Ten lectures on wavelets
GSA: A Gravitational Search Algorithm
Information Sciences: an International Journal
Particle swarm optimization with crazy particles for nonconvex economic dispatch
Applied Soft Computing
A new differential evolution with wavelet theory based mutation operation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Evolutionary programming techniques for economic load dispatch
IEEE Transactions on Evolutionary Computation
Hybrid Particle Swarm Optimization With Wavelet Mutation and Its Industrial Applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Bio-inspired methods for fast and robust arrangement of thermoelectric modulus
International Journal of Bio-Inspired Computation
Constrained optimisation and robust function optimisation with EIWO
International Journal of Bio-Inspired Computation
A new design method using opposition-based BAT algorithm for IIR system identification problem
International Journal of Bio-Inspired Computation
International Journal of Bio-Inspired Computation
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Gravitational search algorithm (GSA) is one of the new optimisation algorithms based on the law of gravity and mass interactions. In this algorithm, the searcher agents are a collection of masses, and their interactions are based on the Newtonian laws of gravity and motion. In this article, a novel GSA with wavelet mutation (WM) (GSAWM) is proposed. It utilises the wavelet theory to enhance the GSA in exploring the solution space more effectively for a better solution. This algorithm is utilised for the optimal solutions of different economic load dispatch (ELD) problems of power systems. The obtained results are compared with those of the other state-of-the-art heuristic optimisation techniques published in the literature. Both the near-optimality of the solution and the convergence speed of the algorithm are promising.