Enhancing Solutions Of Capacity Vehicle Routing Problem Based On An Improvement Ant Colony System Algorithm

The Vehicle Routing Problem (VRP) is a famous routing issue and combinatorial optimization problem. It serves an important task in logistics and supply procession administration appropriate toward its wide applications in transport, product delivery, and services. VRP is one of the major important issues have no perfect solutions yet. Several authors over the last only some decades have recognized many types of research and used several algorithms with various methods to solve it. In this work the problem of the VRP work is described as follows: the vehicles which are used for transportation products toward instance place. Each vehicle begins from a major area at various times every day. The capacitated vehicle routing problem (CVRP) is described as toward service a set of delivery customers by means of well-known demands, the aim of CVRP is toward giving every vehicle with a series of delivers so with the purpose of each and every one of customers are serviced, and the cost of traveling for vehicles are decreased. The paper aims to discover an optimal route for VRP by using Improvement Ant Colony System Algorithm (IACS). Optimal routes are founded based on to decrease the distance and the time for each and every one route which directs to quickest the moving of customers to their locations, also based on developing the CVRP model for optimizing the routing issues. The IACS method has been mostly considered recently for handling several combinatorial optimization issues. In this paper, the IACS has been introduced for solving the CVRP. A wide numerical experiment has been performed on benchmark issues available in recent work. The results have been shown the IACS algorithm is better when compared to conventional metaheuristic methods for handling CVRP

A New Hybrid Approach Based On Discrete Differential Evolution Algorithm To Enhancement Solutions Of Quadratic Assignment Problem

The Combinatorial Optimization Problem (COPs) is one of the branches of applied mathematics and computer sciences, which is accompanied by many problems such as Facility Layout Problem (FLP), Vehicle Routing Problem (VRP), etc. Even though the use of several mathematical formulations is employed for FLP, Quadratic Assignment Problem (QAP) is one of the most commonly used. One of the major problems of Combinatorial NP-hard Optimization Problem is QAP mathematical model. Consequently, many approaches have been introduced to solve this problem, and these approaches are classified as Approximate and Exact methods. With QAP, each facility is allocated to just one location, thereby reducing cost in terms of aggregate distances weighted by flow values. The primary aim of this study is to propose a hybrid approach which combines Discrete Differential Evolution (DDE) algorithm and Tabu Search (TS) algorithm to enhance solutions of QAP model, to reduce the distances between the locations by finding the best distribution of N facilities to N locations, and to implement hybrid approach based on discrete differential evolution (HDDETS) on many instances of QAP from the benchmark. The performance of the proposed approach has been tested on several sets of instances from the data set of QAP and the results obtained have shown the effective performance of the proposed algorithm in improving several solutions of QAP in reasonable time. Afterwards, the proposed approach is compared with other recent methods in the literature review. Based on the computation results, the proposed hybrid approach outperforms the other method

Quadratic Assignment Problem (Model, Applications, Solutions): Review Paper

n operations research, Quadratic Assignment Problem (QAP) is a significant combinatorial optimization problem. When the size of the QAP problem increases, it becomes impossible to solve the problem in polynomial time. Several practical problems such as hospital and campus layout, allocation of gates to airplanes in airports and electrical backboard wiring problems can bemodeled as QAP. The QAP model seeks to identify the optimal distribution of N facilities to N locations in a way that minimizes the total traveling cost based on the distance between every pair of a location and the amount of traffic between every pair of facilities of organizational units within a building. Against this background, there are two main approaches have been suggested to deal with QAP, and they are, the Exact and Approximate (Heuristic and Metaheuristic) approaches. The exact approach provides a global optimal solution for the small size of QAP, while the approximate approaches can find the optimal or a near-optimal solution at a reasonable time for large-sized QAP. The objectives of this study are as follows: (i) To analysis the QAP model, (ii) To conduct a comprehensive survey of the methods that have been used to solve the QAP model, (iii) To identify the issues and limitations of the methods in (ii), and (iv) to explore the best approach that can be used in enhancing the solutions of QAPmodel within a reasonable time based on the accuracy of algorithm. The results show that the hybrid metaheuristic approach has the capability of finding the best results within a reasonable time for the large sized problem

An Efficient Improvement Of Ant Colony System Algorithm For Handling Capacity Vehicle Routing Problem

Capacitated Vehicle Routing Problem (CVRP) is considered as one of the most famous specialized forms of VRP that has attracted considerable attention from researchers. This problem belongs to complex combinatorial optimization problems included in the NP-Hard Problem category, which is a problem that needs difficult computation. This paper presents an improvement of Ant Colony System (ACS) to solve this problem. In this study, the problem deals with a few vehicles which are used for transporting products to specific places. Each vehicle starts from a main location at different times every day. The capacitated vehicle routing problem (CVRP) is defined to serve a group of delivery customers with known demands. The proposed study seeks to find the best solution of CVRP by using improvement ACS with the accompanying targets: (1) To decrease the distance as long distances negatively affect the course of the process since it consumes a great time to visit all customers. (2) To implement the improvement of ACS algorithm on new data from the database of CVRP. Through the implementation of the proposed algorithm better results were obtained from the results of other methods and the results were compared

Review On The Methods To Solve Combinatorial Optimization Problems Particularly:Quadratic Assignment Model

The quadratic assignment problem (QAP) is one of the fundamental combinatorial optimization problem (COPs) in the branch of optimization or operation research in mathematics,from the category of the Facilities Location Problems (FLPs).The quadratic assignment problem (QAP) be appropriate to the group of NP-hard issues and is measured as a challenging problem of the combinatorial optimization.QAP in Location Theory considers one of the problems of facilities tracing which the rate of locating a facility be determined by the spaces between facilities as well as the communication among the further facilities.QAP was presented in 1957 by Beckman and Koopmans as they were attempting to model a problem of facilities location.To survey the researcher’s works for QAP and applied,the mapped research landscape outlines literature into a logical classification and discovers this field basic characteristics represented on the motivation to use the quadratic assignment problem applied in hospital layout and campus planning.This survey achieved a concentrated each QAP article search in three key databases:Web of Science,Science Direct,and IEEE Xplore.Those databases are regarded extensive adequate in covering QAP and the methods utilized in solving QAP

Multi-Objectives Ant Colony System For Solving Multi-Objectives Capacitated Vehicle Routing Problem

As a combinatorial optimization problem, the capacitated vehicle routing problem (CVRP) is a vital one in the domains of distribution, transportation and logistics. Despite the fact that many researchers have solved the problem using a single objective, only little attention has been given to multi-objective optimization. As compared to multi-objectives, the comparison of solutions is easier with single-objective optimization fitness function. In this paper, the following objectives were achieved: (i) in view of the domain of the multiobjective CVRP, the total distance traveled by the vehicles and the total number of vehicles used are reduced, and (ii) in the view of the technique, a multi-objective Ant Colony System is proposed to solve the multiobjective of CVRP based on the experience of sub-paths. The proposed algorithm was evaluated using some standard benchmark problems of CVRP. The results show that the algorithm which has been proposed in this study is highly competitive and quite effective for multi-objective optimization of CVR

An Enhanced Ant Colony System Algorithm Based on Subpaths for Solving the Capacitated Vehicle Routing Problem

The capacitated vehicle routing problem (CVRP) is regarded as an NP-hard problem. Moreover, the CVRP is described as a model that can be used in many applications such as transport, logistics, and distribution. The exact algorithms can find exact optimal solutions on the small-sized problem instances; however, for large-sized instances it is difficult to find the exact optimal solutions in polynomial time. This reason motivated the researchers to present heuristic/metaheuristic algorithms to solve large-sized problem instances within a reasonable computational time. One of the good algorithms that deal with the CVRP is the ant colony optimization (ACO) algorithm. Several ACO algorithms have been suggested in the literature, such as the ant system (AS) algorithm, ant colony system (ACS) algorithm, and so on. On the other hand, ACO is designed to solve the path problem that finds the best way. However, this algorithm still lacks exploratory mechanisms, which results in premature convergence and stagnation issues. Therefore, we propose to develop an enhanced ACS (EACS) algorithm for solving the CVRP based on subpaths. In our proposed algorithm, we propose to utilize the K-nearest neighbour (KNN) algorithm for finding the best initial solution and then enhance the diversity mechanism of the proposed algorithm by avoiding the generation of the same solution using subpaths. This uses the diversity of the generated solution to find a better solution with a shorter route in a reasonable amount of computational time. Furthermore, we propose to apply the three-opt algorithm to the completed subtour and the k-opt algorithm to the subpath gained from the experience of the subpath. Finally, to verify the effectiveness of the proposed EACS algorithm, the algorithm is tested on some CVRP instances and is compared with one of the state-of-the-art methods, namely, the enhanced simulated annealing algorithm. The comparative study showed a better performance of our EACS compared to the enhanced simulated annealing algorithm

Transfer Learning to Detect COVID-19 Automatically from X-Ray Images Using Convolutional Neural Networks

The novel coronavirus disease 2019 (COVID-19) is a contagious disease that has caused thousands of deaths and infected millions worldwide. Thus, various technologies that allow for the fast detection of COVID-19 infections with high accuracy can offer healthcare professionals much-needed help. This study is aimed at evaluating the effectiveness of the state-of-the-art pretrained Convolutional Neural Networks (CNNs) on the automatic diagnosis of COVID-19 from chest X-rays (CXRs). The dataset used in the experiments consists of 1200 CXR images from individuals with COVID-19, 1345 CXR images from individuals with viral pneumonia, and 1341 CXR images from healthy individuals. In this paper, the effectiveness of artificial intelligence (AI) in the rapid and precise identification of COVID-19 from CXR images has been explored based on different pretrained deep learning algorithms and fine-tuned to maximise detection accuracy to identify the best algorithms. The results showed that deep learning with X-ray imaging is useful in collecting critical biological markers associated with COVID-19 infections. VGG16 and MobileNet obtained the highest accuracy of 98.28%. However, VGG16 outperformed all other models in COVID-19 detection with an accuracy, F1 score, precision, specificity, and sensitivity of 98.72%, 97.59%, 96.43%, 98.70%, and 98.78%, respectively. The outstanding performance of these pretrained models can significantly improve the speed and accuracy of COVID-19 diagnosis. However, a larger dataset of COVID-19 X-ray images is required for a more accurate and reliable identification of COVID-19 infections when using deep transfer learning. This would be extremely beneficial in this pandemic when the disease burden and the need for preventive measures are in conflict with the currently available resources

A Hybrid Method Integrating a Discrete Differential Evolution Algorithm with Tabu Search Algorithm for the Quadratic Assignment Problem: A New Approach for Locating Hospital Departments

The facility layout problem (FLP) is a very important class of NP-hard problems in operations research that deals with the optimal assignment of facilities to minimize transportation costs. The quadratic assignment problem (QAP) can model the FLP effectively. One of the FLPs is the hospital facility layout problem that aims to place comprehensive clinics, laboratories, and radiology units within predefined boundaries in a way that minimizes the cost of movement of patients and healthcare personnel. We are going to develop a hybrid method based on discrete differential evolution (DDE) algorithm for solving the QAP. In the existing DDE algorithms, certain issues such as premature convergence, stagnation, and exploitation mechanism have not been properly addressed. In this study, we first aim to discover the issues that make the current problem worse and to identify the best solution to the problem, and then we propose to develop a hybrid algorithm (HDDETS) by combining the DDE and tabu search (TS) algorithms to enhance the exploitation mechanism in the DDE algorithm. Then, the performance of the proposed HDDETS algorithm is evaluated by implementing on the benchmark instances from the QAPLIB website and by comparing with DDE and TS algorithms on the benchmark instances. It is found that the HDDETS algorithm has better performance than both the DDE and TS algorithms where the HDDETS has obtained 42 optimal and best-known solutions from 56 instances, while the DDE and TS algorithms have obtained 15 and 18 optimal and best-known solutions out of 56 instances, respectively. Finally, we propose to apply the proposed algorithm to find the optimal distributions of the advisory clinics inside the Azadi Hospital in Iraq that minimizes the total travel distance for patients when they move among these clinics. Our application shows that the proposed algorithm could find the best distribution of the hospital’s rooms, which are modeled as a QAP, with reduced total distance traveled by the patients

Реалізація покращеного мурашиного алгоритму для рішення проекту надійної комунікаційної мережі

The problem of communication design has been defined as one of the problems that belong to the category of NP-hard problem, and the aim of the topological communication network design is to identify component placement locations and connectivity aspects. On the other hand, the Reliable Communication Network Design (RCND) is a popular optimization problem used for maximizing network reliability. In addition, finding an accurate calculation of RCND explains the problem of NP-hard problem. To this end, literature studies suggested various metaheuristic algorithms that have been used as approximation methods to find the best solution to this problem. Some of these algorithms belong to the Evolutionary Algorithms (EAs) category, such as Genetic Algorithms (GAs), and some belong to the Swarm Intelligence Algorithms (SIAs) category, such as Ant Colony Optimization (ACO). However, to the best of our knowledge, the Ant Colony System (ACS) algorithm, which is considered an updated version of ACO, has not yet been used to design reliability-constrained communication network topologies. Therefore, this study aims to apply the updated version of the ACS algorithm for solving RCND in small, medium, and large networks. The proposed algorithm was benchmarked against present state-of-the-art techniques that address this challenge. The research findings show that the proposed algorithm is an optimal solution for a fully connected small network size (n=6, 7, 8, and 9) and it has been achieved as an optimal solution for all not fully connected sets (n=14, 16, and 20). In each case, the results for medium-sized networks were better than the benchmark resultsЗавдання проектування зв'язку було визначено як одну з проблем, що належать до категорії NP-складних проблем, а мета проектування топологічної мережі зв'язку полягає в тому, щоб визначити місця розміщення компонентів та аспекти зв'язності. З іншого боку, проектування надійної комунікаційної мережі (ПНКМ) – це популярне завдання оптимізації, що використовується для максимізації надійності мережі. Крім того, знаходження точного розрахунку ПНКМ пояснює проблему NP-складного завдання. З цією метою в літературних дослідженнях було запропоновано різні метаевристичні алгоритми, які використовувалися як методи апроксимації для пошуку найкращого вирішення цієї проблеми. Деякі з цих алгоритмів належать до категорії еволюційних алгоритмів (EA), наприклад, генетичні алгоритми (ГА), а деякі відносяться до категорії алгоритмів роєвого інтелекту (АРІ), наприклад, оптимізація мурашиної колонії (ОМК). Однак, наскільки нам відомо,мурашиний алгоритм (МА), який вважається оновленою версією ОМК, ще не використовувався для розробки топології мереж зв'язку з обмеженою надійністю. Таким чином, це дослідження спрямоване на застосування оновленої версії алгоритму МА для вирішення ПНКМ у малих, середніх та великих мережах. Запропонований алгоритм був порівняний з сучасними методами, що вирішують цю проблему. Результати дослідження показують, що запропонований алгоритм є оптимальним рішенням для повної мережі малого розміру (n=6, 7, 8 і 9) і був досягнутий як оптимальне рішення для всіх неповністю зв'язкових множин (n=14). , 16 та 20). У кожному разі результати для мереж середнього розміру були кращими, ніж результати еталонних тесті