DDL-NSGA-II: A Distributed Navigation Interference Source Deployment Method Based on Improved NSGA-II

Distributed navigation interference is a critical technique for degrading the performance of multi-element adaptive nulling antennas, where optimal spatial deployment of interference sources is pivotal to maximizing overall interference effectiveness. However, interference signal propagation is constrained by transmit power, path loss, and terrain occlusion, rendering optimal cooperative source deployment in complex terrain a core challenge in navigation spectrum analysis and interference management. To address this gap, this paper proposes DDL-NSGA-II, an improved NSGA-II algorithm for distributed navigation interference source deployment. By integrating the free-space propagation model with reference-plane-based viewshed analysis, the effective coverage of interference signals in 3D terrain is estimated, and a bi-objective model is constructed with multi-weight coverage rate and effective dispersion as optimization objectives. Three well-established mechanisms are adapted and integrated into standard NSGA-II for this specific problem: differential evolution mutation with binomial crossover, stage-wise dynamic deduplication, and external archive-assisted local search refinement. The contribution lies in their problem-specific integration and adaptation to integer-encoded multi-source deployment, which significantly enhances global exploration and convergence accuracy. Simulation results show that DDL-NSGA-II delivers high-coverage deployment schemes with favorable dispersion under varying terrain resolutions, source quantities, and single-station interference ranges. It outperforms three baseline algorithms in convergence, Pareto front coverage, and solution uniformity, providing robust algorithmic support for intelligent multi-source cooperative deployment in complex terrain.

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Publication Details

Journal
Algorithms
Published
2026-10-14
DOI
https://doi.org/10.3390/a19100880
Primary Topic
Advanced Multi-Objective Optimization Algorithms
Type
article
Field-Weighted Citation Impact
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article

DDL-NSGA-II: A Distributed Navigation Interference Source Deployment Method Based on Improved NSGA-II

Weijie Kang, Xiaowei Shen, Guodong Duan, Bing He
Algorithms
Advanced Multi-Objective Optimization Algorithms
article

DDL-NSGA-II: A Distributed Navigation Interference Source Deployment Method Based on Improved NSGA-II

Weijie Kang, Xiaowei Shen, Guodong Duan, Bing He
article en

Abstract

Distributed navigation interference is a critical technique for degrading the performance of multi-element adaptive nulling antennas, where optimal spatial deployment of interference sources is pivotal to maximizing overall interference effectiveness. However, interference signal propagation is constrained by transmit power, path loss, and terrain occlusion, rendering optimal cooperative source deployment in complex terrain a core challenge in navigation spectrum analysis and interference management. To address this gap, this paper proposes DDL-NSGA-II, an improved NSGA-II algorithm for distributed navigation interference source deployment. By integrating the free-space propagation model with reference-plane-based viewshed analysis, the effective coverage of interference signals in 3D terrain is estimated, and a bi-objective model is constructed with multi-weight coverage rate and effective dispersion as optimization objectives. Three well-established mechanisms are adapted and integrated into standard NSGA-II for this specific problem: differential evolution mutation with binomial crossover, stage-wise dynamic deduplication, and external archive-assisted local search refinement. The contribution lies in their problem-specific integration and adaptation to integer-encoded multi-source deployment, which significantly enhances global exploration and convergence accuracy. Simulation results show that DDL-NSGA-II delivers high-coverage deployment schemes with favorable dispersion under varying terrain resolutions, source quantities, and single-station interference ranges. It outperforms three baseline algorithms in convergence, Pareto front coverage, and solution uniformity, providing robust algorithmic support for intelligent multi-source cooperative deployment in complex terrain.

AlgorithmsVol. 19(10)
PLA Rocket Force University of Engineering (CN)
Openalex Percentile: Top 14%
Advanced Multi-Objective Optimization Algorithms
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