Improvement of evolution process of dandelion algorithm with extreme learning machine for global optimization problems

Dandelion Algorithm (DA) is a novel swarm intelligent optimization algorithm. In evolutionary process of DA, the quality of the seeds generated by dandelions is uneven, and the excellent seeds are expected to be retained and evaluated, while the poor seeds should be discarded without evaluation. In order to determine whether a seed is excellent or not, an improvement of evolution process of dandelion algorithm with extreme learning machine (ELMDA) is proposed in this paper. In ELMDA, firstly, the dandelion population can be partitioned into excellent dandelions and poor dandelions based on fitness values. Then, the excellent dandelions and poor dandelions are assigned corresponding labels (i.e. +1 if excellent or -1 if poor), which can be regarded as a training set, and the training model is built based on ELM. Finally, the model is applied to classify the seeds as excellent or poor, and the excellent seeds are chosen to participate in evolution process. Meanwhile, the robustness of the proposed algorithm is analyzed in this paper. Experimental results performed on test functions show that the proposed algorithm is competitive to its peers. Moreover, the proposed algorithm is demonstrated on three engineering designed problems, and the results indicate that the proposed algorithm has better performance in solving them.