An Adaptive Fitness-Dependent Optimizer for the One-Dimensional Bin Packing Problem

In recent years, the one-dimensional bin packing problem (1D-BPP) has become one of the most famous combinatorial optimization problems. The 1D-BPP is a robust NP-hard problem that can be solved through optimization algorithms. This paper proposes an adaptive procedure using a recently optimized swa...
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Autor*in:	Diaa Salama Abdul-Minaam [verfasserIn] Wadha Mohammed Edkheel Saqar Al-Mutairi [verfasserIn] Mohamed A. Awad [verfasserIn] Walaa H. El-Ashmawi [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Bin packing first fit heuristic fitness-dependent optimizer swarm intelligent algorithms

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 8(2020), Seite 97959-97974
Übergeordnetes Werk:	volume:8 ; year:2020 ; pages:97959-97974

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2020.2985752

Katalog-ID:	DOAJ056654960

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520			\|a In recent years, the one-dimensional bin packing problem (1D-BPP) has become one of the most famous combinatorial optimization problems. The 1D-BPP is a robust NP-hard problem that can be solved through optimization algorithms. This paper proposes an adaptive procedure using a recently optimized swarm algorithm and fitness-dependent optimizer (FDO), named the AFDO, to solve the BPP. The proposed algorithm is based on the generation of a feasible initial population through a modified well-known first fit (FF) heuristic approach. To obtain a final optimized solution, the most critical parameters of the algorithm are adapted for the problem. To the best of our knowledge, this is the first study to apply the FDO algorithm in a discrete optimization problem, especially for solving the BPP. The adaptive algorithm was tested on 30 instances obtained from benchmark datasets. The performance and evaluation results of this algorithm were compared with those of other popular algorithms, such as the particle swarm optimization (PSO) algorithm, crow search algorithm (CSA), and Jaya algorithm. The AFDO algorithm obtained the smallest fitness values and outperformed the PSO, CS, and Jaya algorithms by 16%, 17%, and 11%, respectively. Moreover, the AFDO shows superiority in terms of execution time with improvements over the execution times of the PSO, CS, and Jaya algorithms by up to 46%, 54%, and 43%, respectively. The experimental results illustrate the effectiveness of the proposed adaptive algorithm for solving the 1D-BPP.
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spelling	10.1109/ACCESS.2020.2985752 doi (DE-627)DOAJ056654960 (DE-599)DOAJ7f30a1e519754050b6acb7ed98b42785 DE-627 ger DE-627 rakwb eng TK1-9971 Diaa Salama Abdul-Minaam verfasserin aut An Adaptive Fitness-Dependent Optimizer for the One-Dimensional Bin Packing Problem 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, the one-dimensional bin packing problem (1D-BPP) has become one of the most famous combinatorial optimization problems. The 1D-BPP is a robust NP-hard problem that can be solved through optimization algorithms. This paper proposes an adaptive procedure using a recently optimized swarm algorithm and fitness-dependent optimizer (FDO), named the AFDO, to solve the BPP. The proposed algorithm is based on the generation of a feasible initial population through a modified well-known first fit (FF) heuristic approach. To obtain a final optimized solution, the most critical parameters of the algorithm are adapted for the problem. To the best of our knowledge, this is the first study to apply the FDO algorithm in a discrete optimization problem, especially for solving the BPP. The adaptive algorithm was tested on 30 instances obtained from benchmark datasets. The performance and evaluation results of this algorithm were compared with those of other popular algorithms, such as the particle swarm optimization (PSO) algorithm, crow search algorithm (CSA), and Jaya algorithm. The AFDO algorithm obtained the smallest fitness values and outperformed the PSO, CS, and Jaya algorithms by 16%, 17%, and 11%, respectively. Moreover, the AFDO shows superiority in terms of execution time with improvements over the execution times of the PSO, CS, and Jaya algorithms by up to 46%, 54%, and 43%, respectively. The experimental results illustrate the effectiveness of the proposed adaptive algorithm for solving the 1D-BPP. Bin packing first fit heuristic fitness-dependent optimizer swarm intelligent algorithms Electrical engineering. Electronics. Nuclear engineering Wadha Mohammed Edkheel Saqar Al-Mutairi verfasserin aut Mohamed A. Awad verfasserin aut Walaa H. El-Ashmawi verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 97959-97974 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:97959-97974 https://doi.org/10.1109/ACCESS.2020.2985752 kostenfrei https://doaj.org/article/7f30a1e519754050b6acb7ed98b42785 kostenfrei https://ieeexplore.ieee.org/document/9057531/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 97959-97974
allfields_unstemmed	10.1109/ACCESS.2020.2985752 doi (DE-627)DOAJ056654960 (DE-599)DOAJ7f30a1e519754050b6acb7ed98b42785 DE-627 ger DE-627 rakwb eng TK1-9971 Diaa Salama Abdul-Minaam verfasserin aut An Adaptive Fitness-Dependent Optimizer for the One-Dimensional Bin Packing Problem 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, the one-dimensional bin packing problem (1D-BPP) has become one of the most famous combinatorial optimization problems. The 1D-BPP is a robust NP-hard problem that can be solved through optimization algorithms. This paper proposes an adaptive procedure using a recently optimized swarm algorithm and fitness-dependent optimizer (FDO), named the AFDO, to solve the BPP. The proposed algorithm is based on the generation of a feasible initial population through a modified well-known first fit (FF) heuristic approach. To obtain a final optimized solution, the most critical parameters of the algorithm are adapted for the problem. To the best of our knowledge, this is the first study to apply the FDO algorithm in a discrete optimization problem, especially for solving the BPP. The adaptive algorithm was tested on 30 instances obtained from benchmark datasets. The performance and evaluation results of this algorithm were compared with those of other popular algorithms, such as the particle swarm optimization (PSO) algorithm, crow search algorithm (CSA), and Jaya algorithm. The AFDO algorithm obtained the smallest fitness values and outperformed the PSO, CS, and Jaya algorithms by 16%, 17%, and 11%, respectively. Moreover, the AFDO shows superiority in terms of execution time with improvements over the execution times of the PSO, CS, and Jaya algorithms by up to 46%, 54%, and 43%, respectively. The experimental results illustrate the effectiveness of the proposed adaptive algorithm for solving the 1D-BPP. Bin packing first fit heuristic fitness-dependent optimizer swarm intelligent algorithms Electrical engineering. Electronics. Nuclear engineering Wadha Mohammed Edkheel Saqar Al-Mutairi verfasserin aut Mohamed A. Awad verfasserin aut Walaa H. El-Ashmawi verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 97959-97974 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:97959-97974 https://doi.org/10.1109/ACCESS.2020.2985752 kostenfrei https://doaj.org/article/7f30a1e519754050b6acb7ed98b42785 kostenfrei https://ieeexplore.ieee.org/document/9057531/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 97959-97974
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allfieldsSound	10.1109/ACCESS.2020.2985752 doi (DE-627)DOAJ056654960 (DE-599)DOAJ7f30a1e519754050b6acb7ed98b42785 DE-627 ger DE-627 rakwb eng TK1-9971 Diaa Salama Abdul-Minaam verfasserin aut An Adaptive Fitness-Dependent Optimizer for the One-Dimensional Bin Packing Problem 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, the one-dimensional bin packing problem (1D-BPP) has become one of the most famous combinatorial optimization problems. The 1D-BPP is a robust NP-hard problem that can be solved through optimization algorithms. This paper proposes an adaptive procedure using a recently optimized swarm algorithm and fitness-dependent optimizer (FDO), named the AFDO, to solve the BPP. The proposed algorithm is based on the generation of a feasible initial population through a modified well-known first fit (FF) heuristic approach. To obtain a final optimized solution, the most critical parameters of the algorithm are adapted for the problem. To the best of our knowledge, this is the first study to apply the FDO algorithm in a discrete optimization problem, especially for solving the BPP. The adaptive algorithm was tested on 30 instances obtained from benchmark datasets. The performance and evaluation results of this algorithm were compared with those of other popular algorithms, such as the particle swarm optimization (PSO) algorithm, crow search algorithm (CSA), and Jaya algorithm. The AFDO algorithm obtained the smallest fitness values and outperformed the PSO, CS, and Jaya algorithms by 16%, 17%, and 11%, respectively. Moreover, the AFDO shows superiority in terms of execution time with improvements over the execution times of the PSO, CS, and Jaya algorithms by up to 46%, 54%, and 43%, respectively. The experimental results illustrate the effectiveness of the proposed adaptive algorithm for solving the 1D-BPP. Bin packing first fit heuristic fitness-dependent optimizer swarm intelligent algorithms Electrical engineering. Electronics. Nuclear engineering Wadha Mohammed Edkheel Saqar Al-Mutairi verfasserin aut Mohamed A. Awad verfasserin aut Walaa H. El-Ashmawi verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 97959-97974 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:97959-97974 https://doi.org/10.1109/ACCESS.2020.2985752 kostenfrei https://doaj.org/article/7f30a1e519754050b6acb7ed98b42785 kostenfrei https://ieeexplore.ieee.org/document/9057531/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 97959-97974
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callnumber-first	T - Technology
author	Diaa Salama Abdul-Minaam
spellingShingle	Diaa Salama Abdul-Minaam misc TK1-9971 misc Bin packing misc first fit heuristic misc fitness-dependent optimizer misc swarm intelligent algorithms misc Electrical engineering. Electronics. Nuclear engineering An Adaptive Fitness-Dependent Optimizer for the One-Dimensional Bin Packing Problem
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topic_title	TK1-9971 An Adaptive Fitness-Dependent Optimizer for the One-Dimensional Bin Packing Problem Bin packing first fit heuristic fitness-dependent optimizer swarm intelligent algorithms
topic	misc TK1-9971 misc Bin packing misc first fit heuristic misc fitness-dependent optimizer misc swarm intelligent algorithms misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Bin packing misc first fit heuristic misc fitness-dependent optimizer misc swarm intelligent algorithms misc Electrical engineering. Electronics. Nuclear engineering
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title	An Adaptive Fitness-Dependent Optimizer for the One-Dimensional Bin Packing Problem
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title_full	An Adaptive Fitness-Dependent Optimizer for the One-Dimensional Bin Packing Problem
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title_sort	adaptive fitness-dependent optimizer for the one-dimensional bin packing problem
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title_auth	An Adaptive Fitness-Dependent Optimizer for the One-Dimensional Bin Packing Problem
abstract	In recent years, the one-dimensional bin packing problem (1D-BPP) has become one of the most famous combinatorial optimization problems. The 1D-BPP is a robust NP-hard problem that can be solved through optimization algorithms. This paper proposes an adaptive procedure using a recently optimized swarm algorithm and fitness-dependent optimizer (FDO), named the AFDO, to solve the BPP. The proposed algorithm is based on the generation of a feasible initial population through a modified well-known first fit (FF) heuristic approach. To obtain a final optimized solution, the most critical parameters of the algorithm are adapted for the problem. To the best of our knowledge, this is the first study to apply the FDO algorithm in a discrete optimization problem, especially for solving the BPP. The adaptive algorithm was tested on 30 instances obtained from benchmark datasets. The performance and evaluation results of this algorithm were compared with those of other popular algorithms, such as the particle swarm optimization (PSO) algorithm, crow search algorithm (CSA), and Jaya algorithm. The AFDO algorithm obtained the smallest fitness values and outperformed the PSO, CS, and Jaya algorithms by 16%, 17%, and 11%, respectively. Moreover, the AFDO shows superiority in terms of execution time with improvements over the execution times of the PSO, CS, and Jaya algorithms by up to 46%, 54%, and 43%, respectively. The experimental results illustrate the effectiveness of the proposed adaptive algorithm for solving the 1D-BPP.
abstractGer	In recent years, the one-dimensional bin packing problem (1D-BPP) has become one of the most famous combinatorial optimization problems. The 1D-BPP is a robust NP-hard problem that can be solved through optimization algorithms. This paper proposes an adaptive procedure using a recently optimized swarm algorithm and fitness-dependent optimizer (FDO), named the AFDO, to solve the BPP. The proposed algorithm is based on the generation of a feasible initial population through a modified well-known first fit (FF) heuristic approach. To obtain a final optimized solution, the most critical parameters of the algorithm are adapted for the problem. To the best of our knowledge, this is the first study to apply the FDO algorithm in a discrete optimization problem, especially for solving the BPP. The adaptive algorithm was tested on 30 instances obtained from benchmark datasets. The performance and evaluation results of this algorithm were compared with those of other popular algorithms, such as the particle swarm optimization (PSO) algorithm, crow search algorithm (CSA), and Jaya algorithm. The AFDO algorithm obtained the smallest fitness values and outperformed the PSO, CS, and Jaya algorithms by 16%, 17%, and 11%, respectively. Moreover, the AFDO shows superiority in terms of execution time with improvements over the execution times of the PSO, CS, and Jaya algorithms by up to 46%, 54%, and 43%, respectively. The experimental results illustrate the effectiveness of the proposed adaptive algorithm for solving the 1D-BPP.
abstract_unstemmed	In recent years, the one-dimensional bin packing problem (1D-BPP) has become one of the most famous combinatorial optimization problems. The 1D-BPP is a robust NP-hard problem that can be solved through optimization algorithms. This paper proposes an adaptive procedure using a recently optimized swarm algorithm and fitness-dependent optimizer (FDO), named the AFDO, to solve the BPP. The proposed algorithm is based on the generation of a feasible initial population through a modified well-known first fit (FF) heuristic approach. To obtain a final optimized solution, the most critical parameters of the algorithm are adapted for the problem. To the best of our knowledge, this is the first study to apply the FDO algorithm in a discrete optimization problem, especially for solving the BPP. The adaptive algorithm was tested on 30 instances obtained from benchmark datasets. The performance and evaluation results of this algorithm were compared with those of other popular algorithms, such as the particle swarm optimization (PSO) algorithm, crow search algorithm (CSA), and Jaya algorithm. The AFDO algorithm obtained the smallest fitness values and outperformed the PSO, CS, and Jaya algorithms by 16%, 17%, and 11%, respectively. Moreover, the AFDO shows superiority in terms of execution time with improvements over the execution times of the PSO, CS, and Jaya algorithms by up to 46%, 54%, and 43%, respectively. The experimental results illustrate the effectiveness of the proposed adaptive algorithm for solving the 1D-BPP.
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title_short	An Adaptive Fitness-Dependent Optimizer for the One-Dimensional Bin Packing Problem
url	https://doi.org/10.1109/ACCESS.2020.2985752 https://doaj.org/article/7f30a1e519754050b6acb7ed98b42785 https://ieeexplore.ieee.org/document/9057531/ https://doaj.org/toc/2169-3536
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author2	Wadha Mohammed Edkheel Saqar Al-Mutairi Mohamed A. Awad Walaa H. El-Ashmawi
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up_date	2024-07-03T22:08:01.819Z
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