Hybridization of Particle Swarm Optimization with Variable Neighborhood Search and Simulated Annealing for Improved Handling of the Permutation Flow-Shop Scheduling Problem

Hayat, Iqbal; Tariq, Adnan; Shahzad, Waseem; Masud, Manzar; Ahmed, Shihab; Ali, Muhammad Umair; Zafar, Amad

doi:10.3390/systems11050221

Cited by 10 publications

(3 citation statements)

References 51 publications

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“…Several heuristic algorithms have been proposed to obtain approximate solutions for PFSP [26][27][28][29]. Additionally, metaheuristic optimization techniques have revolutionized combinatorial optimization, leading to a diverse range of strategies for solving PFSP, including genetic algorithms [30,31], simulated annealing [32], estimation of distribution algorithms [7,10,33], and particle swarm optimization [34,35]. In [36], a parallel metaheuristic approach is designed to tackle PFSP.…”

Section: Introductionmentioning

confidence: 99%

An Estimation of Distribution Algorithm for Permutation Flow-Shop Scheduling Problem

Lemtenneche,

Bensayah,

Cheriet

2023

Systems

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Estimation of distribution algorithms (EDAs) is a subset of evolutionary algorithms widely used in various optimization problems, known for their favorable results. Each generation of EDAs builds a probabilistic model to represent the most promising individuals, and the next generation is created by sampling from this model. The primary challenge in designing such algorithms lies in effectively constructing the probabilistic model. The mutual exclusivity constraint imposes an additional challenge for EDAs to approach permutation-based problems. In this study, we propose a new EDA called Position-Guided Sampling Estimation of Distribution Algorithm (PGS-EDA) specifically designed for permutation-based problems. Unlike conventional approaches, our algorithm focuses on the positions rather than the elements during the sampling phase. We evaluate the performance of our algorithm on the Permutation Flow-shop Scheduling Problem (PFSP). The experiments conducted on various sizes of Taillard instances provide evidence of the effectiveness of our algorithm in addressing the PFSP, particularly for small and medium-sized problems. The comparison results with other EDAs designed to handle permutation problems demonstrate that our PSG-EDA algorithm consistently achieves the lowest Average Relative Percentage Deviation (ARPD) values in 19 out of the 30 instances of sizes 20 and 50 used in the study. These findings validate the superior performance of our algorithm in terms of minimizing the makespan criterion of the PFSP.

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Section: Introductionmentioning

confidence: 99%

An Estimation of Distribution Algorithm for Permutation Flow-Shop Scheduling Problem

Lemtenneche,

Bensayah,

Cheriet

2023

Systems

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“…Another approach to efficient optimization is to use metaheuristic algorithms, such as genetic algorithms, simulated annealing, and particle swarm optimization. These algorithms can be used to optimize the hyperparameters of machine learning models, which can improve their performance and reduce their computational cost (Pavão et al, 2017) (Panda, 2018) (Hayat et al, 2023) (Almufti et al, 2023) (Belkourchia et al, 2023).…”

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confidence: 99%

Efficient optimization algorithms for various machine learning tasks, including classification, regression, and clustering

Sihotang

Albert²,

Riandari

et al. 2023

Idea

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The research on efficient optimization algorithms for machine learning is novel because it addresses several gaps in previous research and proposes new solutions to improve the efficiency and accuracy of machine learning models. Firstly, the proposed research focuses on developing more efficient algorithms for large-scale deep learning. While there have been many optimization algorithms proposed for deep learning, the proposed research aims to develop new algorithms that can handle the complexity and scale of these models and improve their efficiency. Secondly, the proposed research aims to explore the effectiveness of optimization algorithms for different types of machine learning tasks. While many studies have focused on deep learning, the proposed research aims to evaluate the effectiveness of optimization algorithms for other types of machine learning tasks, such as reinforcement learning, unsupervised learning, and semi-supervised learning. Thirdly, the proposed research aims to develop optimization algorithms that can handle noisy and incomplete data, which is a significant challenge for machine learning models. The proposed research aims to develop algorithms that can handle noisy and incomplete data and improve the accuracy of machine learning models. Fourthly, the proposed research aims to develop optimization algorithms that can handle non-convex objective functions. While some optimization techniques have been proposed for non-convex optimization, the proposed research aims to develop new algorithms that can handle these functions and improve the accuracy of machine learning models. The proposed research aims to investigate the trade-off between optimization efficiency and model performance. While previous research has explored this trade-off to some extent, the proposed research aims to develop algorithms that can balance these factors and optimize both efficiency and performance. The proposed research is novel because it addresses several gaps in previous research and proposes new solutions to improve the efficiency and accuracy of machine learning models for various tasks, including classification, regression, and clustering. By developing new algorithms and evaluating their effectiveness for different types of machine learning tasks, the proposed research can advance the field of machine learning and improve the accuracy and efficiency of machine learning models.

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“…This algorithm seamlessly merges the attributes of the newly introduced technique with the innovative concept of chaos, promising enhanced performance in handling complex scenarios. The inquiry described in [50] introduces the hybridization of the particle swarm optimization with variable neighborhood search and simulated annealing to tackle permutation flow-shop scheduling problems. The findings detailed in [51] highlight an original hybrid algorithm integrating principles from the particle swarm optimization and puffer fish algorithms, aiming to accurately estimate parameters related to fuel cells.…”

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confidence: 99%

Integrating the Opposition Nelder–Mead Algorithm into the Selection Phase of the Genetic Algorithm for Enhanced Optimization

Zitouni,

Harous

2023

ASI

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In this paper, we propose a novel methodology that combines the opposition Nelder–Mead algorithm and the selection phase of the genetic algorithm. This integration aims to enhance the performance of the overall algorithm. To evaluate the effectiveness of our methodology, we conducted a comprehensive comparative study involving 11 state-of-the-art algorithms renowned for their exceptional performance in the 2022 IEEE Congress on Evolutionary Computation (CEC 2022). Following rigorous analysis, which included a Friedman test and subsequent Dunn’s post hoc test, our algorithm demonstrated outstanding performance. In fact, our methodology exhibited equal or superior performance compared to the other algorithms in the majority of cases examined. These results highlight the effectiveness and competitiveness of our proposed approach, showcasing its potential to achieve state-of-the-art performance in solving optimization problems.

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Hybridization of Particle Swarm Optimization with Variable Neighborhood Search and Simulated Annealing for Improved Handling of the Permutation Flow-Shop Scheduling Problem

Cited by 10 publications

References 51 publications

An Estimation of Distribution Algorithm for Permutation Flow-Shop Scheduling Problem

An Estimation of Distribution Algorithm for Permutation Flow-Shop Scheduling Problem

Efficient optimization algorithms for various machine learning tasks, including classification, regression, and clustering

Integrating the Opposition Nelder–Mead Algorithm into the Selection Phase of the Genetic Algorithm for Enhanced Optimization

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