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 existing multi-objective methods on many-objective DTLZ and WFG test suite which highlight the superiority of proposed technique. Further, the performance of the proposed approach is evaluated on satellite images to detect cloudy region against various types of earth’s surfaces. The performance of the 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.
Highlights
In this article, it is desirable to consider various obresults obtained using NSGA-III with many-objective jectives to satisfy better performance of cloud clusterset
The performance evaluation of the proposed approach is carried on inverse generational distance (IGD), spacing (SP), hypervolume difference (HVD) and delta (∆) metrics to permit its quantitative assessment with different many-objective test problems
There- 3.4 Results on WFG test suite fore, the comparison is carried out using the performance matrix such as IGD, SP, HVD and Delta with Table 6 reports the potential performance of multi-objective social spider clustering algorithm (MOSSO)
Summary
Reduction in Many-Objective Optimization with Social Spider Algorithm for Cloud Detection in Satellite Images ated without considering desired error tolerance and number of objectives (Yuan et al, 2017). Pareto corner search evolutionary algorithm (PCSEA) has been focused on objective reduction concept using dominance and correlation based approach (Singh et al, 2011b). Literature reveals that use of correlation-based approach cannot ensure the preservation of dominance relation and fails to indicate the reason behind the downside of the algorithm (Brockhoff and Zitzler, 2009; Singh et al, 2011b). – A many-objective reduction technique using multiobjective social spider optimization (MOSSO) is introduced by incorporating correlation based errors in the objective space. – Proposed method is further employed to solve cloud clustering problem in multi-spectral satellite images in order to evaluate the accuracy of clustered result with reduced objective set over original objective set.
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