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<p>There are various optimization methods that can be used for inspiration. This study aims to present a novel algorithm inspired by rock climbing. The RCO algorithm, by intelligently mimicking human strategies in the sport of rock climbing, establishes a dynamic and situation-aware balance between global and local search. Rock climbing athletes attempt to reach their destination through the shortest route. In addition to considering trial and error as well as looking at the peak, they pay attention to the climbing speed. The optimization method and route change at the end as well as equations are provided for each route to accelerate the search operation at the end of the route. These characteristics make it a strong candidate for solving complex, multi-peak optimization problems. Also, 23 commonly used unimodal and multimodal functions, CEC2017, CEC 2019, pressure vessel, welded beam, and 10&#xa0;bar truss are employed to evaluate the efficiency of this method. The results indicate the proposed method provides solutions that can compete with other techniques and have a better convergence rate. RCO is in CEC2017 in third and in CEC 2019 in first Friedman mean rank.</p>

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A Novel Optimization Algorithm: Rock Climbing

  • 期刊论文

  • 发表日期：

    2026年2月5日

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  • Mohammad Yassami,
  • Payam Ashtari

摘要

There are various optimization methods that can be used for inspiration. This study aims to present a novel algorithm inspired by rock climbing. The RCO algorithm, by intelligently mimicking human strategies in the sport of rock climbing, establishes a dynamic and situation-aware balance between global and local search. Rock climbing athletes attempt to reach their destination through the shortest route. In addition to considering trial and error as well as looking at the peak, they pay attention to the climbing speed. The optimization method and route change at the end as well as equations are provided for each route to accelerate the search operation at the end of the route. These characteristics make it a strong candidate for solving complex, multi-peak optimization problems. Also, 23 commonly used unimodal and multimodal functions, CEC2017, CEC 2019, pressure vessel, welded beam, and 10 bar truss are employed to evaluate the efficiency of this method. The results indicate the proposed method provides solutions that can compete with other techniques and have a better convergence rate. RCO is in CEC2017 in third and in CEC 2019 in first Friedman mean rank.