Journal of Global Optimization
A Note on the Extended Rosenbrock Function
Evolutionary Computation
Differential evolution algorithm with strategy adaptation for global numerical optimization
IEEE Transactions on Evolutionary Computation
Differential evolution using a neighborhood-based mutation operator
IEEE Transactions on Evolutionary Computation
JADE: adaptive differential evolution with optional external archive
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Enhanced Differential Evolution With Adaptive Strategies for Numerical Optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
International Journal of Bio-Inspired Computation
Diversity enhanced particle swarm optimization with neighborhood search
Information Sciences: an International Journal
An improved particle swarm optimisation for solving generalised travelling salesman problem
International Journal of Computing Science and Mathematics
Particle swarm optimisation: time for uniformisation
International Journal of Computing Science and Mathematics
| Hi-index | 0.00 |
Gaussian bare-bones differential evolution GBDE is a new DE algorithm which employs Gaussian random sampling to generate mutant vectors. Though this method can maintain population diversity and enhance the global search ability, it may result in slow convergence rate. In this paper, we present an improved GBDE IGBDE algorithm by using neighbourhood mutation to accelerate the evolution. Moreover, a modified parameter control method is utilised to adjust the crossover rate CR. To verify the performance of our approach, 13 well-known benchmark functions are tested in the experiments. Simulation results show that IGBDE outperforms the original GBDE in terms of solution accuracy and convergence speed.