Journal of Global Optimization
Adaptive particle swarm optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Self-adaptive differential evolution based on PSO learning strategy
Proceedings of the 12th annual conference on Genetic and evolutionary computation
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
Enhance differential evolution with random walk
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
| Hi-index | 0.00 |
As the performance of differential evolution (DE) is significantly affected by its mutation schemes and parameter settings when solving different problems, this paper proposes a simple yet efficient co-evolutionary DE (CEDE) to enhance the algorithm performance. The CEDE algorithm uses multiple populations to optimize the problem cooperatively, with each population using different operators and/or different parameters. Moreover, as different populations may show different performance on the same problem, we further design an efficient adaptive migration strategy (AMS) to dynamically control the population size of different populations. The CEDE algorithm is tested and compared on four benchmark functions. Experimental results demonstrate the good performance of CEDE when compared with conventional DEs using different operators and/or parameters.