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Related Experiment Video

Updated: Jan 11, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

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Published on: December 9, 2012

A hybrid evolutionary algorithm for influence maximization in complex networks using invasive weed optimization and

Linian Liu¹, Junyi Xu², Shouliang Lai³

¹Department of Multimedia Design, Graduate School of Design, Hanyang University, Seoul, 04763, Korea.

Scientific Reports

|November 11, 2025

Summary

We developed a Hybrid Weed-Gravitational Evolutionary Algorithm (HWGEA) for complex optimization tasks. This novel algorithm enhances performance in continuous and discrete optimization, including social network influence maximization.

Keywords:

Adaptive mutation Complex network optimization Discrete evolutionary algorithms Gravitational search Hybrid metaheuristic Influence maximization Invasive weed optimization

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Area of Science:

Computer Science
Artificial Intelligence
Optimization Algorithms

Background:

Social networks are vital for information spread, but optimizing their vast search spaces is difficult.
Existing optimization methods struggle with the rugged landscapes of large-scale problems.

Purpose of the Study:

To introduce a novel Hybrid Weed-Gravitational Evolutionary Algorithm (HWGEA) for robust continuous optimization.
To develop a discrete variant (DHWGEA) for efficient influence maximization in social networks.
To enhance the balance between exploration and exploitation in optimization.

Main Methods:

HWGEA unifies Invasive Weed Optimization and Gravitational Search with adaptive mutation.
DHWGEA incorporates topology-aware initialization, dynamic local search, and an Expected Influence Score surrogate.
Algorithms were tested on 23 continuous benchmarks and real-world engineering designs.

Main Results:

HWGEA achieved superior performance on continuous benchmarks, outperforming several established algorithms.
HWGEA demonstrated competitive results on engineering design problems.
DHWGEA provided a practical accuracy-efficiency trade-off for influence maximization, outperforming PageRank.

Conclusions:

HWGEA and DHWGEA offer a cohesive, scalable framework for both continuous and discrete optimization.
The adaptive components of the algorithms reduce the need for manual parameter tuning.
These algorithms provide a robust and efficient approach to complex optimization challenges in computer science and AI.