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MEA-CNDP: A Membrane Evolutionary Algorithm for Solving Biobjective Critical Node Detection Problem.

Yaochang Xu1, Ping Guo1,2

  • 1College of Computer Science, Chongqing University, Chongqing 400044, China.

Computational Intelligence and Neuroscience
|December 8, 2021
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Summary
This summary is machine-generated.

This study introduces a novel algorithm, MEA-CNDP, to identify critical nodes in complex networks by addressing the biobjective critical node detection problem (bi-CNDP). The new method demonstrates superior performance, especially for large-scale sparse networks.

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Area of Science:

  • Complex network analysis
  • Optimization algorithms
  • Computational intelligence

Background:

  • The critical node detection problem (CNDP) is crucial for understanding network behavior and resilience.
  • Existing CNDP methods often treat the problem as single-objective, limiting their applicability and requiring substantial prior knowledge.
  • Addressing CNDP as a multi-objective problem offers a more comprehensive approach to identifying influential nodes.

Purpose of the Study:

  • To propose a novel biobjective optimization algorithm, MEA-CNDP, for solving the critical node detection problem.
  • To enhance the identification of critical nodes in complex networks by considering multiple objectives.
  • To provide a more flexible and less knowledge-dependent approach to CNDP.

Main Methods:

  • Development of a membrane evolution algorithm (MEA-CNDP) specifically designed for biobjective CNDP.
  • Implementation of a population initialization strategy based on decision variable evaluation.
  • Inclusion of a strategy for transforming the main objective and updating the membrane inherited pool.
  • Utilization of four distinct membrane evolutionary operators to drive the optimization process.

Main Results:

  • MEA-CNDP demonstrated superior performance compared to existing algorithms across 16 benchmark problems.
  • The algorithm showed effectiveness with both random and logarithmic weights.
  • MEA-CNDP exhibited particular strengths in handling large-scale and sparse bi-CNDP instances.

Conclusions:

  • MEA-CNDP is an effective algorithm for solving the biobjective critical node detection problem.
  • The proposed method offers advantages over traditional single-objective approaches, especially for complex network structures.
  • MEA-CNDP shows significant potential for applications involving large-scale sparse networks.