Modified teaching-learning-based optimization and applications in multi-response machining processes

Ang, Koon Meng; Natarajan, Elango; Isa, Nor Ashidi Mat; Sharma, Abhishek; Rahman, Hameedur; THEN, Richie Yi Shiun; Alrifaey, Moath; Tiang, Sew Sun; Lim, Wei Hong

doi:10.1016/j.cie.2022.108719

Cited by 10 publications

(6 citation statements)

References 66 publications

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“…1, MCDOL-AOA continues this iterative search, following Eqs. ( 8) to (11), until pre-set termination criteria are fulfilled. Upon completion, optimal decision variables in the best solution are decoded to solve the specific optimization problems.…”

Section: Iterative Search Processes Of Mcdol-aoamentioning

confidence: 99%

“…Compared to traditional methods, MSAs offer several advantages, including potent global search capabilities, straightforward implementation, and enhanced scalability. They exploit the unique strengths of their respective inspirations, enabling them to effectively address a variety of complex optimization challenges as outlined in [4], [5], [6], [7], [8], [9], [10], [11], [12], [13].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing Global Optimization Performance of Arithmetic Optimization Algorithm with a Modified Population Initialization Scheme

Ting,

Rahman,

Chiong

et al. 2024

Proceedings of International Conference on Artificial Life and Robotics

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Arithmetic Optimization Algorithm (AOA) is widely used to solve global optimization problems. However, it often faces premature convergence challenges in complex optimization scenarios. A key factor affecting AOA's performance is the solution quality of the initial population. The conventional initialization scheme, despite its prevalence, lacks reliability in ensuring high-quality solutions due to inherent stochastic processes. To address this issue, we propose a modified initialization scheme that improves initial population quality by integrating chaotic maps and oppositional-based learning. Through extensive simulation studies, we demonstrate that the enhanced AOA, equipped with this new initialization scheme, exhibits superior performance in solving a range of benchmark functions with improved accuracy.

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Section: Iterative Search Processes Of Mcdol-aoamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhancing Global Optimization Performance of Arithmetic Optimization Algorithm with a Modified Population Initialization Scheme

Ting,

Rahman,

Chiong

et al. 2024

Proceedings of International Conference on Artificial Life and Robotics

View full text Add to dashboard Cite

show abstract

“…In contrast, MSA-based approaches present a promising solution by integrating natureinspired search operators, facilitating the discovery of optimal network architectures without the need for specialized domain expertise. These methods, including particle swarm optimization (PSO), grey wolf optimization (GWO), teaching-learning-based optimization (TLBO), and differential evolution (DE), exhibit robust global search capabilities and find extensive application across various domains [21][22][23][24]. Due to their appealing features, MSA-based techniques have emerged as popular alternatives to conventional design methods, offering researchers a versatile tool to effectively address a wide array of deep learning challenges.…”

Section: Recent Progress In Network Architecture Design Techniquesmentioning

confidence: 99%

MTLBORKS-CNN: An Innovative Approach for Automated Convolutional Neural Network Design for Image Classification

Ang,

Lim,

Tiang

et al. 2023

Mathematics

Self Cite

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Convolutional neural networks (CNNs) have excelled in artificial intelligence, particularly in image-related tasks such as classification and object recognition. However, manually designing CNN architectures demands significant domain expertise and involves time-consuming trial-and-error processes, along with substantial computational resources. To overcome this challenge, an automated network design method known as Modified Teaching-Learning-Based Optimization with Refined Knowledge Sharing (MTLBORKS-CNN) is introduced. It autonomously searches for optimal CNN architectures, achieving high classification performance on specific datasets without human intervention. MTLBORKS-CNN incorporates four key features. It employs an effective encoding scheme for various network hyperparameters, facilitating the search for innovative and valid network architectures. During the modified teacher phase, it leverages a social learning concept to calculate unique exemplars that effectively guide learners while preserving diversity. In the modified learner phase, self-learning and adaptive peer learning are incorporated to enhance knowledge acquisition of learners during CNN architecture optimization. Finally, MTLBORKS-CNN employs a dual-criterion selection scheme, considering both fitness and diversity, to determine the survival of learners in subsequent generations. MTLBORKS-CNN is rigorously evaluated across nine image datasets and compared with state-of-the-art methods. The results consistently demonstrate MTLBORKS-CNN’s superiority in terms of classification accuracy and network complexity, suggesting its potential for infrastructural development of smart devices.

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“…In recent years, meta-heuristic algorithms are applied for solving complex problems in different applications such as optimization of weight and cost of cantilever retaining wall 70 , multi-response machining processes 71 , symbiosis organisms search for global optimization and image segmentation 72 , human social learning intelligence 73 , nanotubular halloysites in weathered pegmatites 74 , numerical optimization and real-world applications 75 , convergence analysis 76 , higher Dimensional Optimization Problems 77 , non-dominated sorting advanced 78 , Lagrange Interpolation 79 . LCA is quite different from the existing meta-heuristic algorithms although it belongs to the category of human-based meta-heuristics.…”

Section: Introductionmentioning

confidence: 99%

Learning cooking algorithm for solving global optimization problems

Gopi,

Mohapatra

2024

Sci Rep

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In recent years, many researchers have made a continuous effort to develop new and efficient meta-heuristic algorithms to address complex problems. Hence, in this study, a novel human-based meta-heuristic algorithm, namely, the learning cooking algorithm (LCA), is proposed that mimics the cooking learning activity of humans in order to solve challenging problems. The LCA strategy is primarily motivated by observing how mothers and children prepare food. The fundamental idea of the LCA strategy is mathematically designed in two phases: (i) children learn from their mothers and (ii) children and mothers learn from a chef. The performance of the proposed LCA algorithm is evaluated on 51 different benchmark functions (which includes the first 23 functions of the CEC 2005 benchmark functions) and the CEC 2019 benchmark functions compared with state-of-the-art meta-heuristic algorithms. The simulation results and statistical analysis such as the t-test, Wilcoxon rank-sum test, and Friedman test reveal that LCA may effectively address optimization problems by maintaining a proper balance between exploitation and exploration. Furthermore, the LCA algorithm has been employed to solve seven real-world engineering problems, such as the tension/compression spring design, pressure vessel design problem, welded beam design problem, speed reducer design problem, gear train design problem, three-bar truss design, and cantilever beam problem. The results demonstrate the LCA’s superiority and capability over other algorithms in solving complex optimization problems.

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Modified teaching-learning-based optimization and applications in multi-response machining processes

Cited by 10 publications

References 66 publications

Enhancing Global Optimization Performance of Arithmetic Optimization Algorithm with a Modified Population Initialization Scheme

Enhancing Global Optimization Performance of Arithmetic Optimization Algorithm with a Modified Population Initialization Scheme

MTLBORKS-CNN: An Innovative Approach for Automated Convolutional Neural Network Design for Image Classification

Learning cooking algorithm for solving global optimization problems

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