A multi-objective approach for dynamic missile allocation using artificial neural networks for time sensitive decisions

Karasakal, Orhan; Karasakal, Esra; Silav, Ahmet

doi:10.1007/s00500-021-05923-x

Cited by 8 publications

(6 citation statements)

References 26 publications

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“…Compared to the previous approaches, the generalization of the information on DM preferences and the innovative search for improved solutions are the characteristics of this approach. In this approach, many types of nonlinear preference structures can also be represented [17,18]. Using neural networks to solve MOP problems has several advantages over methods based on utility functions and interactive methods that use utility functions.…”

Section: Introductionmentioning

confidence: 99%

A New Decision-Making GMDH Neural Network: Effective for Limited and Fuzzy Data

Hong

Zhao

Kausar

et al. 2022

Computational Intelligence and Neuroscience

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This paper presents a new approach to solve multi-objective decision-making (DM) problems based on neural networks (NN). The utility evaluation function is estimated using the proposed group method of data handling (GMDH) NN. A series of training data is obtained based on a limited number of initial solutions to train the NN. The NN parameters are adjusted based on the error propagation training method and unscented Kalman filter (UKF). The designed DM is used in solving the practical problem, showing that the proposed method is very effective and gives favorable results, under limited fuzzy data. Also, the results of the proposed method are compared with some similar methods.

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Section: Introductionmentioning

confidence: 99%

A New Decision-Making GMDH Neural Network: Effective for Limited and Fuzzy Data

Hong

Zhao

Kausar

et al. 2022

Computational Intelligence and Neuroscience

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“…The CBAA can solve only one-to-one target assignment problems, where each agent can select at most one target, and each target can be assigned to at most one agent. However, it cannot be applied to the multi-target attack problem of a single aircraft and the problem of multiple aircraft attacking the same target cooperatively in multi-aircraft cooperative BVR air combat, and thus is not consistent with the target assignment model defined by Equation (8).…”

Section: Mtcbaamentioning

confidence: 99%

“…Theorem 1. Under the same situational conditions, the MTCBAA and MTSGA generate the same target assignment scheme for the multi-aircraft cooperative BVR air combat target assignment problem given by Equation (8).…”

Section: Minimum Optimization Performance Of Mtcbaamentioning

confidence: 99%

“…It should be noted that the TOBI assumption does not always hold for the target assignment problem defined by Equation (8). For a target b j that needs to be assigned twice in the MTSGA, if its first assignment iteration is advanced by the target exchange shown in Equation (31)-i.e., the index of iteration corresponding to the first assignment of b j in the MTSGA is k 1 and the index of iteration corresponding to the second assignment of b j in MTSGA is k 2 -after the target swapping corresponding to Equation (31), the index of iteration corresponding to the first assignment of b j becomes k 1 = N min − k 2 + 1, and k 1 < k 1 , which correspond to two cases.…”

Section: Minimum Optimization Performance Of Mtcbaamentioning

confidence: 99%

“…Common target assignment methods based on the central node can be roughly divided into two categories: optimization methods [5] and heuristic methods [6,7]. With the development of artificial intelligence technology, centralized target assignment methods based on neural networks [8], reinforcement learning [9], and decision trees [10] have emerged in recent years. In the early stage, the centralized target assignment was the most widely associated target assignment approach but, to obtain global situational information for the central node, a large amount of communication is required.…”

Section: Introductionmentioning

confidence: 99%

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A Multi-Target Consensus-Based Auction Algorithm for Distributed Target Assignment in Cooperative Beyond-Visual-Range Air Combat

Lyu

Dai

et al. 2022

Aerospace

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With recent advances in airborne weapons, air combat tends to occur in the form of beyond-visual-range (BVR) combat and multi-aircraft cooperation. Target assignment is critical in multi-aircraft BVR air combat decision-making. Most previous research on target assignment for multi-aircraft cooperative BVR air combat has focused on centralized algorithms, which can be time-consuming and unreliable. This paper proposes an efficient distributed target assignment algorithm called the multi-target consensus-based auction algorithm (MTCBAA). First, by analyzing the main geometric aspects of BVR air combat, a target assignment model for cooperative BVR air combat was established. Next, based on a consensus-based auction algorithm (CBAA), the MTCBAA was developed to solve the target assignment problem by introducing a cooperative decision-making variable. Although the MTCBAA is based on a greedy mechanism, it can guarantee at least 50% global optimization performance, which was proven through a demonstration of the minimum optimization performance of a centralized target assignment algorithm, since the centralized algorithm is equivalent to the MTCBAA. Finally, experiments were conducted, including an experiment that illustrates the operation of the proposed algorithm, Monte Carlo comparisons with a centralized target assignment method based on the immune algorithm, and deployment experiments on a semi-physical simulation platform. Compared with the heuristic target assignment algorithm, the proposed algorithm significantly improved the target assignment efficiency. The practicality of the proposed algorithm was further verified through a distributed semi-physical simulation experiment.

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Naval Air Defense Planning problem: A novel formulation and heuristics

Arslan,

Karasakal,

Kırca

2024

Naval Research Logistics

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This article focuses on air defense in maritime environment, which involves protecting friendly naval assets from aerial threats. Specifically, we define and address the Naval Air Defense Planning (NADP) problem, which consists of maneuvering decisions of the ships and scheduling weapons and sensors to the threats in order to maximize the total expected survival probability of friendly units. The NADP problem is more realistic and applicable than previous studies, as it considers features such as sensor assignment requirements, weapon and sensor blind sectors, sequence‐dependent setup times, and ship's infrared/radar signature. In this study, a mixed‐integer nonlinear programming model of the NADP problem is presented and heuristic solution approaches are developed. Computational results demonstrate that these heuristic approaches are both fast and efficient in solving the NADP problem.

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A multi-objective approach for dynamic missile allocation using artificial neural networks for time sensitive decisions

Cited by 8 publications

References 26 publications

A New Decision-Making GMDH Neural Network: Effective for Limited and Fuzzy Data

A New Decision-Making GMDH Neural Network: Effective for Limited and Fuzzy Data

A Multi-Target Consensus-Based Auction Algorithm for Distributed Target Assignment in Cooperative Beyond-Visual-Range Air Combat

Naval Air Defense Planning problem: A novel formulation and heuristics

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