A Greedy Ant Colony System for Defensive Resource Assignment Problems

Rezende, Mônica de; Lima, Beatriz Souza Leite Pires de; Guimarães, Solange Terezinha de Lima

doi:10.1080/08839514.2018.1451137

Cited by 14 publications

(5 citation statements)

References 11 publications

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“…In general, assignment problems are NP-hard, and hence can be solved only approximately for large number of decision variables. For example, authors in Rezende et al (2018) provide a greedy approach based on ant colony system to solve the WTA problem. Multi-agent defense problems are difficult to solve optimally because the problem becomes computationally intractable for large number of agents.…”

Section: Related Workmentioning

confidence: 99%

Aerial Swarm Defense by StringNet Herding: Theory and Experiments

2021

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This paper studies a defense approach against one or more swarms of adversarial agents. In our earlier work, we employed a closed formation (“StringNet”) of defending agents (defenders) around a swarm of adversarial agents (attackers) to confine their motion within given bounds, and guide them to a safe area. The adversarial agents were assumed to remain close enough to each other, i.e., within a prescribed connectivity region. To handle situations when the attackers no longer stay within such a connectivity region, but rather split into smaller swarms (clusters) to maximize the chance or impact of attack, this paper proposes an approach to learn the attacking sub-swarms and reassign defenders toward the attackers. We use a “Density-based Spatial Clustering of Application with Noise (DBSCAN)” algorithm to identify the spatially distributed swarms of the attackers. Then, the defenders are assigned to each identified swarm of attackers by solving a constrained generalized assignment problem. We also provide conditions under which defenders can successfully herd all the attackers. The efficacy of the approach is demonstrated via computer simulations, as well as hardware experiments with a fleet of quadrotors.

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Section: Related Workmentioning

confidence: 99%

Aerial Swarm Defense by StringNet Herding: Theory and Experiments

2021

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“…In Equation (10), α is a penalty factor. To eliminate the effect of inconsistent dimensions, all items at the RHS of Equation ( 10) are normalized.…”

Section: Bpso Algorithmmentioning

confidence: 99%

“…The huge combinatorial complexity of the problem means that even with supercomputers now available, exact optimal solutions cannot be obtained in real time. So, researchers have focused on developing modern heuristic algorithms such as the genetic algorithm (GA), simulated annealing algorithm (SA), ant colony optimization (ACO), particle swarm optimization (PSO) and sine cosine algorithm (SCA), as well as some variant algorithms [10]. These heuristic algorithms have been developed and are widely used as search algorithms in a variety of applications.…”

Section: Introductionmentioning

confidence: 99%

Swarm Intelligence Algorithms for Weapon-Target Assignment in a Multilayer Defense Scenario: A Comparative Study

Cao

Fang

2020

Symmetry

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Weapon-target assignment (WTA) is a kind of NP-complete problem in military operations research. To solve the multilayer defense WTA problems when the information about enemy’s attacking plan is symmetric to the defender, we propose four heuristic algorithms based on swarm intelligence with customizations and improvements, including ant colony optimization (ACO), binary particle swarm optimization (BPSO), integer particle swarm optimization (IPSO) and sine cosine algorithm (SCA). Our objective is to assess and compare the performance of different algorithms to determine the best algorithm for practical large-scale WTA problems. The effectiveness and performance of various algorithms are evaluated and compared by means of a benchmark problem with a small scale, the theoretical optimal solution of which is known. The four algorithms can obtain satisfactory solutions to the benchmark problem with high quality and high robustness, while IPSO is superior to BPSO, ACO and SCA with respect to the solution quality, algorithmic robustness and computational efficiency. Then, IPSO is applied to a large-scale WTA problem, and its effectiveness and performance are further assessed. We demonstrate that IPSO is capable of solving large-scale WTA problems with high efficiency, high quality and high robustness, thus meeting the critical requirements of real-time decision-making in modern warfare.

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“…With the development of computer performance, numerous methods are developed to solve the WTA problems, such as branch and bound [2], PSO [3,8,13], artificial bee colony algorithm [5], genetic algorithm [10][11][12], large-scale neighborhood algorithm [14,15], and ant colony algorithm [16,17]. Some new methods are also applied to deal with the WTA problem such as simplified swarm optimization [4], multiscale quantum harmonic oscillator algorithm [7], marginal-return-based constructive heuristic [9], and Markov decision model [18].…”

Section: Introductionmentioning

confidence: 99%

Research on Dynamic Weapon Target Assignment Based on Cross-Entropy

Huang

et al. 2020

Mathematical Problems in Engineering

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The weapon target assignment (WTA) is a classical problem of defense-related applications which is proved to be a NP-complete problem. In this paper, a practical and available dynamic weapon target assignment (DWTA) formulation is given which incorporates two meaningful and conflicting objectives, that is, minimizing weapon costs and maximizing combat benefits. As we know, heuristic methods have some shortcomings such as slow convergence speed and local optimum in solving the nonlinear integer optimization problem. To this end, a novel DWTA algorithm based on cross-entropy (CE) method is introduced, where the resources requirement condition for targets is taken into consideration. The CE method associates an estimation problem with the DWTA optimization problem, and then, the estimation problem is transformed into a convex optimization problem. The Karush–Kuhn–Tucker conditions are applied to solve the convex optimization problem, and the iteration formulas to find the optimal solution are deducted. Furthermore, in order to verify the performance of CE method in dealing with the DWTA problem, several simulations in different combat scenarios are implemented. The results reveal that, compared with the benchmark heuristic and Monte-Carlo (MC) methods, there are some notable advantages in solving the DWTA problem based on CE method with regard to the solution quality and time consumption.

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A Greedy Ant Colony System for Defensive Resource Assignment Problems

Cited by 14 publications

References 11 publications

Aerial Swarm Defense by StringNet Herding: Theory and Experiments

Aerial Swarm Defense by StringNet Herding: Theory and Experiments

Swarm Intelligence Algorithms for Weapon-Target Assignment in a Multilayer Defense Scenario: A Comparative Study

Research on Dynamic Weapon Target Assignment Based on Cross-Entropy

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