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<p>A novel metaheuristic optimization algorithm, namely the Farthest better or Nearest worse Optimizer (FNO) algorithm, is proposed in this paper. The idea behind the FNO algorithm is derived from the qualities and distances between agents’ positions in a search space. The process of searching in the FNO includes two phases. During the first phase of the FNO, it jumps over the nearest regions with lower potential to avoid local optima. In the second phase, the algorithm tries to explore the farthest positions with higher potential to reach or explore the global optimum. These operations aim to enhance population diversity and provide the FNO with opportunities to discover high-quality regions while avoiding low-quality regions. A structural component within FNO, called Dynamic Focus Strategy (DFS), is also presented for controlling the exploration ratio. The DFS applies a random vector as a coefficient to shrink the area around the farthest better positions throughout the search process. Several experimental studies have been conducted on well-known benchmark suites, comprising 45 benchmarks, to assess the efficacy of the FNO algorithm. Additionally, five engineering problems were used to evaluate the practical applicability of the proposed FNO algorithm. The Wilcoxon test, as a well-known non-parametric statistical test, is conducted to fairly compare results. The findings indicate that the FNO algorithm performs competitively against other state-of-the-art population-based metaheuristic algorithms on the tested problems.</p>

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Farthest better or nearest worse optimizer: a novel metaheuristic algorithm

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月7日

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  • Ahmad Taheri,
  • Keyvan RahimiZadeh,
  • Jan Baumbach,
  • Amin Beheshti,
  • Olga Zolotareva,
  • Mohammed Azmi Al-Betar,
  • Seyedali Mirjalili,
  • Amir H. Gandomi

摘要

A novel metaheuristic optimization algorithm, namely the Farthest better or Nearest worse Optimizer (FNO) algorithm, is proposed in this paper. The idea behind the FNO algorithm is derived from the qualities and distances between agents’ positions in a search space. The process of searching in the FNO includes two phases. During the first phase of the FNO, it jumps over the nearest regions with lower potential to avoid local optima. In the second phase, the algorithm tries to explore the farthest positions with higher potential to reach or explore the global optimum. These operations aim to enhance population diversity and provide the FNO with opportunities to discover high-quality regions while avoiding low-quality regions. A structural component within FNO, called Dynamic Focus Strategy (DFS), is also presented for controlling the exploration ratio. The DFS applies a random vector as a coefficient to shrink the area around the farthest better positions throughout the search process. Several experimental studies have been conducted on well-known benchmark suites, comprising 45 benchmarks, to assess the efficacy of the FNO algorithm. Additionally, five engineering problems were used to evaluate the practical applicability of the proposed FNO algorithm. The Wilcoxon test, as a well-known non-parametric statistical test, is conducted to fairly compare results. The findings indicate that the FNO algorithm performs competitively against other state-of-the-art population-based metaheuristic algorithms on the tested problems.