Metaheuristic Approaches for One-Dimensional Bin Packing Problem: A Comparative Performance Study

Munien, Chanaleä; Mahabeer, Shiv; Dzitiro, Esther; Singh, Sharad; Zungu, Siluleko; Ezugwu, Absalom E.

doi:10.1109/access.2020.3046185

Cited by 15 publications

(7 citation statements)

References 61 publications

(98 reference statements)

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“…Note that high computational cost is the main drawback of the exact methods when tackling the same problem. Although meta‐heuristic algorithms cannot guarantee finding optimal solutions like the exact methods, they are able to obtain acceptable results within a reasonable time frame 27 . As such, the comparative study of this nature demonstrated in this article can serve as a practical indicator for interested domain experts in selecting more appropriate implementation methods for tackling difficult and complex QAP and its variants instances.…”

Section: Introductionmentioning

confidence: 95%

A performance study of meta‐heuristic approaches for quadratic assignment problem

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Pillay

Pillai

et al. 2021

Concurrency and Computation

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The quadratic assignment problem (QAP) is a well‐known challenging combinatorial optimization problem that has received many researchers' attention with varied real‐world and industrial applications areas. It is noteworthy to mention that a plethora of nature‐inspired optimization algorithms have successfully been used to solve various optimization problems, including several variants of the QAPs. In this article, a comprehensive literature review is presented to show the most relevant nature‐inspired algorithms that have been used in solving the QAP. More so, extensive experiments are conducted and analyzed to show the performance of the well‐known state‐of‐the‐art nature‐inspired meta‐heuristic optimization algorithms in solving the QAP, including the ant colony optimization (ACO), bat algorithm, genetic algorithm (GA), particle swarm optimization (PSO), and tabu search algorithm. Besides, a modified variant of the discrete PSO algorithm is implemented and compared with existing approaches. The six selected algorithms' performances, including the modified PSO, are validated on eight commonly used QAP instances of varying complexity and size, considering the quality of solutions achieved and computational time consumed by the representative algorithms. The numerical results revealed that the most competitive algorithm was ACO, while the GA appeared to be the worst performed algorithm among the six compared meta‐heuristic algorithms. However, based on the extensive analysis conducted on the tested algorithms, further improvements are suggested, including implementing new modified versions of the tested algorithms to tackle the QAP and its variant instances.

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Section: Introductionmentioning

confidence: 95%

A performance study of meta‐heuristic approaches for quadratic assignment problem

Achary

Pillay

Pillai

et al. 2021

Concurrency and Computation

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“…Bio-inspired optimization algorithm represents a class of metaheuristic algorithms whose principles are inspired by biology and natural phenomenon and have been successfully applied to solve different problems [49]. This category of algorithms exploits the basic process of nature and then translates them into rules or procedures, which are then model computationally for solving complex real-life problems [50] [51] [52], [53], [54], [55], [56], [57]. They are mostly population-based algorithms, and examples of such are Satin Bowerbird Optimizer (SBO), Earthworm Optimisation Algorithm (EOA), Wildebeest Herd Optimization (WHO), Virus Colony Search (VCS), Slime Mould Algorithm (SMA), Invasive weed colonization optimization (IWO), Biogeography-based optimization (BBO), Coronavirus optimization algorithm (COA), emperor penguin and salp swarm algorithm (ESA).…”

Section: Metaheuristic Optimization Algorithms: Bioinspired-based Algorithmsmentioning

confidence: 99%

Ebola Optimization Search Algorithm: A New Nature-Inspired Metaheuristic Optimization Algorithm

et al. 2022

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Nature computing has evolved with exciting performance to solve complex real-world combinatorial optimization problems. These problems span across engineering, medical sciences, and sciences generally. The Ebola virus has a propagation strategy that allows individuals in a population to move among susceptible, infected, quarantined, hospitalized, recovered, and dead sub-population groups. Motivated by the effectiveness of this strategy of propagation of the disease, a new bio-inspired and population-based optimization algorithm is proposed. This study presents a novel metaheuristic algorithm named Ebola optimization search algorithm (EOSA) based on the propagation mechanism of the Ebola virus disease. First, we designed an improved SIR model of the disease, namely SEIR-HVQD: Susceptible (S), exposed (E), Infected (I), Hospitalized (H), recovered (R), Vaccinated (V), Quarantine (Q), and Death or death (D). Secondly, we represented the new model using a mathematical model based on a system of first-order differential equations. A combination of the propagation and mathematical models was adapted for developing the new metaheuristic algorithm. To evaluate the performance and capability of the proposed method in comparison with other optimization methods, two sets of benchmark functions consisting of forty-seven (47) classical and thirty (30) constrained IEEE-CEC benchmark functions were investigated. The results indicate that the performance of the proposed algorithm is competitive with other state-of-the-art optimization methods based on scalability, convergence, and sensitivity analyses. Extensive simulation results show that the EOSA outperforms popular metaheuristic algorithms such as the Particle Swarm Optimization Algorithm (PSO), Genetic Algorithm (GA), and Artificial Bee Colony Algorithm (ABC). Also, the algorithm was applied to address the complex problem of selecting the best combination of convolutional neural network (CNN) hyperparameters in the image classification of digital mammography. Results obtained showed the optimized CNN architecture successfully detected breast cancer from digital images at an accuracy of 96.0%.

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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%

“…Ref. [30] presents a systematic performance evaluation study for some representative algorithms, with some initial computational results to show their effectiveness and their ability to achieve promising solutions. The experiments were made using three standard bin packing problem dataset categories with more than 1210 instances.…”

Section: Very Recent Relevant Publicationsmentioning

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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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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Metaheuristic Approaches for One-Dimensional Bin Packing Problem: A Comparative Performance Study

Cited by 15 publications

References 61 publications

A performance study of meta‐heuristic approaches for quadratic assignment problem

A performance study of meta‐heuristic approaches for quadratic assignment problem

Ebola Optimization Search Algorithm: A New Nature-Inspired Metaheuristic Optimization Algorithm

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

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