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Journal of Zhejiang University SCIENCE C 1998 Vol.-1 No.-1 P.

http://doi.org/10.1631/FITEE.2000623


A multi-objective improved bat algorithm based on angle decomposition for electronic jamming task allocation


Author(s):  Luda ZHAO, Bin WANG, Xiaoping JIANG, Yicheng LU, Jun HE

Affiliation(s):  College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China; more

Corresponding email(s):   zld301@126.com

Key Words:  ECM, Jamming task allocation, Multi-objective optimization, MOIBA/AD


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Luda ZHAO, Bin WANG, Xiaoping JIANG, Yicheng LU, Jun HE. A multi-objective improved bat algorithm based on angle decomposition for electronic jamming task allocation[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .

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Abstract: 
In modern warfare, the variety of electronic countermeasure (ECM) equipment and the complexity of combat task organization poses a great challenge to commanders. To address this challenge, we first present a new electronic jamming task allocation model that takes multiple groups, multiple equipment types and multiple targets into account. The resulting model is essentially a large-scale multi-objective combinatorial optimization problem, intractably complicated to solve optimally. We next propose a multi-objective improved bat algorithm based on angle decomposition (MOIBA/AD) to quickly identify high-quality solutions to the model. Our proposed algorithm improves the decomposition strategy by replacing the planar space with the angle space, which helps greatly reduce the difficulty of processing evolutionary individuals and hence the time complexity of the multi-objective evolutionary algorithm based on decomposition(MOEA/D). Moreover, the population replacement strategy is enhanced utilizing the improved bat algorithm (BA), which helps evolutionary individuals avoid getting trapped in local optima. Computational experiments on EJTA problems of different scales demonstrate the superiority of our proposed method over three state-of-the-art multi-objective evolutionary algorithms (MOEAs), including the non-dominated sorting genetic algorithm-II (NSGA-II), MOEA/D, and multi-objective particle swarm optimization (MOPSO). Our proposed method performs better in terms of three performance criteria, and produces solutions of higher quality while maintaining a better distribution of the Pareto solution set.

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