A dissipative particle swarm optimization
Xiao-Feng Xie, Wen-Jun Zhang, Zhi-Lian Yang
TL;DR
This paper introduces a dissipative particle swarm optimization method inspired by self-organization principles, which enhances optimization performance by maintaining a far-from-equilibrium state through negative entropy.
Contribution
It presents a novel dissipative PSO algorithm that leverages dissipative structures and negative entropy to improve optimization efficiency.
Findings
Improved performance on multimodal functions
Effective in driving irreversible evolution
Enhances convergence behavior
Abstract
A dissipative particle swarm optimization is developed according to the self-organization of dissipative structure. The negative entropy is introduced to construct an opening dissipative system that is far-from-equilibrium so as to driving the irreversible evolution process with better fitness. The testing of two multimodal functions indicates it improves the performance effectively
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMetaheuristic Optimization Algorithms Research · Evolutionary Algorithms and Applications · Advanced Algorithms and Applications