Design of an Optimized Architecture for Manned and Unmanned Combat System-of-Systems: Formulation and Coevolutionary Optimization

Shu, Zhe; Wang, Weiping; Wang, Rui

doi:10.1109/access.2018.2870969

Cited by 9 publications

(5 citation statements)

References 54 publications

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“…The IFM-NSGAIII solves largescale problems, instead of the small-scale problems, as the NSGA-III does, and introduces information feedback to influence the offspring population (Gu and Wang, 2020). The P-NSGA-III that modifies the elitist framework of the NSGA-III uses preferred vectors to improve local search (Shu, Wang, W., and Wang, R., 2018). Also, the Permutation-coded NSGA-III encodes chromosomes as permutations of graph vertices (Prakash et al, 2020).…”

Section: Related Workmentioning

confidence: 99%

EF1-NSGA-III: An evolutionary algorithm based on the first front to obtain non-negative and non-repeated extreme points

2020

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Multi-Objective and Many-objective Optimization problems have been extensively solved through evolutionary algorithms over a few decades. Despite the fact that NSGA-II and NSGA-III are frequently employed as a reference for a comparative evaluation of new evolutionary algorithms, the latter is proprietary. In this paper, we used the basic framework of the NSGA-II, which is very similar to the NSGA-III, with significant changes in its selection operator. We took the first front generated at the non-dominating sort procedure to obtain nonnegative and nonrepeated extreme points. This opensource version of the NSGA-III is called EF1-NSGA-III, and its implementation does not start from scratch; that would be reinventing the wheel. Instead, we took the NSGA-II code from the authors in the repository of the Kanpur Genetic Algorithms Laboratory to extend the EF1-NSGA-III. We then adjusted its selection operator from diversity, based on the crowding distance, to the one found on reference points and preserved its parameters. After that, we continued with the adaptive EF1-NSGA-III (A-EF1-NSGA-III), and the efficient adaptive EF1-NSGA-III (A2-EF1-NSGA-III), while also contributing to explain how to generate different types of reference points. The proposed algorithms resolve optimization problems with constraints of up to 10 objective functions. We tested them on a wide range of benchmark problems, and they showed notable improvements in terms of convergence and diversity by using the Inverted Generational Distance (IGD) and the HyperVolume (HV) performance metrics. The EF1-NSGA-III aims to resolve the power consumption for Centralized Radio Access Networks and the BiObjective Minimum DiameterCost Spanning Tree problems.

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Section: Related Workmentioning

confidence: 99%

EF1-NSGA-III: An evolutionary algorithm based on the first front to obtain non-negative and non-repeated extreme points

2020

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“…A second stage involves the actual process of searching solutions. The human decision maker intervenes here by expressing her preferences with respect to the alternative solutions (see [6]- [12] and [13]). Finally, in the last stage, the decisionmaker chooses the final solution from among several alternatives.…”

Section: Literature Reviewmentioning

confidence: 99%

A Decision Support Tool for Urban Freight Transport Planning Based on a Multi-Objective Evolutionary Algorithm

et al. 2019

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We present an optimization procedure based on a hybrid version of an evolutionary multiobjective decision-making algorithm for its application in urban freight transportation planning problems. This tool is intended to solve the planning problems of a merchandise distribution firm that dispatches small volume fractional loads of fresh foods on daily schedules. The firm owns a network of distribution centers supplying a large number of small businesses in Buenos Aires and its surroundings. The recombination operator of the evolutionary algorithm used here has been designed specifically for this problem. It is intended to embody a strategy that takes into account constraints like temporary closeness, closeness time window and connectivity in order to improve its performance in the clustering phase. The representation allows incorporating specific information about the actual instances of the problem and uses adaptive control of the parameters in the calibration stage. The performance of the proposed optimizer was tested against the results obtained by two evolutionary algorithms, NSGA II and SPEA 2, widely used in similar problems. We use hypervolume as a measure of convergence and dispersion of Pareto fronts. The statistical analysis of the results obtained with the three algorithms uses the Wilcoxon rank sum test, which yields evidence that our procedure provides good results.

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“…Manned/unmanned aerial vehicle cooperative combat can simultaneously take advantages of manned vehicle with abilities of good battlefield perception and strong decisionmaking and the advantages of unmanned vehicle with abilities of flexible tactical maneuver, strongly sustained warfare ability and low cost. It can also effectively reduce casualties, thereby it will be an important mode of aerial combat in the future [1], [2]. The core problem of manned/unmanned aerial vehicle cooperative combat is to realize real-time remotely piloting of UAV [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Prediction-Based Adaptive Sliding Mode Control for Remotely Piloted System With Time Delay and Parameter Uncertainty

Fan

et al. 2019

IEEE Access

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This paper proposes a state prediction adaptive sliding mode (SPASM) control method for the remotely piloted system (RPS). With consideration of the time delay caused by the large transmission delay existing in the RPS, a prediction algorithm is proposed to provide the state prediction by using the state transition matrix. To approximate the uncertain lag of the remotely piloted vehicle (RPV) control augmentation system, an adaptive law is proposed to estimate the parameter uncertainties, and the overestimating problem is resolved efficiently. Meanwhile, to deal with the unmodeled dynamics and the predicted errors, a sliding mode controller is designed to guarantee the robustness of the whole closed-loop system. The simulation results show that the SPASM controller can not only guarantee the stability of the RPS in the presence of large time delay but also has a desirable performance of tracking the pilot's inputs while existing the unmodeled dynamics and parameter uncertainties.INDEX TERMS Remotely piloted system (RPS), remotely piloted vehicle (RPV), time delay, state prediction, adaptive sliding mode control.

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Design of an Optimized Architecture for Manned and Unmanned Combat System-of-Systems: Formulation and Coevolutionary Optimization

Cited by 9 publications

References 54 publications

EF1-NSGA-III: An evolutionary algorithm based on the first front to obtain non-negative and non-repeated extreme points

EF1-NSGA-III: An evolutionary algorithm based on the first front to obtain non-negative and non-repeated extreme points

A Decision Support Tool for Urban Freight Transport Planning Based on a Multi-Objective Evolutionary Algorithm

Prediction-Based Adaptive Sliding Mode Control for Remotely Piloted System With Time Delay and Parameter Uncertainty

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