Toplu taşıma sistemlerinin evrimsel algoritmalarla optimizasyonu

This study aims to examine, regulate, and update the land transportation of the Erzurum Metropolitan Municipality (EMM), Turkey using computerized calculation techniques. In line with these targets, some critical information has been obtained for study: the number of buses, the number of expeditions, the number of bus lines, and the number and maps of existing routes belonging to EMM. By using the information that has been obtained, this study aims at outlining specific outputs according to the input parameters, such as determining the optimal routes, the average travel, and the journey time. Once all of these situations were considered, various optimization algorithms were used to get the targeted outputs in response to the determined input parameters. In addition, the study found that the problem involved in modeling the land transport network of the EMM is in line with the so-called “traveling salesman problem,” which is a scenario about optimization often discussed in the literature. This study tried to solve this problem by using the genetic algorithm, the clonal selection algorithm, and the DNA computing algorithm. The location data for each bus stops on the bus lines selected for the study were obtained from the EMM, and the distances between these coordinates were obtained by using Google Maps via a Google API. These distances were stored in a distance matrix file and used as input parameters in the application and then were put through optimization algorithms developed initially on the MATLAB platform. The study’s results show that the algorithms developed for the proposed approaches work efficiently and that the distances for the selected bus lines can be shortened.Bu çalışma, Erzurum Büyükşehir Belediyesi'nin (EBB) Türkiye kara ulaşımını bilgisayarlı hesaplama teknikleri kullanarak incelemeyi, düzenlemeyi ve güncellemeyi amaçlamaktadır. Bu hedefler doğrultusunda, çalışma için bazı önemli bilgiler: otobüs sayısı, sefer sayısı, otobüs hattı sayısı ve EBB’ye ait mevcut güzergâh sayısı ve haritaları elde edilmiştir. Bu çalışma, elde edilen bilgileri kullanarak, optimal rotaların belirlenmesi, ortalama seyahat ve yolculuk süresi gibi girdi parametrelerine göre belirli çıktıların ana hatlarını çizmeyi amaçlamaktadır. Tüm bu durumlar göz önüne alındığında, belirlenen girdi parametrelerine karşılık hedeflenen çıktıları elde etmek için çeşitli optimizasyon algoritmaları kullanılmıştır. Çalışma, EBB’ nin ulaşım ağının modellenmesindeki problemin, literatürde sıklıkla tartışılan optimizasyonla ilgili bir senaryo olan “gezgin satıcı problemi” ile uyumlu olduğunu bulmuştur. Çalışmada genetik algoritma, klonal seçim algoritması ve DNA hesaplama algoritması kullanılarak bu problem çözülmeye çalışılmıştır. Çalışmada seçilen otobüs hatlarındaki her bir durak için konum bilgisi EBB'den alınmış ve bu koordinatlar arasındaki mesafeler bir Google API üzerinden Google Maps kullanılarak elde edilmiştir. Bu mesafeler bir mesafe matrisi dosyasında saklanmış ve uygulamada giriş parametreleri olarak kullanılmış daha sonra MATLAB platformunda geliştirilen optimizasyon algoritmalarına aktarılmıştır. Çalışmanın sonuçları, önerilen yaklaşımlar için geliştirilen algoritmaların verimli çalıştığını ve seçilen otobüs hatları için mesafelerin kısaltılabileceğini göstermektedir

Traveling Salesman Problem

This book is a collection of current research in the application of evolutionary algorithms and other optimal algorithms to solving the TSP problem. It brings together researchers with applications in Artificial Immune Systems, Genetic Algorithms, Neural Networks and Differential Evolution Algorithm. Hybrid systems, like Fuzzy Maps, Chaotic Maps and Parallelized TSP are also presented. Most importantly, this book presents both theoretical as well as practical applications of TSP, which will be a vital tool for researchers and graduate entry students in the field of applied Mathematics, Computing Science and Engineering

Data Partition and Communication On Parallel Heuristik Model Based on Clonal Selection Algorithm

Researchers conducted experiments on parallel algorithms, which are inspired by the clonal selection, called Clonal Selection Algorithm (CSA). This algorithm is a population-based heuristic solution. Course-grained parallelism model applied to improve the execution time. Inter-process communication overhead is addressed by adjusting the communication frequencies and size of data communicated. In this research, conducted experiments on six parallel computing models represent all possible partitions and communications. Experiments conducted using data of NP-Problem, Traveling Salesman Problem (TSP). The algorithm is implemented using the model of message passing libraries using MPJExpress. Experiments conducted in a cluster computation environment. Result shows the best parallelism model is achieved by partitioning the initial population data at the beginning of communication and the end of generation. Communication frequency can be up to per 1% of the population size generated. Using four dataset from TSPLib, this reseache shows effect of the communication frequency that increased the best cost, from 44.16% to 87.01% for berlin52.tsp; from 9.61% to 53.43%  for kroA100.tsp, and from 12.22% to 17.18% for tsp225.tsp. With eight processors, using communication frequency will be reduced the execution time e.g 93.07%, 91.60%, 89.60%, 74.74% for burma14.tsp, berlin52.tsp, kroA100.tsp, and tsp225.tsp respectively. We conclude that frequency of communication greatly affects the execution time, and also the best cost. It improved execution time and best cost

Modified Discrete Firefly Algorithm Combining Genetic Algorithm for Traveling Salesman Problem

The Firefly Algorithm (FA) has a few disadvantages in solving the constrained global optimization problem, including that it is difficult to produce initial population, the size of relative attractiveness has nothing to do with the absolute brightness of fireflies, the inertia weight does not take full advantage of the information of objective function, and it cannot better control and constrain the mobile distance of firefly. In this paper, we propose a novel method based on discrete firefly algorithm combining genetic algorithm for traveling salesman problem. We redefine the distance of firefly algorithm by introducing swap operator and swap sequence to avoid algorithm easily falling into local solution and accelerate convergence speed. In addition, we adopt dynamic mechanism based on neighborhood search algorithm. Finally, the comparison experiment results show that the novel algorithm can search perfect solution within a short time, and greatly improve the effectiveness of solving the traveling salesman problem, it also significantly improves computing speed and reduces iteration number

免疫学的および進化的アルゴリズムに基づく改良された群知能最適化に関する研究

富山大学・富理工博甲第175号・楊玉・2020/3/24富山大学202

Immunity-based evolutionary algorithm for optimal global container repositioning in liner shipping

Global container repositioning in liner shipping has always been a challenging problem in container transportation as the global market in maritime logistics is complex and competitive. Supply and demand are dynamic under the ever changing trade imbalance. A useful computation optimization tool to assist shipping liners on decision making and planning to reposition large quantities of empty containers from surplus countries to deficit regions in a cost effective manner is crucial. A novel immunity-based evolutionary algorithm known as immunity-based evolutionary algorithm (IMEA) is developed to solve the multi-objective container repositioning problems in this research. The algorithm adopts the clonal selection and immune suppression theories to attain the Pareto optimal front. The proposed algorithm was verified with benchmarking functions and compared with four optimization algorithms to assess its diversity and spread. The developed algorithm provides a useful means to solve the problem and assist shipping liners in the global container transportation operations in an optimized and cost effective manner. © 2010 The Author(s).published_or_final_versionSpringer Open Choice, 21 Feb 201

Classification results of coronary heart disease database by using the clonal selection method with receptor editing

The clonal selection principle is used to explain the basic features of an adaptive immune response to a antigenic stimulus. It established the idea that only those cells that recognize the antigens are selected to proliferate and differentiate. This paper explains a computational implementation of the clonal selection principle that explicitly takes into account the affinity maturation of the immune response. The clonal selection algorithm by incorporating receptor editing method, RECSA, has been proposed by Gao. This paper tries to classify the medical database of Coronary Heart Disease databases and reports the computational results for 4 kinds of training datasets