Developing AEA system-of-systems mission plans with a multi-objective genetic algorithm

Jason C. HandUber; Jeffrey P. Ridder

doi:10.1117/12.719427

2 May 2007 Developing AEA system-of-systems mission plans with a multi-objective genetic algorithm

Jason C. HandUber, Jeffrey P. Ridder

Proceedings Volume 6563, Evolutionary and Bio-inspired Computation: Theory and Applications; 656304 (2007) https://doi.org/10.1117/12.719427
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

Abstract

The role of an airborne electronic attack (AEA) system-of-systems (SoS) is to increase survivability of friendly aircraft by jamming hostile air defense radars. AEA systems are scarce, high-demand assets and have limited resources with which to engage a large number of radars. Given the limited resources, it is a significant challenge to plan their employment to achieve the desired results. Plans require specifying locations of jammers, as well as the mix of wide- and narrow-band jamming assignments delivered against particular radars. Further, the environment is uncertain as to the locations and emissions behaviors of radars. Therefore, we require plans that are not only capable, but also robust to the variability of the environment. In this paper, we use a multi-objective genetic algorithm to develop capable and robust AEA SoS mission plans. The algorithm seeks to determine the Pareto-front of three objectives - maximize the operational objectives achieved by friendly aircraft, minimize the threat to friendly aircraft, and minimize the expenditure of AEA assets. The results show that this algorithm is able to provide planners with the quantitative information necessary to intelligently construct capable and robust mission plans for an AEA SoS.

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Jason C. HandUber and Jeffrey P. Ridder "Developing AEA system-of-systems mission plans with a multi-objective genetic algorithm", Proc. SPIE 6563, Evolutionary and Bio-inspired Computation: Theory and Applications, 656304 (2 May 2007); https://doi.org/10.1117/12.719427

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KEYWORDS

Radar

Genetic algorithms

Algorithm development

Artificial intelligence

Chemical elements

Defense systems

Receivers

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