Air combat tactics optimization using stochastic genetic algorithms

Mulgund, Sandeep; Harper, Karen A.; Krishnakumar, Kalmanje; Zacharias, Greg L.

doi:10.1109/icsmc.1998.726484

Cited by 25 publications

(18 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [5], Krishnakumar et al presented Stochastic Genetic Algorithms (Stochastic GAs) to solve problems with a large number of real design parameters efficiently. Stochastic GAs have been successfully applied to Integrated Flight Propulsion Controller designs [5] and air combat tactics optimization [6]. As they mentioned, the Stochastic GAs bridge the gap between ES and GAs to handle large design problems.…”

Section: Adaptive Range Genetic Algorithmsmentioning

confidence: 99%

Transonic Wing Shape Optimization Based on Evolutionary Algorithms

Obayashi

Oyama

Nakamura

2000

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract.A practical three-dimensional shape optimization for aerodynamic design of a transonic wing has been performed using Evolutionary Algorithms (EAs). Because EAs coupled with aerodynamic function evaluations require enormous computational time, Numerical Wind Tunnel (NWT) located at National Aerospace Laboratory in Japan has been utilized based on the simple master-slave concept. Parallel processing makes EAs a very promising approach for practical aerodynamic design.

show abstract

Section: Adaptive Range Genetic Algorithmsmentioning

confidence: 99%

Transonic Wing Shape Optimization Based on Evolutionary Algorithms

Obayashi

Oyama

Nakamura

2000

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…The air combat situation and the solution of MTA problem are shown in Fig. 3, in which team F and targets T fly head on at the same altitude with different tactical formations [9] . At this moment, team F performs a preemptive and coordinated attack on targets T.…”

Section: T~5 Tmentioning

confidence: 99%

Improved Particle Swarm Optimization Algorithm and its Application in Coordinated Air Combat Missile-Target Assignment

Teng

Huigang

Huang

et al. 2008

2008 7th World Congress on Intelligent Control and Automation

View full text Add to dashboard Cite

Based on the analysis of the basic particle swarm optimization (PSO) algorithm, an improved particle swarm optimization (IPSO) algorithm was proposed to solve the problem with missile-target assignment in coordinated air combat (MTACAC). There were three improvements: 1. Adaptive adjustment of inertia weight; 2. Amelioration of particle velocity and position; 3. Better optimization strategy. Based on the principles of coordinated air combat efficiency and operational research, a missile-target assignment mathematical model was established. The IPSO algorithm was applied to seek the optimal missile assignment scheme for multi-target coordinated air-to-air combat. The simulation result indicated that the model of MTACAC was practical and feasible, and the IPSO algorithm was fast, simple, and more effective in finding out the global optimum assignment, when compared with the basic PSO algorithm and the genetic algorithm (GA).Index Terms -intelligent algorithm; improved particle swarm optimization; coordinated air combat; missile-target assignment

show abstract

“…As the matter of fact, the DM model for CMTA has been proposed for many years and many algorithms have been applied. For example, Rosenberger et al, 5 Lee et al, 6 Ahuja et al, 7 Kewley and Embrechts 8 and Mulgund et al 9 In these papers, the DM problem is considered as a combinatorial optimization problem and genetic algorithms (GAs) are applied. However GAs with low efficiency may easily suffer from prematurity.…”

Section: Introductionmentioning

confidence: 99%

Heuristic Quantum Genetic Algorithm for Air Combat Decision Making on Cooperative Multiple Target Attack

Kong

2013

Int. J. Model. Simul. Sci. Comput.

View full text Add to dashboard Cite

In order to achieve the optimal attack outcome in the air combat under the beyond visual range (BVR) condition, the decision-making (DM) problem which is to set a proper assignment for the friendly fighters on the hostile fighters is the most crucial task for cooperative multiple target attack (CMTA). In this paper, a heuristic quantum genetic algorithm (HQGA) is proposed to solve the DM problem. The originality of our work can be supported in the following aspects: (1) the HQGA assigns all hostile fighters to every missile rather than fighters so that the HQGA can encode chromosomes with quantum bits (Q-bits); (2) the relative successful sequence probability (RSSP) is defined, based on which the priority attack vector is constructed; (3) the HQGA can heuristically modify quantum chromosomes according to modification technique proposed in this paper; (4) last but not the least, in some special conditions, the HQGA gets rid of the constraint described by other algorithms that to obtain a better result. In the end of this paper, two examples are illustrated to show that the HQGA has its own advantage over other algorithms when dealing with the DM problem in the context of CMTA.

show abstract

Air combat tactics optimization using stochastic genetic algorithms

Cited by 25 publications

References 4 publications

Transonic Wing Shape Optimization Based on Evolutionary Algorithms

Transonic Wing Shape Optimization Based on Evolutionary Algorithms

Improved Particle Swarm Optimization Algorithm and its Application in Coordinated Air Combat Missile-Target Assignment

Heuristic Quantum Genetic Algorithm for Air Combat Decision Making on Cooperative Multiple Target Attack

Contact Info

Product

Resources

About