فهرست نشریات علمی دسترسی آزاد ایران

This study proposes a new metaheuristic optimization algorithm named the Position Based Energy Thinking Method (PETM). The algorithm is conceptually inspired by two contrasting modes of thinking observed in professional chess, without attempting any formal modeling or direct mapping of chess principles. PETM draws inspiration from aggressive and dynamic thinking, commonly associated with Garry Kasparov’s playing style, and from positional and gradual thinking, associated with Anatoly Karpov’s style. These inspirations are used solely to design distinct search behaviors. In PETM, the concept of energy represents aggressive search behavior and is used to generate larger and more diverse modifications in candidate solutions, thereby improving global exploration. In contrast, positional thinking leads to conservative search behavior based on incremental refinements, which enhances local exploitation and promotes stable convergence. The algorithm adaptively selects and combines these two behaviors to maintain an effective balance between exploration and exploitation while reducing the risk of premature convergence. PETM is developed for continuous optimization problems and does not rely on any specific assumptions about the objective function structure. Its performance is evaluated using a set of standard benchmark functions, including unimodal, multimodal, and fixed dimension problems. Comparative experiments with several well known metaheuristic algorithms demonstrate that PETM achieves competitive convergence speed, accuracy, and robustness, confirming its effectiveness for solving complex numerical optimization problems.