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An Adaptation Multi-Group Quasi-Affine Transformation Evolutionary Algorithm for Global Optimization and Its

Nengxian Liu1, Jeng-Shyang Pan2,3,4, Jin Wang5,6

  • 1College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China. lylnx@fzu.edu.cn.

Sensors (Basel, Switzerland)
|September 25, 2019
PubMed
Summary
This summary is machine-generated.

A new multi-group quasi-affine transformation evolutionary algorithm enhances global optimization by improving population diversity. This adaptive algorithm outperforms existing methods in benchmark tests and wireless sensor network node localization tasks.

Keywords:
differential evolutiondistance vector-hopglobal optimizationmulti-groupnode localizationquasi-affine transformation evolutionary algorithmwireless sensor networks

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Area of Science:

  • Computational Intelligence
  • Optimization Algorithms
  • Metaheuristics

Background:

  • Metaheuristic algorithms are crucial for solving complex optimization problems across various scientific domains.
  • Existing quasi-affine transformation evolutionary algorithms (QAT-EA) can be further improved for enhanced performance.

Purpose of the Study:

  • To propose a novel adaptation of the multi-group quasi-affine transformation evolutionary algorithm (MG-QAT-EA) for global optimization.
  • To enhance population diversity and adaptively manage mutation strategies for improved efficiency and exploration-exploitation balance.

Main Methods:

  • Population is randomly divided into three groups, each employing a distinct mutation strategy.
  • Adaptive scale factor (F) update policies are implemented during the search process.
  • The algorithm's performance is validated using the CEC2013 test suite and a node localization problem in wireless sensor networks (WSNs).

Main Results:

  • The proposed MG-QAT-EA demonstrated superior performance compared to three QAT-EA variants, two differential evolution variants, and two particle swarm optimization variants on the CEC2013 test suite.
  • In wireless sensor network node localization, the MG-QAT-EA achieved higher accuracy than competing algorithms.

Conclusions:

  • The proposed adaptive MG-QAT-EA effectively enhances population diversity and balances exploration-exploitation for superior global optimization.
  • The algorithm shows significant promise for applications requiring high accuracy, such as node localization in wireless sensor networks.