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

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 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, 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.

Discarding–Recovering and Co-Evolution Mechanisms Based Evolutionary Algorithm for Hyperspectral Feature Selection

“…Due to its robustness, this mechanism has received extensive attention and has been widely used in various fields, including natural language processing and image retrieval [21,22]. By combining EA, the co-evolution mechanism improves the search efficiency of the EA in feature selection to some extent [23][24][25]. It divides the original feature set into many subsets; subpopulations are formed based on the agents generated by these subsets, then this mechanism enhances the diversity by information interacting between the agents in different subpopulations.…”