RETRACTED ARTICLE: An improved nature inspired meta-heuristic algorithm for 1-D bin packing problems

Abdel‐Basset, Mohamed; Manogaran, Gunasekaran; Abdel-Fatah, Laila; Mirjalili, Seyedali

doi:10.1007/s00779-018-1132-7

Cited by 58 publications

(19 citation statements)

References 52 publications

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“…However, there are many more. A whale optimization algorithm for BP is proposed in Reference [27]; some more "animal algorithms" are cuckoo search [28], squirrel search algorithm [29]; firefly, cuckoo search, and artificial bee colony algorithms are investigated (among others) in Reference [30], bat optimization is considered in Reference [31], and African buffalo optimization in Reference [32,33]. It is hard to imagine an animal whose habits cannot be used for optimization.…”

Section: The Metaheuristic Zoomentioning

confidence: 99%

“…Operations Research also uses these tricks. For example, Reference [27] also builds some tricky improvements into the initial whale optimization algorithm (e.g., applies Lévy flight for whale movements, adds a new mutation phase, and switches in a clever way between the exploration and the exploitation phases by a logistic chaotic map). Now, let us have a closer look at the so-called Benchmark Problems.…”

Section: A "New" Paradigmmentioning

confidence: 99%

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Efficient Pre-Solve Algorithms for the Schwerin and Falkenauer_U Bin Packing Benchmark Problems for Getting Optimal Solutions with High Probability

et al. 2021

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Bin Packing is one of the research areas of Operations Research with many industrial applications, as well as rich theoretical impact. In this article, the authors deal with Bin Packing on the practical side: they consider two Bin Packing Benchmark classes. These benchmark problems are often used to check the “usefulness”, efficiency of algorithms. The problem is well-known to be NP-hard. Instead of introducing some exact, heuristic, or approximation method (as usual), the problem is attacked here with some kind of greedy algorithm. These algorithms are very fast; on the other hand, they are not always able to provide optimal solutions. Nevertheless, they can be considered as pre-processing algorithms for solving the problem. It is shown that almost all problems in the considered two benchmark classes are, in fact, easy to solve. In case of the Schwerin class, where there are 200 instances, it is obtained that all instances are solved by the greedy algorithm, optimally, in a very short time. The Falkenauer U class is a little bit harder, but, here, still more than 91% of the instances are solved optimally very fast, with the help of another greedy algorithm. Based on the above facts, the main contribution of the paper is to show that pre-processing is very useful for solving such kinds of problems.

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Section: The Metaheuristic Zoomentioning

confidence: 99%

Section: A "New" Paradigmmentioning

confidence: 99%

Efficient Pre-Solve Algorithms for the Schwerin and Falkenauer_U Bin Packing Benchmark Problems for Getting Optimal Solutions with High Probability

et al. 2021

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“…Variants of other algorithms [43], [44] have been proposed for solving the 1D-BPP, and their results were evaluated through various benchmark datasets. In this paper, the proposed adaptive algorithm was tested using three benchmark datasets with various item weights and bin capacities.…”

Section: Literature Reviewmentioning

confidence: 99%

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

et al. 2020

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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. INDEX TERMS Bin packing, first fit heuristic, fitness-dependent optimizer, swarm intelligent algorithms.

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“…Prasad et al (2018) proposed an algorithm for UAV special operations like monitoring the forest region. Abdel-Basset, Manogaran, Abdel-Fatah and Mirjalili’s (2018) meta-heuristic algorithm has been used to solve critical problems.…”

Section: Influence Of Technology In Unmanned Aerial Vehiclementioning

confidence: 99%

Influence of unmanned aerial vehicle in medical product transport

Prasad¹,

Abishek

Karthick³

2019

IJIUS

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Purpose The purpose of this paper is to discuss the special applications of unmanned aerial vehicles (UAVs) for the transport of medical goods. Design/methodology/approach Experimental work has been carried out to predict the performance characteristics of UAVs. Findings The results have been obtained to predict the range and endurance of UAVs, which can be optimized based on the payload and source of power. Originality/value Real-time applications. As the medical products are necessary in the real time life saving events.

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RETRACTED ARTICLE: An improved nature inspired meta-heuristic algorithm for 1-D bin packing problems

Cited by 58 publications

References 52 publications

Efficient Pre-Solve Algorithms for the Schwerin and Falkenauer_U Bin Packing Benchmark Problems for Getting Optimal Solutions with High Probability

Efficient Pre-Solve Algorithms for the Schwerin and Falkenauer_U Bin Packing Benchmark Problems for Getting Optimal Solutions with High Probability

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

Influence of unmanned aerial vehicle in medical product transport

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