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Mixed-Variable Correlation-Aware Metaheuristic for Deployment Optimization of 3-D Sensor Networks

Published: 14 July 2024 Publication History

Abstract

Deployment optimization of 3-D sensor networks is essential for the overall cost of the system and the downstream tasks performance. The key of establishing realistic deployment is twofold: a high-fidelity mathematical programming model and an efficient algorithm for solving it. In this paper, we revisit the 3-D sensor networks deployment and present a mixed-variable optimization problem (MVOP) which jointly considers the discrete subset selection decision, continuous orientation decision, and decisionmaking under uncertainty. Based on the proposed real-world application, we innovatively design a mixed-variable correlation-aware genetic algorithm as the solver. Different from mainstream two-partition methods in MVOP, our algorithm captures the problem-specific features of deployment optimization and introduces a correlation-aware search paradigm which interactively updates the discrete and continuous decision variables. On the one hand, we update the discrete part (i.e., subset selection of candidate locations) first and then optimize the continuous part (i.e., sensor orientation parameters). On the other hand, we customize a heuristic mechanism to start with continuous part to identify the suitable discrete part. Experiments demonstrate that our approach can improve the performance of small-scale and large-scale scenarios of deployment by up to 55.7% and 56.4%, respectively, compared to state-of-the-art MVOP algorithms.

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  • (2024)Deep Reinforcement Learning for Multi-Period Facility Location pk-median Dynamic Location ProblemProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691249(173-183)Online publication date: 29-Oct-2024

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  1. Mixed-Variable Correlation-Aware Metaheuristic for Deployment Optimization of 3-D Sensor Networks

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      cover image ACM Conferences
      GECCO '24: Proceedings of the Genetic and Evolutionary Computation Conference
      July 2024
      1657 pages
      ISBN:9798400704949
      DOI:10.1145/3638529
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      Publication History

      Published: 14 July 2024

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      Author Tags

      1. deployment
      2. sensor networks
      3. metaheuristics
      4. mixed-variable optimization
      5. correlation-aware

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      • Research-article

      Funding Sources

      • National Natural Science Foundations of China
      • Shanghai Municipal Science and Technology Major Project, China
      • Shanghai Municipal Commission of Science and Technology Project, China
      • Young Elite Scientist Sponsorship Program by China Association for Science and Technology
      • Youth Talent Program Supported by China Railway Society

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      GECCO '24
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      GECCO '24: Genetic and Evolutionary Computation Conference
      July 14 - 18, 2024
      VIC, Melbourne, Australia

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      Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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      • (2024)Deep Reinforcement Learning for Multi-Period Facility Location pk-median Dynamic Location ProblemProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691249(173-183)Online publication date: 29-Oct-2024

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