A multi-objective genetic algorithm for a special type of 2D orthogonal packing problems

Erozan, İhsan; Çalışkan, Enes

doi:10.1016/j.apm.2019.07.010

Cited by 6 publications

(2 citation statements)

References 22 publications

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“…In conclusion, the paper method significantly improves the denoised effect for CT image of advanced COVID-19 with non-symptom to a certain extent by different noisy density. Aiming at the different kinds of COVID-19 CT imagesadaptive hybrid genetic combined with exponential simulated annealing [41] (AHG+ESA)orthogonal adaptive genetic [42] combined with rapid simulated annealing [43] (OAG+RSA)traditional adaptive genetic [44] combined with exponential simulated annealing (TAG+ESA)adaptive hybrid genetic combined with doppler effect simulated annealing (AHG+DESA, paper method) are used for simulation comparison tests of denoising parameters optimization. The fitness evolution curve for different COVID-19 CT images are shown in Fig.14 .…”

Section: Resultsmentioning

confidence: 99%

COVID-19 CT image denoising algorithm based on adaptive threshold and optimized weighted median filter

Guo

Guo-wei

Han

et al. 2022

Biomedical Signal Processing and Control

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Section: Resultsmentioning

confidence: 99%

COVID-19 CT image denoising algorithm based on adaptive threshold and optimized weighted median filter

Guo

Guo-wei

Han

et al. 2022

Biomedical Signal Processing and Control

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“…Traveling Salesman [92] Traveling Salesman [93] Multiple Traveling Salesman [94] Bottleneck Traveling Salesman [95] Cutting Stock [96] Cutting Stock [97] 2D Cutting [98] Packing [99] Packing [100] 2D Packing [101] Bin Packing [102] Knapsack [103] Knapsack [104] Subset Sum [105] Unbounded Knapsack [105] Bounded Knapsack [106] Multiple Knapsack [107] Quadratic Knapsack [108] Scheduling [109] Scheduling [110] Production Scheduling [111] Workforce Scheduling [112] Job-Shop Scheduling [113] Precedence Constrained Scheduling [114] Educational Timetabling [115] Educational Timetabling [116] Facility Location [117] Assignment [118] Quadratic Assignment [119] Spanning Tree [120] Maximum Leaf Spanning Tree [121] Degree Constrained Spanning Tree [122] Minimum Spanning Tree [123] Boolean Satisfiability [124] Boolean Satisfiability [125] Covering [126] Minimum Vertex Cover [127] Set Cover [128] Exact Cover [129] Minimum Edge Cover [130] Vehicle Routing [131] Vehicle Routing…”

Section: Type Problemmentioning

confidence: 99%

A Systematic Review of Hyper-Heuristics on Combinatorial Optimization Problems

et al. 2020

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Hyper-heuristics aim at interchanging different solvers while solving a problem. The idea is to determine the best approach for solving a problem at its current state. This way, every time we make a move it gets us closer to a solution. The problem changes; so does its state. As a consequence, for the next move, a different solver may be invoked. Hyper-heuristics have been around for almost 20 years. However, combinatorial optimization problems date from way back. Thus, it is paramount to determine whether the efforts revolving around hyper-heuristic research have been targeted at the problems of the highest interest for the combinatorial optimization community. In this work, we tackle such an endeavor. We begin by determining the most relevant combinatorial optimization problems, and then we analyze them in the context of hyper-heuristics. The idea is to verify whether they remain as relevant when considering exclusively works related to hyper-heuristics. We find that some of the most relevant problem domains have also been popular for hyper-heuristics research. Alas, others have not and few efforts have been directed towards solving them. We identify the following problem domains, which may help in furthering the impact of hyper-heuristics: Shortest Path, Set Cover, Longest Path, and Minimum Spanning Tree. We believe that focusing research on ways for solving them may lead to an increase in the relevance and impact that hyperheuristics have on combinatorial optimization problems.

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