Lawrence Berkeley National Laboratory

In this paper, we propose a new adaptive unified differential evolution algorithmfor single-objective global optimization. Instead of the multiple mutation strate-gies proposed in conventional differential evolution algorithms, this algorithmemploys a single equation unifying multiple strategies into one expression. It hasthe virtue of mathematical simplicity and also provides users the flexibility forbroader exploration of the space of mutation operators. By making all controlparameters in the proposed algorithm self-adaptively evolve during the processof optimization, it frees the application users from the burden of choosing appro-priate control parameters and also improves the performance of the algorithm.In numerical tests using thirteen basic unimodal and multimodal functions, theproposed adaptive unified algorithm shows promising performance in compari-son to several conventional differential evolution algorithms.