Objective Reduction in Many-Objective Optimization with Social Spider Algorithm for Cloud Detection in Satellite Images

Abstract An important difficulty with multi-objective algorithms to analyze many-objective optimization problems (MaOPs) is the visualization of large dimensional Pareto front. This article has alleviated this issue by utilizing objective reduction approach in order to remove non-conflicting objectives from original objective set. The present work proposed formulation of objective reduction technique with multi-objective social spider optimization (MOSSO) algorithm to provide decision regarding conflict objectives and generate approximate Pareto front of non-dominated solutions. A comprehensive analysis of objective reduction approach is carried out with existingmulti-objective methods on many-objective DTLZ and WFG test suite which highlight the superiority of proposed technique. Further, the performance of proposed approach is evaluated on satellite images to detect cloudy region against various types of earth’s surfaces. The performance of proposed approach is compared against existing benchmark many-objective algorithm, NSGA-III in order to evaluate the potential of proposed method in clustering application. It is observed that obtained clustering results using reduced objective set of MOSSO algorithm provides almost equivalent accuracy with results obtained using NSGA-III with many-objective set.