A quasi-human strategy-based improved basin filling algorithm for the orthogonal rectangular packing problem with mass balance constraint

Liu, Jingfa; Li, Jian; Lu, Zhengding; Xue, Yu

doi:10.1016/j.cie.2017.03.019

Cited by 8 publications

(5 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this experiment, we ran the ABC algorithm on a benchmark provided in [7], that there are 10 test instances R1, R2, . .…”

Section: B Experiments 2: Numerical Computation On An Open Benchmark ...mentioning

confidence: 99%

“…, 100 respectively. Two algorithms in the literatures were selected as the baselines, which are the quasi-human algorithm (IBF) in [7] and the genetic algorithm (GA) in [2]. IBF uses the container radius as an input and its objective is to find a layout smaller than the given radius.…”

Section: B Experiments 2: Numerical Computation On An Open Benchmark ...mentioning

confidence: 99%

“…From then on, some meta-heuristics were proposed for this problem, such as the genetic algorithms (GA) [2], the particle swarm optimization (PSO) [3,4], the simulated annealing algorithm (SA) [5], and the ant colony optimization (ACO) [6]. Another class of algorithms are based on the quasi-physics and quasi-human strategies [7], that an elastic potential energy function is defined to measure the overlaps between objects, and then the overlaps are reduced by the elastic force step by step. Recently, [8] This article aims at designing a new algorithm for RLOP with the help of some "guiders".…”

Section: Introductionmentioning

confidence: 99%

“…On an open benchmark with ten instances, the performance of the proposed ABC outperforms the widely used genetic algorithm [2] and the quasi-human algorithm [7].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Problem-specific knowledge based artificial bee colony algorithm for the rectangle layout optimization problem in satellite design

Xu¹,

Wan²,

Dong³

2022

Proceedings of the 34th International Conference on Software Engineering and Knowledge Engineering

View full text Add to dashboard Cite

The layout optimization problem is brought from the design of the recoverable satellite, where a set of objects (equipments or devices) are required to be installed on a circular load board. The aim of the problem is to find a layout of the objects with no interference, less unbalance, and less space occupied. Artificial bee colony (ABC) algorithms show good performance in many engineering problems. In this article, based on the analysis of the solution distribution, a problemspecific knowledge based ABC is proposed, which is configured with special initialization and parameter settings. On an open benchmark with ten instances, the proposed ABC is compared with two widely used algorithms. Its performance outperforms the genetic algorithm on all the instances, and outperforms the quasi-human algorithm on nine instances.

show abstract

“…In this experiment, we ran the ABC algorithm on a benchmark provided in [7], that there are 10 test instances R1, R2, . .…”

Section: B Experiments 2: Numerical Computation On An Open Benchmark ...mentioning

confidence: 99%

Section: B Experiments 2: Numerical Computation On An Open Benchmark ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…On an open benchmark with ten instances, the performance of the proposed ABC outperforms the widely used genetic algorithm [2] and the quasi-human algorithm [7].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Problem-specific knowledge based artificial bee colony algorithm for the rectangle layout optimization problem in satellite design

Xu¹,

Wan²,

Dong³

2022

Proceedings of the 34th International Conference on Software Engineering and Knowledge Engineering

View full text Add to dashboard Cite

show abstract

“…On the other hand, the global optimization technique [33] tries to solve the packing problem by improving the solution iteratively based on an initial solution, which is subdivided into two types. The first type is called the quasi-physical quasi-human algorithm [34,35,36], which is mostly motivated by some physical phenomenon, or some wisdom observed in human activities [37,38]. The second type is called the meta-heuristic optimization, mainly built by defining an evaluation function that employs a trade-off of randomisation and local search that directs and re-models the basic heuristic to generate feasible solutions.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Large Neighborhood Search for Circle Bin Packing Problem

He,

Tole,

et al. 2020

Preprint

View full text Add to dashboard Cite

We address a new variant of packing problem called the circle bin packing problem (CBPP), which is to find a dense packing of circle items to multiple square bins so as to minimize the number of used bins. To this end, we propose an adaptive large neighborhood search (ALNS) algorithm, which uses our Greedy Algorithm with Corner Occupying Action (GACOA) to construct an initial layout. The greedy solution is usually in a local optimum trap, and ALNS enables multiple neighborhood search that depends on the stochastic annealing schedule to avoid getting stuck in local minimum traps. Specifically, ALNS perturbs the current layout to jump out of a local optimum by iteratively reassigns some circles and accepts the new layout with some probability during the search. The acceptance probability is adjusted adaptively using simulated annealing that fine-tunes the search direction in order to reach the global optimum. We benchmark computational results against GACOA in heterogeneous instances. ALNS always outperforms GACOA in improving the objective function, and in several cases, there is a significant reduction on the number of bins used in the packing.

show abstract

Incomplete Big Data Filling Algorithm Based on Deep Learning

Chen¹

2022

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

A quasi-human strategy-based improved basin filling algorithm for the orthogonal rectangular packing problem with mass balance constraint

Cited by 8 publications

References 29 publications

Problem-specific knowledge based artificial bee colony algorithm for the rectangle layout optimization problem in satellite design

Problem-specific knowledge based artificial bee colony algorithm for the rectangle layout optimization problem in satellite design

Adaptive Large Neighborhood Search for Circle Bin Packing Problem

Incomplete Big Data Filling Algorithm Based on Deep Learning

Contact Info

Product

Resources

About