A Decomposition-Based Multiobjective Clonal Selection Algorithm for Hyperspectral Image Feature Selection

Feature selection is an effective way to handle the strong correlation of hyperspectral image data by screening the significant features and is generally accepted to be a multiobjective optimization problem. Nevertheless, due to the randomness of the strategies and the ambiguity of the optimization directions, the existing multiobjective evolutionary optimization-based feature selection methods can suffer from inefficient search and loss of search space with promising solutions when faced with the high-dimensional and multipeak search space. The multiobjective evolutionary algorithm based on decomposition (MOEA/D) employs a decomposition framework to provide exact guidance for the optimization directions. Unfortunately, random operators are still used, leading to inadequate local optimization. Thus, evolutionary strategies with search preference, such as clonal selection, may be necessary for local search. In this article, a novel decomposition-based multiobjective clonal selection algorithm for feature selection (MOCSA/D_FS) is proposed to obtain a feature subset with a superior classification performance. In MOCSA/D_FS, the information entropy and the ratio of the relative scatter value and mutual information are utilized as two objective functions to evaluate the information amount and redundancy. A series of subproblems are then obtained by decomposing the multiobjective problem through weight vectors with an
$l_{2}$
-norm constraint used to balance the search space. Subsequently, a clonal selection method with search space preference performs a detailed local search on each subproblem, which can fully exploit the potential optimal space. The effectiveness and generalizability of the proposed method were confirmed by experiments on four hyperspectral remote sensing image datasets.