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The Role of Bio-Inspired Optimization Algorithms in Medical Feature Selection and Energy Consumption

The Role of Bio-Inspired Optimization Algorithms in Medical Feature Selection and Energy Consumption

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The proliferation of high-dimensional data in healthcare and the increasing demand for intelligent energy management in smart homes have necessitated the development of advanced optimization techniques. Bio-inspired optimization algorithms—drawing inspiration from natural behaviors such as hunting, migration, and evolution—have demonstrated remarkable success in solving complex real-world problems. These algorithms are particularly effective in feature selection for medical data and in planning energy usage in renewable-powered smart environments.

 

Dr. Zenab M. Elgamal has made significant contributions to advancing this field by proposing improved variants of Harris Hawks Optimization (HHO), Equilibrium Optimization (EO), and Reptile Search Optimization (RSO), as well as applying a multi-objective Grey Wolf Optimizer (GWO) to energy consumption problems. Her research emphasizes hybridization techniques, including simulated annealing, chaotic maps, and elite opposition-based learning, to enhance optimization performance.

Key Algorithms and Applications

1. Harris Hawks Optimization with Simulated Annealing
The improved HHO integrates a simulated annealing mechanism to enhance global search capabilities and avoid local optima. This hybrid model excels in selecting minimal yet highly relevant features from complex medical datasets, thereby improving disease classification models and reducing computational overhead [1].

2. Equilibrium Optimization with Elite Opposition-Based Learning
This improved EO algorithm utilizes elite opposition-based learning and a novel local search strategy to maintain population diversity and accelerate convergence. It has shown excellent performance in medical feature selection by selecting optimal subsets of clinical and genetic features [2].

3. Reptile Search Optimization with Chaotic Map and Simulated Annealing
The improved RSO algorithm incorporates chaotic maps to introduce randomness and simulated annealing to maintain global exploration, thereby enhancing its ability to find globally optimal feature subsets. This approach is especially effective in biomedical classification tasks such as cancer and diabetes diagnosis [3].

4. Multi-objective Grey Wolf Optimizer for Energy Consumption
Expanding beyond healthcare, Dr. Elgamal collaborated on the development of a multi-objective GWO algorithm tailored for energy consumption in smart homes powered by renewable energy systems. The algorithm optimally schedules household appliances based on criteria such as energy cost, user comfort, and carbon emissions. By mimicking the leadership hierarchy and hunting behavior of grey wolves, the algorithm efficiently explores the trade-offs between multiple objectives, contributing to the sustainability of smart grids [4].

Real-World Impact

Medical Diagnosis and Treatment
The optimized algorithms significantly improve the performance of predictive models by selecting the most informative features from vast medical datasets. Applications include early cancer detection, heart disease prediction, and genetic biomarker identification.

Smart Healthcare Systems
Feature selection using these algorithms aids in developing intelligent, interpretable systems for patient monitoring and personalized medicine, which is essential in Internet of Medical Things (IoMT) environments.

Energy Efficiency in Smart Homes
The GWO-based approach supports the integration of renewable energy sources such as solar and wind, ensuring cost-effective and eco-friendly energy consumption. It also adapts dynamically to real-time demand and energy pricing.

Conclusion

Bio-inspired optimization algorithms are proving essential for addressing high-dimensional problems in both the medical and energy domains. Dr. Zenab Elgamal’s research showcases how hybridized evolutionary strategies can be tailored to diverse applications, from enhancing diagnostic accuracy to optimizing renewable energy usage. These contributions not only improve system performance but also align with global goals in health and sustainability. As data complexity increases and environmental concerns rise, such algorithms will continue to play a critical role in shaping future technologies.

References
[1] Elgamal, Z.M., Yasin, N.B.M., Tubishat, M., et al. (2020). An Improved Harris Hawks Optimization Algorithm with Simulated Annealing for Feature Selection in the Medical Field. IEEE Access, 8, 186638–186652.
[2] Elgamal, Z.M., Yasin, N.M., Sabri, A.Q.M., et al. (2021). Improved Equilibrium Optimization Algorithm Using Elite Opposition-Based Learning and New Local Search Strategy for Feature Selection in Medical Datasets. Computation, 9(6), 68.
[3] Elgamal, Z., Sabri, A.Q.M., Tubishat, M., et al. (2022). Improved Reptile Search Optimization Algorithm Using Chaotic Map and Simulated Annealing for Feature Selection in Medical Field. IEEE Access, 10, 51428–51446.
[4] Makhadmeh, S.N., Al-Betar, M.A., Al-Obeidat, F., Elgamal, Z.M., et al. (2024). A Multi-Objective Grey Wolf Optimizer for Energy Planning Problem in Smart Home Using Renewable Energy Systems. Sustainable Operations and Computers, 5, 88–101.

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