A New Swarm-Based Simulated Annealing Hyper-Heuristic Algorithm for Clustering Problem

Bonab, Mohammad Babrdel; Hashim, Siti Zaiton Mohd; Tay, Yong Haur; Kheng, Goh Yong

doi:10.1016/j.procs.2019.12.104

Cited by 8 publications

(2 citation statements)

References 11 publications

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“…The hyper heuristic algorithm contains a high-level heuristic and multiple low-level heuristics, where the high-level heuristic is used to choose the low-level heuristics. Bonab et al (2019) designed a swarm-based simulated annealing hyper heuristic algorithm to deal with clustering problem. Almeida et al (2020) proposed a MOEA/D-based hyper heuristic algorithm and demonstrated that the algorithm can be well applied in a permutation flow shop scheduling problem.…”

Section: Optimization Methodsmentioning

confidence: 99%

A shuffled frog leaping-based hyper heuristic algorithm for material feeding scheduling optimization in an automatic monorail shuttle system

Zhou

Zhao

2023

Preprint

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In recent years, mixed-model assembly lines (MMALs) have been widely adopted by the automotive enterprises to face the requirements of mass customization. Meanwhile, the diversified components of various end products are placing the material feeding processes at great challenges. Under this circumstance, to raise the material feeding efficiency, this paper proposes an automatic monorail shuttle system (AMSS) based on line-integrated supermarkets applied to the MMALs by introducing the load-exchangeable shuttles and crossovers. Considering the importance of cost-control in manufacturing enterprises, we establish a mathematical model with the objective to minimize the total costs of the material feeding system, which includes the installation costs of crossovers and the input and operation costs of load-exchangeable shuttles. Then, due to the NP-hard nature of the proposed problem, a shuffled frog leaping-based hyper heuristic (SBHH) algorithm is developed to determine the allocation and scheduling of the shuttles and crossovers, which adopts the shuffled frog leaping algorithm (SFLA) as the high-level heuristic to select the low-level heuristics. To improve the performance of the algorithm, the concept of ‘dynamic decision unit’ is presented to raise the solution accuracy and the convergence speed. Finally, the simulation results verified the superiority of the proposed SBHH algorithm both in solution quality and convergence speed by comparing with other optimization algorithm in the existing literature.

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Section: Optimization Methodsmentioning

confidence: 99%

A shuffled frog leaping-based hyper heuristic algorithm for material feeding scheduling optimization in an automatic monorail shuttle system

Zhou

Zhao

2023

Preprint

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“…In another study, a simulated annealing algorithm was used as the HLH to select and evaluate performance for 19 LLHs for various clustering problems. [22] The cost function was the summed distance between each instance and the cluster center. Kumari [23] proposed a hyper-heuristic algorithm for multi-objective software module clustering.…”

Section: ) Hyper-heuristicmentioning

confidence: 99%

A Multi-Objective Hyper-Heuristic Clustering Algorithm for Formulas in Traditional Chinese Medicine

Shi,

Zhang,

et al. 2023

IEEE Access

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Syndrome types are important for diagnosis and treatment in traditional Chinese medicine. Syndrome types can be summarized by domain experts as formula clusters. In this paper, we propose seven feature models for the formula clustering problem based on categories, subcategories, functional tendencies and names of Chinese materia medica. A novel multi-objective clustering hyper-heuristic algorithm is obtained. In our proposed algorithm, 12 low-level heuristics are used for clustering solution perturbation by merging clusters, dividing clusters or moving points between clusters based on received solutions from the high-level heuristic. The high-level heuristic evaluates the received solutions from low-level heuristics, updates the solution pool, and selects initial solutions for the next iteration via roulette wheel selection on the Pareto front. Experimental results demonstrate that the proposed algorithm outperforms other clustering algorithms in most datasets. The initial number of clusters has less influence on the final clustering solutions for our proposed algorithm than for other clustering algorithms. For most datasets, the roulette wheel selection mechanism on the Pareto front shows higher convergence rates and accuracy than a random selection mechanism. Accuracy was higher for feature models based on functional tendencies than for the other feature models.

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A New Genetic-Based Hyper-Heuristic Algorithm for Clustering Problem

Bonab

Goi

Madhavan

et al. 2021

Advances in Intelligent Systems and Computing

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A New Swarm-Based Simulated Annealing Hyper-Heuristic Algorithm for Clustering Problem

Cited by 8 publications

References 11 publications

A shuffled frog leaping-based hyper heuristic algorithm for material feeding scheduling optimization in an automatic monorail shuttle system

A shuffled frog leaping-based hyper heuristic algorithm for material feeding scheduling optimization in an automatic monorail shuttle system

A Multi-Objective Hyper-Heuristic Clustering Algorithm for Formulas in Traditional Chinese Medicine

A New Genetic-Based Hyper-Heuristic Algorithm for Clustering Problem

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