Population interaction network in representative gravitational search algorithms: Logistic distribution leads to
Haotian Li1, Yifei Yang2, Yirui Wang3,4
1Faculty of Engineering, University of Toyama, Toyama-shi, 930-8555, Japan.
Heliyon
|June 3, 2024
Summary
Population interaction networks reveal how meta-heuristic algorithms (MHAs) share information. Powerful algorithms like differential evolution (DE) lean towards Poisson distribution, while weaker ones like particle swarm optimization (PSO) and gravitational search algorithm (GSA) favor Logistic distribution.
Area of Science:
- Computational Intelligence
- Optimization Algorithms
- Network Science
Background:
- Meta-heuristic algorithms (MHAs) are widely used for complex optimization problems.
- Understanding inter-individual information exchange within MHAs is crucial for performance analysis.
- Population Interaction Networks (PIN) offer a novel framework to visualize and analyze these interactions.
Purpose of the Study:
- To investigate the relationship between inter-individual information interaction patterns and the performance of various MHAs.
- To analyze the information dynamics of representative MHAs, including differential evolutionary algorithm (DE), particle swarm optimization (PSO), and gravitational search algorithm (GSA) and its variants.
- To evaluate the effectiveness of Population Interaction Networks (PIN) in characterizing and comparing MHA performance.
Main Methods:
- Utilized Population Interaction Networks (PIN) to model information flow among individuals in MHAs.
- Analyzed seven MHAs, including DE, PSO, GSA, and four GSA variants, on IEEE Congress on Evolutionary Computation 2017 benchmark functions.
- Fitted the cumulative distribution function (CDF) of node degrees from PIN to seven distribution models.
Main Results:
- Differential evolutionary algorithm (DE) exhibited a stronger skew towards the Poisson distribution.
- Particle swarm optimization (PSO), gravitational search algorithm (GSA), and its variants showed a skew towards the Logistic distribution.
- Greater deviation from the Logistic distribution correlated with improved performance in GSA variants, indicating its benefit for GSA enhancement.
Conclusions:
- Population Interaction Networks (PIN) provide a powerful method for characterizing MHA information dynamics.
- The study demonstrates a link between distribution patterns of node degrees in PIN and MHA performance.
- Deviating from the Logistic distribution is identified as a key factor for improving GSA performance.
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