Hybrid multi-objective evolutionary algorithms based on decomposition for wireless sensor network coverage optimization
- Submitting institution
-
University of Nottingham, The
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1334170
- Type
- D - Journal article
- DOI
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10.1016/j.asoc.2018.03.053
- Title of journal
- Applied Soft Computing
- Article number
- -
- First page
- 268
- Volume
- 68
- Issue
- -
- ISSN
- 1568-4946
- Open access status
- Compliant
- Month of publication
- April
- Year of publication
- 2018
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
-
- Citation count
- 27
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Due to increasing demands on telecommunications, efficient and effective network resource optimisation presents a new challenge for both communications and optimisation research. With a novel hybrid algorithm based on effective evolutionary algorithms, highly competitive results described in this work demonstrate significant potential for the deployment in real-world applications. This cross-disciplinary research has been adapted by researchers in various other research areas, ranging from communications, crowd-sensing, edge computing, evolutionary computation, logistic dispatching, to wireless mobile tracking, etc.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
