Harmony search algorithm with adaptive parameter setting for solving large bin packing problems

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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“…Let Reference [49] be mentioned here. It proposes a new (sophisticated) algorithm and solves 120 instances from the Scholl group.…”

Section: Bin Packing Benchmarksmentioning

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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Harmony search algorithm with adaptive parameter setting for solving large bin packing problems

Adamuthe¹,

Nitave²

2020

10.5267/j.dsl

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Optimizing large scale bin packing problem with hybrid harmony search algorithm

Adamuthe¹,

Nitave²

2021

10.5267/j.ijiec

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Bin packing problem (BPP) is a combinatorial optimization problem with a wide range of applications in fields such as financial budgeting, load balancing, project management, supply chain management. Harmony search algorithm (HSA) is widely used for various real-world and engineering problems due to its simplicity and efficient problem solving capability. Literature shows that basic HSA needs improvement in search capability as the performance of the algorithm degrades with increase in the problem complexity. This paper presents HSA with improved exploration and exploitation capability coupled with local iterative search based on random swap operator for solving BPP. The study uses the despotism based approach presented by Yadav et al. (2012) [Yadav P., Kumar R., Panda S.K., Chang, C. S. (2012). An intelligent tuned harmony search algorithm for optimisation. Information Sciences, 196, 47-72.] to divide Harmony memory (HM) into two categories which helps to maintain balance between exploration and exploitation. Secondly, local iterative search explores multiple neighborhoods by exponentially swapping components of solution vectors. A problem specific HM representation, HM re-initialization strategy and two adaptive PAR strategies are tested. The performance of proposed HSA is evaluated on 180 benchmark instances which consists of 100, 200 and 500 objects. Evaluation metrics such as best, mean, success rate, acceleration rate and improvement measures are used to compare HSA variations. The performance of the HSA with iterative local search outperforms other two variations of HSA.

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Harmony search algorithm with adaptive parameter setting for solving large bin packing problems

Cited by 3 publications

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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

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

Harmony search algorithm with adaptive parameter setting for solving large bin packing problems

Optimizing large scale bin packing problem with hybrid harmony search algorithm

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