Ecological Cycle Optimizer: A novel nature-inspired metaheuristic algorithm for global optimization
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909757470670848 |
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| author | Ma, Boyu Shi, Jiaxiao Ji, Yiming Wang, Zhengpu |
| author_facet | Ma, Boyu Shi, Jiaxiao Ji, Yiming Wang, Zhengpu |
| contents | This article proposes the Ecological Cycle Optimizer (ECO), a novel metaheuristic algorithm inspired by energy flow and material cycling in ecosystems. ECO draws an analogy between the dynamic process of solving optimization problems and ecological cycling. Unique update strategies are designed for the producer, consumer and decomposer, aiming to enhance the balance between exploration and exploitation processes. Through these strategies, ECO is able to achieve the global optimum, simulating the evolution of an ecological system toward its optimal state of stability and balance. Moreover, the performance of ECO is evaluated against five highly cited algorithms-CS, HS, PSO, GWO, and WOA-on 23 classical unconstrained optimization problems and 24 constrained optimization problems from IEEE CEC-2006 test suite, verifying its effectiveness in addressing various global optimization tasks. Furthermore, 50 recently developed metaheuristic algorithms are selected to form the algorithm pool, and comprehensive experiments are conducted on IEEE CEC-2014 and CEC-2017 test suites. Among these, five top-performing algorithms, namely ARO, CFOA, CSA, WSO, and INFO, are chosen for an in-depth comparison with the ECO on the IEEE CEC-2020 test suite, verifying the ECO's exceptional optimization performance. Finally, in order to validate the practical applicability of ECO in complex real-world problems, five state-of-the-art algorithms, including NSM-SFS, FDB-SFS, FDB-AGDE, L-SHADE, and LRFDB-COA, along with four best-performing algorithms from the "CEC2020 competition on real-world single objective constrained optimization", namely SASS, sCMAgES, EnMODE, and COLSHADE, are selected for comparative experiments on five engineering problems from CEC-2020-RW test suite (real-world engineering problems), demonstrating that ECO achieves performance comparable to those of advanced algorithms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_20458 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Ecological Cycle Optimizer: A novel nature-inspired metaheuristic algorithm for global optimization Ma, Boyu Shi, Jiaxiao Ji, Yiming Wang, Zhengpu Neural and Evolutionary Computing This article proposes the Ecological Cycle Optimizer (ECO), a novel metaheuristic algorithm inspired by energy flow and material cycling in ecosystems. ECO draws an analogy between the dynamic process of solving optimization problems and ecological cycling. Unique update strategies are designed for the producer, consumer and decomposer, aiming to enhance the balance between exploration and exploitation processes. Through these strategies, ECO is able to achieve the global optimum, simulating the evolution of an ecological system toward its optimal state of stability and balance. Moreover, the performance of ECO is evaluated against five highly cited algorithms-CS, HS, PSO, GWO, and WOA-on 23 classical unconstrained optimization problems and 24 constrained optimization problems from IEEE CEC-2006 test suite, verifying its effectiveness in addressing various global optimization tasks. Furthermore, 50 recently developed metaheuristic algorithms are selected to form the algorithm pool, and comprehensive experiments are conducted on IEEE CEC-2014 and CEC-2017 test suites. Among these, five top-performing algorithms, namely ARO, CFOA, CSA, WSO, and INFO, are chosen for an in-depth comparison with the ECO on the IEEE CEC-2020 test suite, verifying the ECO's exceptional optimization performance. Finally, in order to validate the practical applicability of ECO in complex real-world problems, five state-of-the-art algorithms, including NSM-SFS, FDB-SFS, FDB-AGDE, L-SHADE, and LRFDB-COA, along with four best-performing algorithms from the "CEC2020 competition on real-world single objective constrained optimization", namely SASS, sCMAgES, EnMODE, and COLSHADE, are selected for comparative experiments on five engineering problems from CEC-2020-RW test suite (real-world engineering problems), demonstrating that ECO achieves performance comparable to those of advanced algorithms. |
| title | Ecological Cycle Optimizer: A novel nature-inspired metaheuristic algorithm for global optimization |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2508.20458 |