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<p>The development of intelligent optimization methods has become a highly active research area in recent decades. This paper introduces a philosophy-inspired optimization algorithm called the Philosophical Proposition Optimizer (ΦPO), which models knowledge acquisition based on philosophical propositions in epistemology. In the proposed philosophical model, three developmental states for philosophical propositions, Justified True Belief (JTB), Possibly False Belief (PFB), and Unjustified True Belief (UTB), are iteratively refined using three specialized operators: Providing Justification (PJ), Raising Metaphysical Skepticism (RMS), and Raising Epistemic Skepticism (RES). To evaluate the performance of ΦPO on challenging optimization problems, it is applied to the single-objective bound-constrained benchmark problems of the IEEE Congress on Evolutionary Computation 2014 and 2024 (CEC 2014 and 2024), as well as to benchmark engineering problems. The performance of ΦPO is compared against five categories of algorithms: (1) widely used classical methods, (2) established post-2019 methods, (3) advanced PSO- and DE-based methods, (4) winners of CEC competitions, and (5) well-studied methods for solving engineering design problems. Two established non-parametric statistical methods, the Friedman test and the Wilcoxon signed-rank test, are used to analyze performance. The findings highlight the advantages of ΦPO across a range of numerical optimization problems, underscoring its competitiveness and potential in the field. Importantly, ΦPO was intentionally designed to be simple, interpretable, and parameter-free, avoiding complex adaptive strategies and extensive parameter tuning. It consistently delivers stable, high-quality solutions and exhibits fast convergence in many cases. The results demonstrate that ΦPO performs competitively across multiple benchmark suites, often ranking among the top-performing algorithms and outperforming several state-of-the-art methods, including recent CEC competition winners and engineering-specific optimizers. Its unique epistemic approach to solution refinement further enhances robustness, distinguishing it in both numerical and engineering optimization tasks.</p>

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Philosophical proposition optimizer (ΦPO): an epistemology-inspired algorithm for numerical optimization

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  • 发表日期：

    2025年11月4日

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  • Siamak Talatahari,
  • Hadi Bayazidi,
  • Pooya Sareh

摘要

The development of intelligent optimization methods has become a highly active research area in recent decades. This paper introduces a philosophy-inspired optimization algorithm called the Philosophical Proposition Optimizer (ΦPO), which models knowledge acquisition based on philosophical propositions in epistemology. In the proposed philosophical model, three developmental states for philosophical propositions, Justified True Belief (JTB), Possibly False Belief (PFB), and Unjustified True Belief (UTB), are iteratively refined using three specialized operators: Providing Justification (PJ), Raising Metaphysical Skepticism (RMS), and Raising Epistemic Skepticism (RES). To evaluate the performance of ΦPO on challenging optimization problems, it is applied to the single-objective bound-constrained benchmark problems of the IEEE Congress on Evolutionary Computation 2014 and 2024 (CEC 2014 and 2024), as well as to benchmark engineering problems. The performance of ΦPO is compared against five categories of algorithms: (1) widely used classical methods, (2) established post-2019 methods, (3) advanced PSO- and DE-based methods, (4) winners of CEC competitions, and (5) well-studied methods for solving engineering design problems. Two established non-parametric statistical methods, the Friedman test and the Wilcoxon signed-rank test, are used to analyze performance. The findings highlight the advantages of ΦPO across a range of numerical optimization problems, underscoring its competitiveness and potential in the field. Importantly, ΦPO was intentionally designed to be simple, interpretable, and parameter-free, avoiding complex adaptive strategies and extensive parameter tuning. It consistently delivers stable, high-quality solutions and exhibits fast convergence in many cases. The results demonstrate that ΦPO performs competitively across multiple benchmark suites, often ranking among the top-performing algorithms and outperforming several state-of-the-art methods, including recent CEC competition winners and engineering-specific optimizers. Its unique epistemic approach to solution refinement further enhances robustness, distinguishing it in both numerical and engineering optimization tasks.