A Hybrid Meta-heuristic for the Container Loading Problem

It is very common in an enterprise daily operation to solve Container Loading Problem (CLP). Especially, it is an important issue in the logistic management. The problem aims to determine the arrangement of objects with the best utilization ratio in a container. It belongs to the combinatorial optimization problem. In this paper, a two-phased method focusing on the improvement of the efficiency and on the reducing of the problem size is proposed. In the first phase, a constructive method incorporated with a decision rule borrowing from ant colony optimization is used to construct tower set. The pheromone updating mechanism is useful in choosing proper object while constructing tower using decision rule. In the second phase, an improvement method based on genetic algorithm is used. First, the method sorts the towers by the utilization ratio and then assigns a number to each tower accordingly. The chromosome is a sequence of tower numbers which represents the arrangement of towers in the container’s bottom plane. The fitness function is defined as the utilization ratio. A new structure to store the pheromone is proposed which can help the ant in choosing the appropriate object while constructing tower. In this way, the efficiency of the method and the utilization of the container are improved

Hybrid Metaheuristics for the Traveling Salesman Problem and the Quadratic Assignment Problem

這篇論文呈現了針對複雜問題尋求最佳解而發展的超啓發式 (Metaheuristic) 演算法所做的研究，這個新的超啓發式演算法是以結合三個演算法為基礎，分別為蟻群最佳化演算法 (Ant Colony Optimization)、遺傳演算法 (Genetic Algorithm)、以及區域搜尋方法，我們將之命名為ANGEL，其主要構想為藉由整合全域搜尋法與區域搜尋法來加強探索 (exploration) 及利用 (exploitation) 的能力。在遺傳演算法中，初始母體不是經由隨機產生而是擷取蟻群最佳化演算法產生的解來組成以提供效能的提昇。同時我們也利用費洛蒙更新 (pheromone updating) 來記憶遺傳演算法的搜尋努力，並將之利用在隨後的蟻群最佳化演算法中，以將蟻群最佳化演算法引導到較好的區域。遺傳演算法及蟻群最佳化演算法所產生的解會藉由區域搜尋方法來做改善，區域搜尋對於探索區域地貌是很有用的方法，對於特定解的鄰近解的檢視，所使用的是某種類型的操作。蟻群最佳化演算法與遺傳演算法交互合作的執行以協助對方，我們也提出一個概念稱為優生策略 (Eugenic Strategy)，它能在不損失解品質的情況下加速GA的收斂。 我們使用了兩個測試問題，分別為旅行推銷員問題 (Traveling Salesman Problem) 及二次分配問題 (Quadratic Assignment Problem)。在解決旅行推銷員問題時，經由重複粹取最小擴展樹 (Minimum Spanning Tree)，可以得到一組有希望包含最佳解的邊之集合，稱之為Promising Edges Set (PES)，藉此特性，我們提出了一個新的遺傳演算法exploring PES GA (EPGA) 及一個新的交配子 exploring edge set crossover operator (EESX)，同時，也提出另一個新的交配子 eugenic edge preserving crossover operator (EEPX)，它是藉由利用PES來改善解的品質。 藉著執行一系列的實驗來評估這個新超啓發式演算法的能力，這兩個問題所使用的問題組包含各種大小的實驗組合，均取自相關著名的比較基準問題組，實驗結果顯明ANGEL的效能相當好。This dissertation represents the research of the development of new metaheuristics for locating optimal solutions to difficult problems. The new metaheuristic is founded upon the combination of three algorithms, the ant colony optimization (ACO), the genetic algorithm (GA), and the local search method, which is called ANGEL. The key idea is to enhance the ability of exploration and exploitation by incorporating global search with local search. Instead of starting from randomly generated population, GA retrieves the solutions previously constructed by ACO to provide a performance boost. In GA, the pheromone updating is used to memorize the GA's search efforts. The updated pheromone will guide ACO to a hopefully better area. The solutions generated by ACO and GA are refined by means of the local search. Local search is useful for exploring a localized landscape. The examination of the neighborhood of a given solution is derived from some class of operations. The ACO and the GA runs alternatively and cooperatively to enhance each other. A concept called the eugenic strategy that guides the GA to converge quickly without degenerating solution quality also has been proposed. Two test problems were used - the Traveling Salesman Problem (TSP) and the Quadratic Assignment Problem (QAP). In solving TSP, a set of promising edges, called promising edge set (PES), is derived by iteratively extracting the minimum spanning trees. A new genetic algorithm called the exploring PES GA (EPGA) with a new crossover operator called the exploring edge set crossover (EESX) is proposed. Also, a new crossover operator, called the eugenic edge preserving crossover (EEPX), is proposed to improve the solutions by exploiting PES. A series of experiments were conducted to assess the capabilities of this new metaheuristic. Instances of these two problems were taken from well known benchmarks with different problem sizes.Contents 1 Introduction ……………………………………………………………………… 1 1.1 Motivation …………………………………………………………………… 1 1.2 An Overview ………………………………………………………………… 3 2 Two Combinatorial Optimization Problems …………………………………… 5 2.1 The Traveling Salesman Problem …………………………………………… 5 2.1.1 Definition ………………………………………………………………… 5 2.1.2 Previously Proposed Algorithms ………………………………………… 6 2.2 The Quadratic Assignment Problem ………………………………………… 7 2.2.1 Definition ………………………………………………………………… 7 2.2.2 Previously Proposed Algorithms ………………………………………… 8 3 Techniques Used in ANGEL………………………………………………………… 10 3.1 Ant Colony Optimization …………………………………………………… 10 3.1.1 Overview of the Ant Colony Optimization ……………………………… 10 3.1.2 Ants, Pheromones, and Solutions Evaluation …………………………… 11 3.2 Genetic Algorithm …………………………………………………………… 13 3.2.1 Overview of the Genetic Algorithm …………………………………… 13 3.2.2 Genetic Operators ……………………………………………………… 14 3.3 Local Search ………………………………………………………………… 14 3.3.1 Overview of the Local Search …………………………………………… 14 3.3.2 2-OPT, 3-OPT, and Their Variants ……………………………………… 15 3.3.3 The Lin-Kernighan Algorithm …………………………………………… 16 4 Two Hybrid Metaheuristics for the Traveling Salesman Problem ……………… 18 4.1 The Exploring PES Genetic Algorithm ……………………………………… 18 4.1.1 Generation of the Promising Edge Set …………………………………… 19 4.1.2 Applying EPGA for the TSP ……………………………………………… 24 4.1.3 Solution representation …………………………………………………… 24 4.1.4 Decoding and evaluation ………………………………………………… 24 4.1.5 Genetic operators ………………………………………………………… 25 4.1.6 Initialization ……………………………………………………………… 27 4.1.7 Experimental results and discussions …………………………………… 29 4.2 ANGEL Metaheuristic for the TSP ………………………………………… 31 4.2.1 The Ant Colony Optimization …………………………………………… 32 4.2.2 The Genetic Algorithm …………………………………………………… 34 4.2.3 The Lin-Kernighan Heuristic……………………………………………… 39 4.2.4 Design of Experiments …………………………………………………… 39 4.2.5 Evaluation of Experimental Results ……………………………………… 41 5 ANGEL Metaheuristic for the Quadratic Assignment Problem ……………… 50 5.1 ANGEL Metaheuristic for the QAP ………………………………………… 51 5.1.1 The ACO Phase …………………………………………………………… 53 5.1.2 The GA Phase …………………………………………………………… 55 5.1.3 The Local Search ………………………………………………………… 61 5.2 Design of Experiments ………………………………………………………… 62 5.3 Evaluation of Experimental Results …………………………………………… 64 6 Conclusions and Future Research Works ……………………………………… 82 Bibliography …………………………………………………………………………… 84 VITA …………………………………………………………………………………… 93 Publications ……………………………………………………………………………… 94 List of Figures 3.1 An iteration of the ACO metaheuristic ……………………………………………… 11 3.2 A 2-opt move ……………………………………………………………………… 15 3.3 A 3-opt move ……………………………………………………………………… 15 4.1 Construction process of the PES for the instance att48 …………………………… 20 4.2 Representation of a chromosome …………………………………………………… 24 4.3 The decoding procedure …………………………………………………………… 25 4.4 The EESX crossover operator ……………………………………………………… 26 4.5 EPGA for TSP ……………………………………………………………………… 28 4.6 ANGELTSP metaheuristic for the TSP ……………………………………………… 31 4.7 The illustration of the OX1 crossover for TSP ……………………………………… 35 4.8 The illustration of the EEPX crossover for TSP …………………………………… 36 4.9 The illustration of the mutation operator …………………………………………… 38 5.1 The representation of a solution …………………………………………………… 51 5.2 ANGELQAP metaheuristic …………………………………………………………… 52 5.3 The ACOQAP phase of ANGEL for QAP …………………………………………… 53 5.4 The GA phase of ANGEL for QAP ………………………………………………… 56 5.5 The illustration of OX1 crossover for QAP ……………………………………… 58 5.6 The illustration of DPX crossover for QAP ………………………………………… 60 List of Tables 4.1 Analysis of the optimal tour edges' coverage rate in PES ………………………… 22 4.2 EPGA's test results of some TSPLIB instances …………………………………… 30 4.3 The comparison of ANGELTSP and LKH on TSPLIB instances with sizes greater than 500. ………………………………………………………… 44 4.4 ANGELTSP's test results of the Random Uniform Euclidean instances, Clustered Euclidean instances, and Random Matrix instances. …………………… 45 4.5 ANGELTSP's test results of Large TSPLIB instances. ……………………………… 46 4.6 The comparison of ANGELTSP's results and other methods' results on the Random Uniform Euclidean instances (E*), the Clustered Euclidean instances (C*), and the Random Matrix instances (M*). ………………………… 47 4.7 The comparison of ANGELTSP's results and other methods' results on large instances. ……………………………………………………………………… 48 4.8 The comparison of ANGELTSP, ANGELcandidatelist, Without ACO, and ANGELw/o eepx. ………………………………………………………………… 49 5.1 The computational results of ANGELQAP on all QAPLIB instances with sizes not less than 30. ……………………………………………………………… 68 5.2 Comparison of results obtained by ANGELQAP, GA+LS, and ACO+LS. ………… 69 5.3 Comparison of results obtained by ANGELQAP, GA+LS, and ACO+LS. ………… 70 5.4 Comparison of results obtained by ANGELQAP and ANGELQAP +EliteGroup. …… 71 5.5 Comparison of results obtained by ANGELQAP using different crossover operators. …………………………………………………………… 72 5.6 Quality of various heuristic methods for QAPLIB instances. ……………………… 73 5.7 The computation results of ANGELQAP on QAPLIB nug type instances. ………… 76 5.8 The computation results of ANGELQAP on QAPLIB bur type instances. ………… 76 5.9 The computation results of ANGELQAP on QAPLIB chr type instances. ………… 77 5.10 The computation results of ANGELQAP on QAPLIB esc and els type instances. … 77 5.11 The computation results of ANGELQAP on QAPLIB had and kra type instances. … 78 5.12 The computation results of ANGELQAP on QAPLIB scr and rou type instances. … 78 5.13 The computation results of ANGELQAP on QAPLIB tho, wil and ste type instances. …………………………………………………………………………… 79 5.14 The computation results of ANGELQAP on QAPLIB sko type instances. ………… 79 5.15 The computation results of ANGELQAP on QAPLIB tai type instances. ………… 80 5.16 The computation results of ANGELQAP on QAPLIB tai-b type instances. ……… 80 5.17 The computation results of ANGELQAP on QAPLIB lipa-a type instances. ……… 81 5.18 The computation results of ANGELQAP on QAPLIB lipa-b type instances. ……… 8

New Horizons of Macrophage Immunomodulation in the Healing of Diabetic Foot Ulcers

Diabetic foot ulcers (DFUs) are one of the most costly and troublesome complications of diabetes mellitus. The wound chronicity of DFUs remains the main challenge in the current and future treatment of this condition. Persistent inflammation results in chronic wounds characterized by dysregulation of immune cells, such as M1 macrophages, and impairs the polarization of M2 macrophages and the subsequent healing process of DFUs. The interactive regulation of M1 and M2 macrophages during DFU healing is critical and seems manageable. This review details how cytokines and signalling pathways are co-ordinately regulated to control the functions of M1 and M2 macrophages in normal wound repair. DFUs are defective in the M1-to-M2 transition, which halts the whole wound-healing machinery. Many pre-clinical and clinical innovative approaches, including the application of topical insulin, CCL chemokines, micro RNAs, stem cells, stem-cell-derived exosomes, skin substitutes, antioxidants, and the most recent Phase III-approved ON101 topical cream, have been shown to modulate the activity of M1 and M2 macrophages in DFUs. ON101, the newest clinically approved product in this setting, is designed specifically to down-regulate M1 macrophages and further modulate the wound microenvironment to favour M2 emergence and expansion. Finally, the recent evolution of macrophage modulation therapies and techniques will improve the effectiveness of the treatment of diverse DFUs

Association of Serum Uric Acid Concentration with Diabetic Retinopathy and Albuminuria in Taiwanese Patients with Type 2 Diabetes Mellitus

Patients with type 2 diabetes mellitus (DM) may experience chronic microvascular complications such as diabetic retinopathy (DR) and diabetic nephropathy (DN) during their lifetime. In clinical studies, serum uric acid concentration has been found to be associated with DR and DN. The goal of this study was to evaluate the relationship between the increases in serum uric acid level and the severity of DR and albuminuria in Taiwanese patients with type 2 DM. We recorded serum uric acid concentration, the severity of DR, and the severity of albuminuria by calculating urinary albumin-to-creatinine ratio (UACR) in 385 patients with type 2 DM. In multivariate logistic regression analysis, a high uric acid concentration was a risk factor for albuminuria (odds ratio (OR), 1.227; 95% confidence interval (CI) = 1.015–1.482; p = 0.034) and DR (OR, 1.264; 95% CI = 1.084–1.473; p = 0.003). We also demonstrated that there was a higher concentration of serum uric acid in the patients with more severe albuminuria and DR. In conclusion, an increased serum uric acid level was significantly correlated with the severity of albuminuria and DR in Taiwanese patients with type 2 DM

Association of Country Income Level With the Characteristics and Outcomes of Critically Ill Patients Hospitalized With Acute Kidney Injury and COVID-19

Introduction: Acute kidney injury (AKI) has been identified as one of the most common and significant problems in hospitalized patients with COVID-19. However, studies examining the relationship between COVID-19 and AKI in low- and low-middle income countries (LLMIC) are lacking. Given that AKI is known to carry a higher mortality rate in these countries, it is important to understand differences in this population. Methods: This prospective, observational study examines the AKI incidence and characteristics of 32,210 patients with COVID-19 from 49 countries across all income levels who were admitted to an intensive care unit during their hospital stay. Results: Among patients with COVID-19 admitted to the intensive care unit, AKI incidence was highest in patients in LLMIC, followed by patients in upper-middle income countries (UMIC) and high-income countries (HIC) (53%, 38%, and 30%, respectively), whereas dialysis rates were lowest among patients with AKI from LLMIC and highest among those from HIC (27% vs. 45%). Patients with AKI in LLMIC had the largest proportion of community-acquired AKI (CA-AKI) and highest rate of in-hospital death (79% vs. 54% in HIC and 66% in UMIC). The association between AKI, being from LLMIC and in-hospital death persisted even after adjusting for disease severity. Conclusions: AKI is a particularly devastating complication of COVID-19 among patients from poorer nations where the gaps in accessibility and quality of healthcare delivery have a major impact on patient outcomes

Characteristics and outcomes of an international cohort of 600 000 hospitalized patients with COVID-19

Background: We describe demographic features, treatments and clinical outcomes in the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC) COVID-19 cohort, one of the world's largest international, standardized data sets concerning hospitalized patients. Methods: The data set analysed includes COVID-19 patients hospitalized between January 2020 and January 2022 in 52 countries. We investigated how symptoms on admission, co-morbidities, risk factors and treatments varied by age, sex and other characteristics. We used Cox regression models to investigate associations between demographics, symptoms, co-morbidities and other factors with risk of death, admission to an intensive care unit (ICU) and invasive mechanical ventilation (IMV). Results: Data were available for 689 572 patients with laboratory-confirmed (91.1%) or clinically diagnosed (8.9%) SARS-CoV-2 infection from 52 countries. Age [adjusted hazard ratio per 10 years 1.49 (95% CI 1.48, 1.49)] and male sex [1.23 (1.21, 1.24)] were associated with a higher risk of death. Rates of admission to an ICU and use of IMV increased with age up to age 60 years then dropped. Symptoms, co-morbidities and treatments varied by age and had varied associations with clinical outcomes. The case-fatality ratio varied by country partly due to differences in the clinical characteristics of recruited patients and was on average 21.5%. Conclusions: Age was the strongest determinant of risk of death, with a ∼30-fold difference between the oldest and youngest groups; each of the co-morbidities included was associated with up to an almost 2-fold increase in risk. Smoking and obesity were also associated with a higher risk of death. The size of our international database and the standardized data collection method make this study a comprehensive international description of COVID-19 clinical features. Our findings may inform strategies that involve prioritization of patients hospitalized with COVID-19 who have a higher risk of death