An Adaptive LS-SVM Based Differential Evolution Algorithm

Differential Evolution (DE) is featured by its simple parameter control; genetic operation and fine robustness. However, DE yet still has difficulty with complex functions in continuous space due to its searching blindness and inefficiency from time to time. An adaptive DE algorithm based on LS-SVM (Least Square Support Vector Machine) is proposed in this paper. The key genetic operators such as differential mutation and crossover are modified; Adaptive population evolution guiding strategy based on LS-SVM n-best training set approximation and optimization is designed; With applying condition analyzed, the procedure and complexity of the LS-SVM based evolution guiding strategy is summarized. The comparative results of the proposed DE with traditional one based on various standard test functions effectively demonstrate the high accuracy and efficiency of the proposed approach for continuous multi-modal optimization.