A New Approach to Weapon-Target Assignment in Cooperative Air Combat

Chang, Yu-Teng; Li, Zhanwu; Kou, Yingxin; Sun, Qibin; Yang, Huazhong; Zhao, Zhiyuan

doi:10.1155/2017/2936279

Cited by 16 publications

(12 citation statements)

References 24 publications

(26 reference statements)

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“…The application of metaheuristic optimization algorithms to structural optimization is an active area of research. Among a large number of metaheuristic algorithms, the harmony search (HS) algorithm is one of the most widely used and established techniques, and applied to numerous areas such as structural design [ 23 ], water network design [ 24 ], flood model calibration [ 25 ], economic load dispatch [ 26 ], concrete mix proportion design [ 27 ], chaotic systems [ 28 ], timetabling [ 29 ], weapon target assignment [ 30 ], stock price prediction [ 31 ], mobile network security [ 32 ], COVID-19 detection from CT scans [ 33 ], and subway ventilation [ 34 ].…”

Section: Methods Of Optimization and Predictive Model Developmentmentioning

confidence: 99%

Optimal Dimensioning of Retaining Walls Using Explainable Ensemble Learning Algorithms

et al. 2022

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This paper develops predictive models for optimal dimensions that minimize the construction cost associated with reinforced concrete retaining walls. Random Forest, Extreme Gradient Boosting (XGBoost), Categorical Gradient Boosting (CatBoost), and Light Gradient Boosting Machine (LightGBM) algorithms were applied to obtain the predictive models. Predictive models were trained using a comprehensive dataset, which was generated using the Harmony Search (HS) algorithm. Each data sample in this database consists of a unique combination of the soil density, friction angle, ultimate bearing pressure, surcharge, the unit cost of concrete, and six different dimensions that describe an optimal retaining wall geometry. The influence of these design features on the optimal dimensioning and their interdependence are explained and visualized using the SHapley Additive exPlanations (SHAP) algorithm. The prediction accuracy of the used ensemble learning methods is evaluated with different metrics of accuracy such as the coefficient of determination, root mean square error, and mean absolute error. Comparing predicted and actual optimal dimensions on a test set showed that an R2 score of 0.99 could be achieved. In terms of computational speed, the LightGBM algorithm was found to be the fastest, with an average execution speed of 6.17 s for the training and testing of the model. On the other hand, the highest accuracy could be achieved by the CatBoost algorithm. The availability of open-source machine learning algorithms and high-quality datasets makes it possible for designers to supplement traditional design procedures with newly developed machine learning techniques. The novel methodology proposed in this paper aims at producing larger datasets, thereby increasing the applicability and accuracy of machine learning algorithms in relation to optimal dimensioning of structures.

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Section: Methods Of Optimization and Predictive Model Developmentmentioning

confidence: 99%

Optimal Dimensioning of Retaining Walls Using Explainable Ensemble Learning Algorithms

et al. 2022

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“…SPMA decision making is often referred in the literature as multiple attribute, multiple criteria, or multiple objective decision making. 15 In the formulation, discrete alternatives are evaluated against different kinds of attributes (criteria) in order to obtain a ranking of the alternatives. Some swarm optimization algorithms such as ant colonies 38 and hybrid algorithms consisting of improved artificial fish swarm algorithm (AFSA) and improved harmony search (HS) 39 are applied to WTA problem. Using ant colonies algorithm, a solution is developed considering WTA problem as a bi-objective WTA (BOWTA) optimization model which maximizes the expected damage of the enemy and minimizes the cost of missiles.…”

Section: Extended Literaturementioning

confidence: 99%

“…Some swarm optimization algorithms such as ant colonies 38 and hybrid algorithms consisting of improved artificial fish swarm algorithm (AFSA) and improved harmony search (HS) 39 are applied to WTA problem. Using ant colonies algorithm, a solution is developed considering WTA problem as a bi-objective WTA (BOWTA) optimization model which maximizes the expected damage of the enemy and minimizes the cost of missiles.…”

Section: Extended Literaturementioning

confidence: 99%

Agent-based target evaluation and fire doctrine: an aspect-oriented programming view

Hocaoğlu

2021

Journal of Defense Modeling & Simulation

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Threat evaluation is a vital process for any defense system, and it consists of a series of calculation and evaluation based on the inferred capabilities and intents of the targets that aim to give damage to defended assets. Target evaluation is proceeded in a wargame and the aim is to compare weapon target pairs according to a set of criteria. The target evaluation cycle is repeated anytime a new detection is received and when any change happens in the target currently detected. The whole process consists of a set of tasks that are shared between Command and Control units and the tasks require different responsibilities. Each task is succeeded by a specific behavior that is represented as a reasonably ordered set of actions. The task sharing is organized by taking the C2 architectures into account. In this paper, an agent-based command and control entity, which is in charge of target evaluation and giving engagement decision, is designed and it is situated in an air defense simulation environment. The study aims to propose an agent design in military decision-making domain, bringing analytic methods with the first-order logic together, and combine aspect orientation with agent design. The study also improves dynamic aspect management in agent programming using the relation concept.

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“…To assist with the ever more complex and rapidly changing combat environment, combat systems require access to real-time adaptable control systems that can handle uncertain information to provide intelligent decision-making when executing combat tasks. Research work in this area seeks to realize the effective utilization of combat resources by minimizing defensive assets loss, amount of resource utilized during interception, and maximizing the effective kill probability while operating within the multiple constraints of the operating platforms [5,[7][8][9][10]. Effective combat systems in a multi-agent setting must be able to solve conflicts of interest and optimize resource allocation.…”

Section: Introductionmentioning

confidence: 99%

A Coordinated Air Defense Learning System Based on Immunized Classifier Systems

Nantogma

Ran

2021

Symmetry

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Autonomous (unmanned) combat systems will become an integral part of modern defense systems. However, limited operational capabilities, the need for coordination, and dynamic battlefield environments with the requirement of timeless in decision-making are peculiar difficulties to be solved in order to realize intelligent systems control. In this paper, we explore the application of the learning classifier system and Artificial Immune models for coordinated self-learning air defense systems. In particular, this paper presents a scheme that implements an autonomous cooperative threat evaluation and weapon assignment learning approach. Taking into account uncertainties in a successful interception, target characteristics, weapon type and characteristics, closed-loop coordinated behaviors, we adopt a hierarchical multi-agent approach to coordinate multiple combat platforms to achieve optimal performance. Based on the combined strengths of learning classifier and artificial immune-based algorithms, the proposed scheme consists of two categories of agents; a strategy generation agent inspired by learning classifier system, and strategy coordination inspired by Artificial Immune System mechanisms. An experiment in a realistic environment shows that the adopted hybrid approach can be used to learn weapon-target assignment for multiple unmanned combat systems to successfully defend against coordinated attacks. The presented results show the potential for hybrid approaches for an intelligent system enabling adaptable and collaborative systems.

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A New Approach to Weapon-Target Assignment in Cooperative Air Combat

Cited by 16 publications

References 24 publications

Optimal Dimensioning of Retaining Walls Using Explainable Ensemble Learning Algorithms

Optimal Dimensioning of Retaining Walls Using Explainable Ensemble Learning Algorithms

Agent-based target evaluation and fire doctrine: an aspect-oriented programming view

A Coordinated Air Defense Learning System Based on Immunized Classifier Systems

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