Cooperative coevolutionary differential evolution with linkage measurement minimization for large-scale optimization problems in noisy environments

Cooperative coevolutionary differential evolution with linkage measurement minimization for large-scale optimization problems in noisy environments

Blog Article

Abstract Many optimization problems suffer from noise, and the noise combined with the large-scale attributes makes the problem complexity explode.Cooperative coevolution (CC) based on divide and conquer decomposes the problems and solves the sub-problems alternately, which is a popular framework for solving large-scale optimization problems (LSOPs).Many studies show that the CC framework is sensitive to decomposition, and the high-accuracy decomposition methods such as differential grouping (DG), DG2, and recursive DG (RDG) are extremely sensitive to sampling accuracy, which will fail to detect the interactions E Vitamins in noisy environments.

Therefore, solving LSOPs in noisy environments based on the CC framework faces unprecedented challenges.In this paper, we propose a novel decomposition method named linkage measurement minimization (LMM).We regard the decomposition problem as a combinatorial optimization problem and design the linkage measurement function (LMF) based on Linkage Identification by non-linearity check for real-coded GA (LINC-R).

A detailed theoretical analysis explains why our proposal can determine the interactions in noisy environments.In the optimization, Collections we introduce an advanced optimizer named modified differential evolution with distance-based selection (MDE-DS), and the various mutation strategy and distance-based selection endow MDE-DS with strong anti-noise ability.Numerical experiments show that our proposal is competitive with the state-of-the-art decomposition methods in noisy environments, and the introduction of MDE-DS can accelerate the optimization in noisy environments significantly.