An Improved Bio-Inspired Material Generation Algorithm for Engineering Optimization Problems Including PV Source Penetration in Distribution Systems
The Material Generation Optimization (MGO) algorithm is an innovative approach inspired by material chemistry which emulates the processes of chemical compound formation and stabilization to thoroughly explore and refine the parameter space. By simulating the bonding processes—such as the formation...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/2/603 |
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| Summary: | The Material Generation Optimization (MGO) algorithm is an innovative approach inspired by material chemistry which emulates the processes of chemical compound formation and stabilization to thoroughly explore and refine the parameter space. By simulating the bonding processes—such as the formation of ionic and covalent bonds—MGO generates new solution candidates and evaluates their stability, guiding the algorithm toward convergence on optimal parameter values. To improve its search efficiency, this paper introduces an Enhanced Material Generation Optimization (IMGO) algorithm, which integrates a Quadratic Interpolated Learner Process (QILP). Unlike conventional random selection, QILP strategically selects three distinct chemical compounds, resulting in increased diversity, a more thorough exploration of the solution space, and improved resistance to local optima. The adaptable and non-linear adjustments of QILP’s quadratic function allow the algorithm to traverse complex landscapes more effectively. This innovative IMGO, along with the original MGO, is developed to support applications across three phases, showcasing its versatility and enhanced optimization capabilities. Initially, both the original and improved MGO algorithms are evaluated using several mathematical benchmarks from the CEC 2017 test suite and benchmarks to measure their optimization capabilities. Following this, both algorithms are applied to the following three well-known engineering optimization problems: the welded beam design, rolling element bearing design, and pressure vessel design. The simulation results are then compared to various established bio-inspired algorithms, including Artificial Ecosystem Optimization (AEO), Fitness–Distance-Balance AEO (FAEO), Chef-Based Optimization Algorithm (CBOA), Beluga Whale Optimization Algorithm (BWOA), Arithmetic-Trigonometric Optimization Algorithm (ATOA), and Atomic Orbital Searching Algorithm (AOSA). Moreover, MGO and IMGO are tested on a real Egyptian power distribution system to optimize the placement of PV and the capacitor units with the aim of minimizing energy losses. Lastly, the PV parameters estimation problem is successfully solved via IMGO, considering the commercial RTC France cell. Comparative studies demonstrate that the IMGO algorithm not only achieves significant energy loss reduction but also contributes to environmental sustainability by reducing emissions, showcasing its overall effectiveness in practical energy optimization applications. The IMGO algorithm improved the optimization outcomes of 23 benchmark models with an average accuracy enhancement of 65.22% and a consistency of 69.57% compared to the MGO method. Also, the application of IMGO in PV parameter estimation achieved a reduction in computational errors of 27.8% while maintaining superior optimization stability compared to alternative methods. |
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| ISSN: | 2076-3417 |