Two‐Stage Hybrid Optimization Algorithm for Silicon Single Crystal Batch Scheduling Problem under Fuzzy Processing Time

Kang, Lu; Liu, Ding; Wu, Yali; Ping, Guozheng

doi:10.1155/2023/3816574

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…A few examples of such single algorithms include Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Genetic Algorithm (GA), Symbiotic Organisms Search (SOS) and Smell Agent Optimization (SAO) techniques [ 19 ]. Some hybridized algorithms include the Ant Colony Optimization and Genetic Algorithm (ACO-GA) [ 21 ]; Particle Swarm Optimizer and Cuckoo Search (PSO-CS) [ 22 ]; Ant Colony Optimization and Artificial Bee Colony (ACO-ABC) [ 23 ]; Seagull Optimization and Thermal Exchange (SO-TE) algorithm [ 24 ]; Improved Firefly and Symbiosis Organism Search (IF-SOS) algorithm [ 25 ]; Teaching-Learning Based Optimizer and Equilibrium Optimizer (TLBO-EO) algorithm [ 26 ]; Improved Differential Evolutionary and Neighborhood Variable Search (IDE-NVS) algorithm [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

“…The algorithm optimizes the allocation in different batches of scheduled tasks by minimizing the maximum completion time. When compared with the single-stage optimizers, the IDE-NVS showed a better performance [ 27 ]. Thus, the techniques mimic the static and dynamic control optimization characteristics, which regenerate multiple solutions to solve a complex problem in an optimized manner [ 35 ].…”

Section: Introductionmentioning

confidence: 99%

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A hybrid smell agent symbiosis organism search algorithm for optimal control of microgrid operations

Mohammed¹,

Sha’aban

Umoh

et al. 2023

PLoS ONE

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This paper presents a hybrid Smell Agent Symbiosis Organism Search Algorithm (SASOS) for optimal control of autonomous microgrids. In microgrid operation, a single optimization algorithm often lacks the required balance between accuracy and speed to control power system parameters such as frequency and voltage effectively. The hybrid algorithm reduces the imbalance between exploitation and exploration and increases the effectiveness of control optimization in microgrids. To achieve this, various energy resource models were coordinated into a single model for optimal energy generation and distribution to loads. The optimization problem was formulated based on the network power flow and the discrete-time sampling of the constrained control parameters. The development of SASOS comprises components of Symbiotic Organism Search (SOS) and Smell Agent Optimization (SAO) codified in an optimization loop. Twenty-four standard test function benchmarks were used to evaluate the performance of the algorithm developed. The experimental analysis revealed that SASOS obtained 58.82% of the Desired Convergence Goal (DCG) in 17 of the benchmark functions. SASOS was implemented in the Microgrid Central Controller (MCC) and benchmarked alongside standard SOS and SAO optimization control strategies. The MATLAB/Simulink simulation results of the microgrid load disturbance rejection showed the viability of SASOS with an improved reduction in Total Harmonic Distortion (THD) of 19.76%, compared to the SOS, SAO, and MCC methods that have a THD reduction of 15.60%, 12.74%, and 6.04%, respectively, over the THD benchmark. Based on the results obtained, it can be concluded that SASOS demonstrates superior performance compared to other methods. This finding suggests that SASOS is a promising solution for enhancing the control system of autonomous microgrids. It was also shown to apply to other sectors of engineering optimization.

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