Using Variable Interaction Graphs to Improve Particle Swarm Optimization

• URL: http://arxiv.org/abs/2509.06985v1
• Date: Tue, 02 Sep 2025 00:39:46 GMT
• Title: Using Variable Interaction Graphs to Improve Particle Swarm Optimization
• Abstract summary: This paper presents an adaptation to Particle Swarm Optimization (PSO) that dynamically learns and exploits variable interactions during the optimization process.<n>VIGPSO learns how variables influence each other by analyzing how particles move through the search space, and uses these learned relationships to guide future particle movements.<n>VIGPSO was evaluated against standard PSO on eight benchmark functions (three separable, two partially separable, and three non-separable) across 10, 30, 50 and 1000 dimensions.
• Score: 4.5835414225547195
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: This paper presents Variable Interaction Graph Particle Swarm Optimization (VIGPSO), an adaptation to Particle Swarm Optimization (PSO) that dynamically learns and exploits variable interactions during the optimization process. PSO is widely used for real-valued optimization problems but faces challenges in high-dimensional search spaces. While Variable Interaction Graphs (VIGs) have proven effective for optimization algorithms operating with known problem structure, their application to black-box optimization remains limited. VIGPSO learns how variables influence each other by analyzing how particles move through the search space, and uses these learned relationships to guide future particle movements. VIGPSO was evaluated against standard PSO on eight benchmark functions (three separable, two partially separable, and three non-separable) across 10, 30, 50 and 1000 dimensions. VIGPSO achieved statistically significant improvements ($p<0.05$) over the standard PSO algorithm in 28 out of 32 test configurations, with particularly strong performance extending to the 1000-dimensional case. The algorithm showed increasing effectiveness with dimensionality, though at the cost of higher variance in some test cases. These results suggest that dynamic VIG learning can bridge the gap between black-box and gray-box optimization effectively in PSO, particularly for high-dimensional problems.

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