Adaptive Learning Rate Elitism Estimation of Distribution Algorithm Combining Chaos Perturbation for Large Scale Optimization

Estimation of distribution algorithm (EDA) is a kind of EAs, which is based on the technique of probabilistic model and sampling. Large scale optimization problems are a challenge for the conventional EAs. This paper presents an adaptive learning rate elitism EDA combining chaos perturbation (ALREEDA) to improve the performance of traditional EDA to solve high dimensional optimization problems. The famous elitism strategy is introduced to maintain a good convergent performance of this algorithm. The learning rate of σ (a parameter of probabilistic model) is adaptive in the optimization to enhance the algorithm’s global and local search ability, and the chaos perturbation strategy is used to improve the algorithm’s local search ability. Some simulation experiments are conducted to verify the performance of ALREEDA by seven benchmarks of CEC’08 large scale optimization with dimensions 100, 500 and 1000. The results of ALREEDA are promising on majority of the testing problems, and it is comparable with other EDAs and some other improved EAs.