| _version_ | 1866901680949297152 |
|---|---|
| author | Zhang, Jincheng |
| author_facet | Zhang, Jincheng |
| contents | <p><span>Ecosystem modeling is a crucial method for understanding the structure, function, and dynamic changes of ecosystems. This paper proposes a novel heuristic optimization algorithm based on ecosystem modeling—Ecosystem Interaction Optimization (EIO). Its core ideas stem from the multi-level interactions among species, resource gradient regulation, material cycling feedback, and dynamic niche adjustment within ecosystems. The algorithm simulates the interactions between high-level predators, mid-level competitors, and low-level producers to achieve a dynamic balance between global search and local optimization, and introduces resource gradient and energy flow mechanisms to enhance search adaptability. The paper details the algorithm's mathematical formulas, update mechanism, and overall process, providing a theoretical foundation and methodological support for solving subsequent optimization problems.</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17558064 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Ecosystem Interaction Optimization: A Heuristic Algorithm Inspired by Ecosystem Modeling Zhang, Jincheng <p><span>Ecosystem modeling is a crucial method for understanding the structure, function, and dynamic changes of ecosystems. This paper proposes a novel heuristic optimization algorithm based on ecosystem modeling—Ecosystem Interaction Optimization (EIO). Its core ideas stem from the multi-level interactions among species, resource gradient regulation, material cycling feedback, and dynamic niche adjustment within ecosystems. The algorithm simulates the interactions between high-level predators, mid-level competitors, and low-level producers to achieve a dynamic balance between global search and local optimization, and introduces resource gradient and energy flow mechanisms to enhance search adaptability. The paper details the algorithm's mathematical formulas, update mechanism, and overall process, providing a theoretical foundation and methodological support for solving subsequent optimization problems.</span></p> |
| title | Ecosystem Interaction Optimization: A Heuristic Algorithm Inspired by Ecosystem Modeling |
| url | https://doi.org/10.5281/zenodo.17558064 |