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Swarm Optimisation Update Rule Analyzed as Markov Chain

By PulseAugur Editorial · [1 sources] ·

Researchers have analyzed the Simplified Swarm Optimisation (SSO) update rule, modeling it as a finite Markov chain. Their analysis reveals explicit stationary laws and convergence rates for SSO under various conditions, including frozen memory and elitist chains. The study found that SSO, particularly with an analytical redraw rule, can outperform other evolutionary algorithms like the (1+1) evolutionary algorithm on specific benchmark problems such as OneMax and LeadingOnes, though consistent advantages over rate-adjusted mutation or genetic algorithms were not established. AI

IMPACT Provides theoretical insights into swarm optimization algorithms, potentially informing future AI agent designs.

RANK_REASON Academic paper analyzing an optimization algorithm. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.NE (Neural & Evolutionary) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Swarm Optimisation Update Rule Analyzed as Markov Chain

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Academic paper analyzing an optimization algorithm. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Wei-Chang Yeh ·

    A Finite Markov Chain Analysis of the Simplified Swarm Optimisation Update Rule

    Simplified swarm optimisation (SSO) updates each variable by one draw that copies the global or personal best, keeps the current value or redraws uniformly, with probabilities $c_g$, $c_p$, $c_w$ and $c_r$. As a finite Markov chain under frozen memory, its coordinate kernel $c_w …

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