in power systems for voltage sag assessment. In this algorithm, the standard binary gravitational search algorithm is modified by applying the concept and principles of quantum behaviour as to improve the search capability with faster convergence rate. The optimization considers multi objective functions and handles observability constraints determined by the concept of the topological monitor reach area. The overall objective function consists of three functions which are based on the number of required PQM, monitor overlapping index and sag severity index. The proposed QBGSA is applied on the radial 69- bus distribution system and compared with the conventional binary gravitational search algorithm and binary particle swarm optimization and quantum-inspired binary particle swarm optimization techniques.