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Comprehensive Learning Fungal Growth Optimizer for Numerical Optimization and Reservoir Production Optimization
Mingyang Gong1, Zhenyu Song2, Xiaonan Zhang1
1School of Geophysics and Petroleum Resources, Yangtze University, Wuhan 430100, China.
Biomimetics (Basel, Switzerland)
|June 25, 2026
Summary
The Comprehensive Learning Fungal Growth Optimizer (CLFGO) enhances fungal colony simulations by improving search diversity and preventing premature convergence. This novel metaheuristic shows superior performance in complex optimization problems.
Area of Science:
- Computational Intelligence
- Optimization Algorithms
- Nature-Inspired Computing
Background:
- The Fungal Growth Optimizer (FGO) is a metaheuristic inspired by fungal colony behavior.
- FGO can suffer from reduced search diversity during its exploitation phase due to limited peer or global-best information.
- This limitation is particularly pronounced in high-dimensional and complex optimization landscapes.
Purpose of the Study:
- To introduce an enhanced variant of the FGO, termed the Comprehensive Learning Fungal Growth Optimizer (CLFGO).
- To address the diversity loss and premature convergence issues observed in the original FGO.
- To improve the performance of FGO on challenging, high-dimensional, multimodal, and composition optimization problems.
Main Methods:
- Integration of a conditionally activated Comprehensive Learning (CL) strategy into the FGO framework.
- Development of a mechanism where stagnating candidate solutions construct dimension-specific learning exemplars.
- Each dimension learns from the personal best of a different peer, extending the FGO's learning model.
Main Results:
- CLFGO demonstrated improved population diversity and reduced risk of premature convergence.
- Evaluated on 29 CEC2017 benchmark functions (30 dimensions), CLFGO achieved the lowest mean error on 21 functions.
- CLFGO obtained a superior Friedman average rank of 1.5517 compared to nine other metaheuristics.
- Application to a reservoir production optimization problem yielded a mean Net Present Value of 9.97×10^8 USD, outperforming competitors.
Conclusions:
- CLFGO effectively enhances the FGO by incorporating comprehensive learning to maintain diversity.
- The proposed algorithm is well-suited for complex, high-dimensional optimization landscapes where traditional FGO struggles.
- CLFGO shows significant potential for real-world applications, as evidenced by its success in reservoir production optimization.
Keywords:
Comprehensive Learning StrategyFungal Growth Optimizerglobal optimizationproduction optimizationMore Related Videos
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