MOCHIO: a novel Multi-Objective Coronavirus Herd Immunity Optimization algorithm for solving brushless direct current wheel motor design optimization problem

Kumar, C.; Mary, D. Magdalin; T, Gunasekar

doi:10.1080/00051144.2021.2014035

Cited by 12 publications

(2 citation statements)

References 43 publications

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“…It is very simple and easy-to-use as a black-box. Recently, CHIO has been successfully applied for solving several problems such as traveling salesman problems [44] and wheel motor design [45].…”

Section: Introductionmentioning

confidence: 99%

Archive-based coronavirus herd immunity algorithm for optimizing weights in neural networks

Doush

Awadallah

Al-Betar

et al. 2023

Neural Comput & Applic

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The success of the supervised learning process for feedforward neural networks, especially multilayer perceptron neural network (MLP), depends on the suitable configuration of its controlling parameters (i.e., weights and biases). Normally, the gradient descent method is used to find the optimal values of weights and biases. The gradient descent method suffers from the local optimal trap and slow convergence. Therefore, stochastic approximation methods such as metaheuristics are invited. Coronavirus herd immunity optimizer (CHIO) is a recent metaheuristic human-based algorithm stemmed from the herd immunity mechanism as a way to treat the spread of the coronavirus pandemic. In this paper, an external archive strategy is proposed and applied to direct the population closer to more promising search regions. The external archive is implemented during the algorithm evolution, and it saves the best solutions to be used later. This enhanced version of CHIO is called ACHIO. The algorithm is utilized in the training process of MLP to find its optimal controlling parameters thus empowering their classification accuracy. The proposed approach is evaluated using 15 classification datasets with classes ranging between 2 to 10. The performance of ACHIO is compared against six well-known swarm intelligence algorithms and the original CHIO in terms of classification accuracy. Interestingly, ACHIO is able to produce accurate results that excel other comparative methods in ten out of the fifteen classification datasets and very competitive results for others.

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Section: Introductionmentioning

confidence: 99%

Archive-based coronavirus herd immunity algorithm for optimizing weights in neural networks

Doush

Awadallah

Al-Betar

et al. 2023

Neural Comput & Applic

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“…Various bioinspired algorithms, including swarm intelligence and evolutionary algorithms, have been used in various optimization problems involving engineering problems (Houssein et al , 2021; Premkumar et al , 2021) and electromagnetic devices (Ayala et al , 2016; Di Barba, 2010; Di Barba et al , 2018, 2022; Kumar et al , 2022).…”

Section: Introductionmentioning

confidence: 99%

Crowding-based multi-objective artificial gorilla troops optimizer for brushless direct current motor design optimization

Bensoltane,

Belli

2023

COMPEL

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Purpose This paper aims to present a novel multi-objective version of the Gorilla Troops optimizer (GTO), based on crowding distance, to achieve the optimal design of a brushless direct current motor. Design/methodology/approach In the proposed algorithm, the crowding distance technique was integrated into the GTO to perform the leader selection and also for the external archive refinement from extra non-dominated solutions. Furthermore, with a view to improving the diversity of non-dominated solutions in the external archive, mutation operator was used. For constrained problems, an efficient strategy was adopted. The proposed algorithm is referred to as CD-MOGTO. Findings To validate the effectiveness of the proposed approach, it was initially tested on three constrained multi-objective problems; thereafter, it was applied to optimize the design variables of brushless direct current motor to concurrently fulfill six inequality constraints, maximize efficiency and minimize total mass. Originality/value The results revealed the high potential of the proposed algorithm over different recognized algorithms in solving constrained multi-objective issues and the brushless direct current motors.

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Optimal Choice of Parameters for Coronavirus Herd Immunity Optimizer

Liu,

Qi,

et al. 2024

Lecture Notes in Electrical Engineering

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MOCHIO: a novel Multi-Objective Coronavirus Herd Immunity Optimization algorithm for solving brushless direct current wheel motor design optimization problem

Cited by 12 publications

References 43 publications

Archive-based coronavirus herd immunity algorithm for optimizing weights in neural networks

Archive-based coronavirus herd immunity algorithm for optimizing weights in neural networks

Crowding-based multi-objective artificial gorilla troops optimizer for brushless direct current motor design optimization

Optimal Choice of Parameters for Coronavirus Herd Immunity Optimizer

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