Coronavirus Herd Immunity Optimizer (CHIO) for Transmission Expansion Planning

Amini, Shiva; Ghasemi, Sasan; Golpîra, Hêmin; Anvari‐Moghaddam, Amjad

doi:10.1109/eeeic/icpseurope51590.2021.9584540

Cited by 10 publications

(3 citation statements)

References 22 publications

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“…It is crucial to stress that determined social distancing measures may have an impact on the population's immunity rate among persons [35], considering that the freshly formed individual's immunity rate is higher than the population's average immunity rate.…”

Section: Coronavirus Herd Immunity Optimizer (Chio)mentioning

confidence: 99%

Enhancing Ultimate Bearing Capacity Assessment of Rock Foundations using a Hybrid Decision Tree Approach

Guo,

Jiang

2024

IJACSA

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Accurately estimating the ultimate bearing capacity of piles embedded in rock is of paramount importance in the domains of civil engineering, construction, and foundation design. This research introduces an innovative solution to tackle this issue, leveraging a fusion of the Decision Tree method with two state-of-the-art optimization algorithms: the Zebra Optimization Algorithm and the Coronavirus Herd Immunity Optimizer. The research approach encompassed the creation of a hybridized model, unifying the DT with the Zebra Optimization Algorithm and Coronavirus Herd Immunity Optimizer. The primary objective was to augment the precision of the ultimate bearing capacity of prediction for piles embedded in rock. This hybridization strategy harnessed the capabilities of DT along with the two pioneering optimizers to address the inherent uncertainty stemming from diverse factors impacting bearing capacity. The Zebra Optimization Algorithm and Coronavirus Herd Immunity Optimizer showcased their efficacy in refining the base model, leading to substantial enhancements in predictive performance. This study's discoveries make a significant stride in the realm of geotechnical engineering by furnishing a sturdy approach to forecasting ultimate bearing capacity in rocksocketed piles. The hybridization method is a hopeful path for future research endeavors and practical implementations. Specifically, the DT + Zebra Optimization Algorithm model yielded dependable outcomes, as evidenced by their impressive R-squared value of 0.9981 and a low Root mean squared error value of 629.78. The attained outcomes empower engineers and designers to make well-informed choices concerning structural foundations in soft soil settings. Ultimately, this research advocates for safer and more efficient construction methodologies, mitigating the hazards linked to foundation failures.

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Section: Coronavirus Herd Immunity Optimizer (Chio)mentioning

confidence: 99%

Enhancing Ultimate Bearing Capacity Assessment of Rock Foundations using a Hybrid Decision Tree Approach

Guo,

Jiang

2024

IJACSA

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“…The feature selection problem in the medical diagnosis domain has been also solved with the implementation of CHIO [21]. Moreover, CHIO algorithm has been successfully used in many areas such as high renewable penetration microgrid [22] and economic dispatch problems [23] etc.…”

Section: Introductionmentioning

confidence: 99%

Optimal Control of Automatic Voltage Regulator System with Coronavirus Herd Immunity Optimizer Algorithm-Based PID plus Second Order Derivative Controller

Emiroğlu

Gümüş

2022

Academic Platform Journal of Engineering and Smart Systems

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Optimal control of the Automatic Voltage Regulator (AVR) system can improve the system behavior with the optimal parameters obtained based on optimization. The design of proposed Proportional Integral Derivative (PID) and Proportional Integral Derivative Plus Second Order Derivative (PIDD2) controllers are stated as an optimization problem including objective and constraint. The optimization problem is solved by using Coronavirus herd immunity optimizer (CHIO) algorithm to find the best controller parameters. In this paper, optimal design of PID and PIDD2 controllers for AVR system are presented with different objectives. The optimal design of controllers modeled as an optimization problem is solved with the help of the CHIO Algorithm. CHIO is inspired herd immunity against COVID-19 disease by social distancing. The performances of CHIO-based controllers in AVR system are compared with some well-known algorithms. Also, the obtained results demonstrate that the CHIO algorithm yields the least objective value in comparison with the other algorithms. When the results of the proposed approach are compared to those of some modern heuristic optimization algorithms, such as the Particle Swarm Optimization (PSO) algorithm, Differential Evolution (DE) algorithm, Artificial Bee Colony (ABC) algorithm etc. and the superiority of the proposed approach is demonstrated.

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“…The CHIO optimization method is currently considered a state-of-the-art method and has been utilized effectively in many engineering disciplines to tackle challenging linear and nonlinear optimization problems (OPs) with remarkable accuracy and rapid convergence [23]. Several researchers have developed and used the CHIO to address optimization issues [24,25].…”

Section: Introductionmentioning

confidence: 99%

Optimal DG Integration in Power Distribution Systems Using Coronavirus Herd Immunity Optimizer

2022

IJIES

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In this study, the problem of finding an optimal location and size of a distributed generator (DG) in distribution systems with considering operational distribution system constraints is proposed with the objective of maximizing DG hosting capacity (MDHC), reducing system loss, and improving voltage stability index (VSI). The proposed objective function is formulated as a multi-objective mixed-integer nonlinear optimization in order to solve it simultaneously. To solve this problem, the coronavirus herd immunity optimizer (CHIO), a bio-inspired metaheuristic optimization method, is herein proposed to simultaneously tackle a discrete and continuous DG integration problem in distribution systems. Extensive simulations on an IEEE 69-node system with different load levels and DG numbers are performed using MATLAB software to evaluate the efficacy of the proposed method. The simulation results demonstrate that the proposed method efficiently improves overall distribution system performance when compared to different DG numbers and load levels. Furthermore, the CHIO optimization method shows encouraging results and almost obtains the best results in all proposed cases when compared with well-known metaheuristic optimization methods such as genetic algorithm (GA), the hunger games search (HGS), the chaotic neural network algorithm (CNNA), and the water cycle algorithm (WCA). The CHIO can successfully offer a notable solution for the DG integration problem, and the obtained results, for example in case 1, revealed outperforming the CHIO compared to other methods in terms of the MDHC (i.e., 99.999 %), voltage profile improvement (i.e., the minimum voltage magnitude of 0.9696 p.u), VSI improvement (i.e., 29.16 %), and system loss reduction (i.e., 66.95 %) compared with the base case, respectively.

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Coronavirus Herd Immunity Optimizer (CHIO) for Transmission Expansion Planning

Cited by 10 publications

References 22 publications

Enhancing Ultimate Bearing Capacity Assessment of Rock Foundations using a Hybrid Decision Tree Approach

Enhancing Ultimate Bearing Capacity Assessment of Rock Foundations using a Hybrid Decision Tree Approach

Optimal Control of Automatic Voltage Regulator System with Coronavirus Herd Immunity Optimizer Algorithm-Based PID plus Second Order Derivative Controller

Optimal DG Integration in Power Distribution Systems Using Coronavirus Herd Immunity Optimizer

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