Topographical-Geodetic Data for Tactical and Technical Control of Artillery Fire

Bláha, Martin; Šilinger, Karel; Prikryl, B.

doi:10.1109/eecs.2017.14

Cited by 8 publications

(7 citation statements)

References 3 publications

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“…The scenario was scripted prior to experiment and no further human interference in the scenario occurred during the execution phase of experiment. 18 4.2.1. Scenario.…”

Section: Experiments Designmentioning

confidence: 99%

Use of an aerial reconnaissance model during the movement of oversized loads

Drozd

Neubauer

2019

Journal of Defense Modeling & Simulation

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The paper sets out the results of an experiment carried out using the Virtual Battle Space 2 simulator to verify the applicability of an autonomous unmanned aerial system (UAS) reconnaissance model when moving oversized loads. The model is originally implemented within a decision-support system under military conditions. The aim of the experiment is to verify the possibilities of using the model for civilian needs. The basic output of the experiment presented in the paper is confirmation that the autonomous UAS reconnaissance model can be applied in the actual transportation of oversized loads: the experiment confirms the author’s hypothesis. Based on the result of the experiment it is possible to state that a slightly modified UAS model can significantly influence the execution phase of the oversized load movement in a positive way. It shows that the use of the model reduces the time of the movement and avoids unexpected interferences.

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“…The scenario was scripted prior to experiment and no further human interference in the scenario occurred during the execution phase of experiment. 18 4.2.1. Scenario.…”

Section: Experiments Designmentioning

confidence: 99%

Use of an aerial reconnaissance model during the movement of oversized loads

Drozd

Neubauer

2019

Journal of Defense Modeling & Simulation

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“…It has been implemented into several models of military tactics, such as optimal cooperative reconnaissance by aerial and/or ground unmanned vehicles, and optimal logistics distribution on the battlefield. More details about the information support of commanders can be found in [33][34][35][36][37][38][39].…”

Section: Practical Applicationmentioning

confidence: 99%

Using Metaheuristics on the Multi-Depot Vehicle Routing Problem with Modified Optimization Criterion

Stodola

2018

Algorithms

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This article deals with the modified Multi-Depot Vehicle Routing Problem (MDVRP). The modification consists of altering the optimization criterion. The optimization criterion of the standard MDVRP is to minimize the total sum of routes of all vehicles, whereas the criterion of modified MDVRP (M-MDVRP) is to minimize the longest route of all vehicles, i.e., the time to conduct the routing operation is as short as possible. For this problem, a metaheuristic algorithm-based on the Ant Colony Optimization (ACO) theory and developed by the author for solving the classic MDVRP instances-has been modified and adapted for M-MDVRP. In this article, an additional deterministic optimization process which further enhances the original ACO algorithm has been proposed. For evaluation of results, Cordeau's benchmark instances are used.Algorithms 2018, 11, 74 2 of 14 was introduced by Montoya-Torres et al. [6], who consider papers published between 1988 and 2014. Several variants of MDVRP are studied in the article: time windows, split delivery, heterogeneous fleet, periodic deliveries, and pickup and delivery. Another survey of publications on VRP and its variants can be found in [7], where 277 articles published between 2009 and 2015 are classified.Popular metaheuristic algorithms for MDVRP are based on the tabu search approach. One of the first tabu search-based algorithms was published by Gillet and Johnson in 1976 [8]. The same approach is used by Chao, Golden and Vasil [9], or Renaud, Laporte and Boctor [10], and indeed, examples of new algorithms using the tabu search approach continue to be published [11,12].Other popular metaheuristic approaches for MDVRP are simulated annealing [13,14] and genetic algorithms [15]. A hybrid algorithm, based on both simulated annealing and the genetic approach, was proposed by Seidgar et al. [16]. An excellent survey of papers using genetic algorithms for MDVRP can be found in [17].More recently, bio-inspired algorithms have also been broadly used for MDVRP. Ant colony optimization algorithms are inspired by the behavior of ants when searching for food. There are many publications using this for MDVRP. Yu, Yang and Xie [18] proposed an algorithm which transfers the problem to VRP by inserting a virtual central depot (V-MDVRP). Another solution was proposed by Yao et al. [19], who applied the ACO algorithm to a seafood product delivery routing problem. The most recent works include algorithms by Yalian [20], Gao [21], and Ma et al. [22].In the last few years, hybrid metaheuristic methods have emerged. They combine features from various methodologies to take advantage of their different strengths. Ting and Chen [23] introduced a combination of the ant colony algorithm, and simulated annealing for MDVRP with time windows. Vidal et al. [24] proposed a method which combines the exploration breadth of population-based evolutionary searches, the aggressive-improvement capabilities of neighborhood-based metaheuristics, and advanced population-diversity management schemes. Oliveira et al. [25] de...

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“…The algorithm proposed in this article is used to plan the route of the UGV. More information about the model can be found in [ 3 ]; the general topic of decision support for commanders is explored in [ 4 , 5 , 6 , 7 , 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Algorithm Based on Ant Colony Optimization and Simulated Annealing Applied to the Dynamic Traveling Salesman Problem

Stodola

Michenka

Nohel

et al. 2020

Entropy

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The dynamic traveling salesman problem (DTSP) falls under the category of combinatorial dynamic optimization problems. The DTSP is composed of a primary TSP sub-problem and a series of TSP iterations; each iteration is created by changing the previous iteration. In this article, a novel hybrid metaheuristic algorithm is proposed for the DTSP. This algorithm combines two metaheuristic principles, specifically ant colony optimization (ACO) and simulated annealing (SA). Moreover, the algorithm exploits knowledge about the dynamic changes by transferring the information gathered in previous iterations in the form of a pheromone matrix. The significance of the hybridization, as well as the use of knowledge about the dynamic environment, is examined and validated on benchmark instances including small, medium, and large DTSP problems. The results are compared to the four other state-of-the-art metaheuristic approaches with the conclusion that they are significantly outperformed by the proposed algorithm. Furthermore, the behavior of the algorithm is analyzed from various points of view (including, for example, convergence speed to local optimum, progress of population diversity during optimization, and time dependence and computational complexity).

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Topographical-Geodetic Data for Tactical and Technical Control of Artillery Fire

Cited by 8 publications

References 3 publications

Use of an aerial reconnaissance model during the movement of oversized loads

Use of an aerial reconnaissance model during the movement of oversized loads

Using Metaheuristics on the Multi-Depot Vehicle Routing Problem with Modified Optimization Criterion

Hybrid Algorithm Based on Ant Colony Optimization and Simulated Annealing Applied to the Dynamic Traveling Salesman Problem

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