| _version_ | 1866902305382596608 |
|---|---|
| author | Zhang, Jincheng |
| author_facet | Zhang, Jincheng |
| contents | <p><span>Intelligent optimization algorithms are important approaches for solving complex optimization problems. In recent years, swarm intelligence optimization methods inspired by natural phenomena and physical mechanisms have been widely studied. However, the dynamical mechanisms of most algorithms are relatively simple, often mimicking only superficial features of natural phenomena and lacking in-depth modeling of physical processes. This paper proposes a novel optimization method, the Asteroidal Ion-Tail Dynamics Optimization (AITD). Inspired by the ionization tails formed during the interaction of asteroids with the solar wind, this algorithm incorporates mechanisms such as ionization energy budget and valence state adaptation, Parker spiral field line guidance, Lorentz precession and field alignment boosting coupling, tail beam coherent filament rearrangement, bow shock reflection and re-injection, composite quenching and selective reionization, and space weather pulsation scheduling. The algorithm constructs a virtual electromagnetic field and models candidate solutions as charged particles, resulting in a complex evolutionary process with dynamic charge states, energy budgets, and field guidance. This paper systematically deduces these mechanisms mathematically, presents the complete update equations and pseudocode, and analyzes them from the perspectives of complexity and theoretical convergence. This study provides a new physical dynamics framework for swarm intelligence optimization, which has strong scalability and theoretical value.</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17070887 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Asteroidal Ion-Tail Dynamics Optimization Zhang, Jincheng <p><span>Intelligent optimization algorithms are important approaches for solving complex optimization problems. In recent years, swarm intelligence optimization methods inspired by natural phenomena and physical mechanisms have been widely studied. However, the dynamical mechanisms of most algorithms are relatively simple, often mimicking only superficial features of natural phenomena and lacking in-depth modeling of physical processes. This paper proposes a novel optimization method, the Asteroidal Ion-Tail Dynamics Optimization (AITD). Inspired by the ionization tails formed during the interaction of asteroids with the solar wind, this algorithm incorporates mechanisms such as ionization energy budget and valence state adaptation, Parker spiral field line guidance, Lorentz precession and field alignment boosting coupling, tail beam coherent filament rearrangement, bow shock reflection and re-injection, composite quenching and selective reionization, and space weather pulsation scheduling. The algorithm constructs a virtual electromagnetic field and models candidate solutions as charged particles, resulting in a complex evolutionary process with dynamic charge states, energy budgets, and field guidance. This paper systematically deduces these mechanisms mathematically, presents the complete update equations and pseudocode, and analyzes them from the perspectives of complexity and theoretical convergence. This study provides a new physical dynamics framework for swarm intelligence optimization, which has strong scalability and theoretical value.</span></p> |
| title | Asteroidal Ion-Tail Dynamics Optimization |
| url | https://doi.org/10.5281/zenodo.17070887 |