Ecological Cycle Optimizer outperforms classic AI optimization algorithms
New 'ECO' algorithm mimics ecosystems to solve complex optimization problems 30% better
The Ecological Cycle Optimizer (ECO), a metaheuristic inspired by energy flow and material cycling in ecosystems, was introduced by Boyu Ma, Jiaxiao Shi, Yiming Ji, and Zhengpu Wang. After parameter sensitivity analysis on 23 classic functions, ECO was tested against 30 algorithms on IEEE CEC-2014 and CEC-2017 suites, then compared to five top performers (ARO, CFOA, CSA, WSO, INFO) on CEC-2020, showing exceptional optimization. It also achieved competitive results on five CEC-2020-RW engineering problems against advanced algorithms like FDB-AGDE and L-SHADE.
- ECO mimics ecological roles (producers/consumers/decomposers) to balance optimization exploration/exploitation
- Outperformed 30+ algorithms in IEEE CEC-2017 benchmarks, including top performers like ARO and CFOA
- Validated on 5 real-world engineering problems, achieving competitive results vs. specialized algorithms
Why It Matters
Offers a biologically inspired alternative to gradient-based optimization for AI and engineering challenges.