Feature selection (FS) is vital in hyperspectral image (HSI) classification, it is an NP-hard problem, and swarm intelligence and evolutionary algorithms (SIEAs) have been proved effective in solving it. However, the ...
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Feature selection (FS) is vital in hyperspectral image (HSI) classification, it is an NP-hard problem, and swarm intelligence and evolutionary algorithms (SIEAs) have been proved effective in solving it. However, the high dimensionality of HSIs still leads to the inefficient operation of SIEAs. In addition, many SIEAs exist, but few studies have conducted a comparative analysis of them for HSI FS. Thus, our study has two goals: (1) to propose a new filter-wrapper (F-W) framework that can improve the SIEAs' performance;and (2) to apply ten SIEAs under the F-W framework (F-W-SIEAs) to optimize the support vector machine (SVM) and compare their performance concerning five aspects, namely the accuracy, the number of selected bands, the convergence rate, and the relative runtime. Based on three HSIs (i.e., Indian Pines, Salinas, and Kennedy Space Center (KSC)), we demonstrate how the proposed framework helps improve these SIEAs' performances. The five aspects of the ten algorithms are different, but some have similar optimization capacities. On average, the F-W-Genetic Algorithm (F-W-GA) and F-W-Grey Wolf Optimizer (F-W-GWO) have the strongest optimization abilities, while the F-W-GWO requires the least runtime among the ten. The F-W-Marine Predators Algorithm (F-W-MPA) is second only to the two and slightly better than F-W-Differential Evolution (F-W-DE). The F-W-Ant Lion Optimizer (F-W-ALO), F-W-I-Ching Divination evolutionary Algorithm (F-W-IDEA), and F-W-Whale Optimization Algorithm (F-W-WOA) have the middle optimization abilities, and F-W-IDEA takes the most runtime. Moreover, the F-W-SIEAs outperform other commonly used FS techniques in accuracy overall, especially in complex scenes.
This paper addresses a ternary-integration scheduling problem that incorporates employee timetabling into the scheduling of machines and transporters in a job-shop environment with a finite number of heterogeneous tra...
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This paper addresses a ternary-integration scheduling problem that incorporates employee timetabling into the scheduling of machines and transporters in a job-shop environment with a finite number of heterogeneous transporters where the objective is to minimize the completion time of all jobs. The problem is first formulated as a mixed-integer linear programming model. Then, an Anarchic Society Optimization (ASO) algorithm is developed to solve large-sized instances of the problem. The formulation is used to solve small-sized instances and to evaluate the quality of the solutions obtained for instances with larger sizes. A comprehensive numerical study is carried out to assess the performance of the proposed ASO algorithm. The algorithm is compared with three alternative metaheuristic algorithms. It is also compared with several algorithms developed in the literature for the integrated scheduling of machines and transporters. Moreover, the algorithms are tested on a set of adapted benchmark instances for an integrated problem of machine scheduling and employee timetabling. The numerical analysis suggests that the ASO algorithm is both effective and efficient in solving large-sized instances of the proposed integrated job shop scheduling problem. (C) 2017 Elsevier Ltd. All rights reserved.
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