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A Bilevel Gene-Based Multiobjective Memetic Algorithm for Passive Localization System Deployment Optimization
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AI
DOI：10.1109/TEVC.2022.3168427.png)
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Abstract
En 中文
The passive localization system (PLS) is fundamental to many wireless applications. The deployment of the monitoring stations plays a key role in the performance of the PLSes. However, the workflow of the emerging cutting-edge PLSes is becoming more flexible in the complicated environment, which makes it hard to optimize the deployment. To fulfill the requirement of the real-world applications, we propose a multiobjective PLS deployment optimization model, including a surrogate geometric dilution of precision (S-GDOP) model and a system coverage indicator to meet the demand for the detection performance of the known and unknown targets. The proposed S-GDOP is separable and open to various performance-related factors in this article. Motivated by the various cooperation mechanisms and the empirical deployment patterns, we propose a bilevel gene-based multiobjective memetic algorithm within the decomposition framework to solve this problem. By maintaining an adaptive multicomponent gene population (MCGP) and a local pivot (LP)-based local search, the population evolves on two precise and consecutive gene levels, which effectively utilizes the problem and evolution-related heuristic information. The proposed algorithm outperforms another four popular algorithms in 83.3% bilateral comparisons and obtains more implicit deployment patterns, clearer deployment structures, and better converged Pareto fronts.
Keywords:
Location awareness
Optimization
Memetics
Statistics
Sociology
Measurement uncertainty
Heuristic algorithms
Geometric dilution of precision (GDOP)
memetic algorithm
Index Terms
multiobjective evolutionary algorithm (MOEA)
passive localization system (PLS)
Journal
IF:
12
Papers:
1.8K
Citations:
2.4W