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Target search optimization by threshold resetting

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  • Arup Biswas
  • Satya N Majumdar
  • Arnab Pal

Abstract

We introduce a new class of first passage time optimization driven by threshold resetting, inspired by many natural processes where crossing a critical limit triggers failure, degradation or transition. In here, search agents are collectively reset when a threshold is reached, creating event-driven, system-coupled simultaneous resets that induce long-range interactions. We develop a unified framework to compute search times for these correlated stochastic processes, with ballistic searchers as a key example uncovering diverse optimization behaviors. A cost function, akin to breakdown penalties, reveals that optimal resetting can forestall larger losses. This formalism generalizes to broader stochastic systems with multiple degrees of freedom.

Suggested Citation

  • Arup Biswas & Satya N Majumdar & Arnab Pal, 2025. "Target search optimization by threshold resetting," Papers 2504.13501, arXiv.org.
    • Handle: RePEc:arx:papers:2504.13501
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    File URL: http://arxiv.org/pdf/2504.13501
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