Multiple Differential Evolution Research Articles

An adaptive clonal selection algorithm with multiple differential evolution strategies

Clonal selection algorithms have provided significant insights into numerical optimization problems. However, most mutation operators in conventional clonal selection algorithms have semi-blindness and lack an effective guidance mechanism, which has thus become one of the important factors restricting the performance of algorithms. To address these problems, this study develops an improved clonal selection algorithm called an adaptive clonal selection algorithm with multiple differential evolution strategies (ADECSA) with three features: (1) an adaptive mutation strategy pool based on its historical records of success is introduced to guide the immune response process effectively; (2) an adaptive population resizing method is adopted to speed up convergence; and (3) a premature convergence detection method and a stagnation detection method are proposed to alleviate premature convergence and stagnation problems in the evolution by enhancing the diversity of the population. Experimental results on a wide variety of benchmark functions demonstrate that our proposed method achieves better performance than both state-of-the-art clonal selection algorithms and differential evolution algorithms. Especially in the comparisons with other clonal selection algorithms, our proposed method outperforms at least 23 out of 30 benchmark functions from the CEC2014 test suite.

A distributed surrogate system assisted differential evolutionary algorithm for computationally expensive history matching problems

Recently, surrogate assisted evolutionary algorithms (SAEAs) are widely studied and applied for history matching problems due to surrogate models can accelerate convergence. However, most of the SAEAs lose the ability of parallel sampling due to the introduction of surrogate models, in which, a small number of potential solutions are selected for evaluation in each iteration. Generally, history matching involves a large number of numerical simulations, and the role of parallel computing cannot be ignored. To address this issue, this paper proposes a distributed surrogate system assisted differential evolution algorithm, termed DSS-DE. A distributed surrogate system (DSS) based on ensemble learning techniques is first developed, which builds a large number of basic learners before optimization, to effectively approximate different regions in the search space. Following that, performing multiple differential evolution (DE) optimizers with different mutation operators concurrently to sample a set of solutions to find as many as possible local or global optima of the data mismatch objective function. Moreover, based on the DSS prediction, a parallel infill strategy is designed to screen the potential promising solutions. Combined with the convolutional variational autoencoder (CVAE) based parameterization technique, a history matching workflow is developed. Empirical studies on two multimodal benchmark functions demonstrate that the proposed algorithm can obtain high-quality solutions on a limited computational budget. Furthermore, the proposed history matching workflow is validated on three synthetic waterflooding reservoir case studies with different geological characteristics. The test results show that the effectiveness of the proposed algorithm for history matching problems.

An adaptive immune-inspired multi-objective algorithm with multiple differential evolution strategies

Most multi-objective immune algorithms (MOIAs) adopt clonal selection to speed up convergence, as this operator only clones the best individuals during the search process. However, this approach somehow deteriorates the population diversity, which may cause a MOIA to be trapped in a local optimum and could also lead to premature convergence when tackling some complicated multi-objective optimization problems (MOPs). In order to overcome this problem, an adaptive immune-inspired multi-objective algorithm (AIMA) is presented in this paper, in which multiple differential evolution (DE) strategies having distinct advantages are embedded into a conventional MOIA. Our proposed approach strengthens the exploration capabilities of a MOIA while also improving its population diversity. At each generation, based on the current search stage, an adaptive selection method is designed to choose an appropriate DE strategy for evolution. The core idea is to effectively combine the advantages of three DE strategies when solving different MOPs. A number of comparative experiments are conducted on the well-known and frequently-used WFG and DTLZ test problems. Our experimental results validate the superiority of our proposed AIMA, as it performs better than some state-of-the-art multi-objective optimization algorithms and some state-of-the-art MOIAs.