Guohua Wu

TL;DR: A multi-population based approach is proposed to realize the adapted ensemble of multiple strategies of differential evolution, thereby resulting in a new DE variant named multi- Population ensemble DE (MPEDE) which simultaneously consists of three mutation strategies.

TL;DR: The success of EDEV reveals that, through an appropriate ensemble framework, different DE variants of different merits can support one another to cooperatively solve optimization problems.

TL;DR: A set of 57 real-world Constrained Optimization Problems are described and presented as a benchmark suite to validate the COPs and reveal that the selected problems are indeed challenging to these algorithms, which have been shown to solve many synthetic benchmark problems easily.

TL;DR: A survey on the use of ensemble strategies in POAs is provided and an overview of similar methods in the literature such as hyper-heuristics, island models, adaptive operator selection, etc. are provided and compare them with the ensemble Strategies in the context of POAs.

TL;DR: In this article, an application of an advanced adaptive differential evolution algorithm on the problem of PV module parameter estimation using minimum available information from the manufacturer datasheet by implementing single-diode and double diode models Linear population size reduction technique of success history based Adaptive differential evolution (L-SHADE) algorithm is implemented to minimize the error of currentvoltage relationships at the above-mentioned three important points defining the I-V characteristic.