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A Bilevel Gene-Based Multiobjective Memetic Algorithm for Passive Localization System Deployment Optimization

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.

Ketersediaan

Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION; Vol.27 No.2 April 2023

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 355-369

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

MEMETIC ALGORITHM
MULTIOBJECTIVE EVOLUTIONARY ALGORITHM (MOEA)
GEOMETRIC DILUTION OF PRECISION (GDOP)
PASSIVE LOCALIZATION SYSTEM (PLS)

Info Detail Spesifik

DOI: 10.1109/TEVC.2022.3168427

Pernyataan Tanggungjawab

Zhao Wang, Maoguo Gong, Peng Li, Fei Xie, Mingyang Zhang

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