scholarly journals MULTI-LEVEL PARALLEL FRAMEWORK

2014 ◽  
pp. 20-28
Author(s):  
Mitica Craus ◽  
Laurentiu Rudeanu
Keyword(s):  
Data Structures ◽  
Parallel Implementation ◽  
Object Oriented ◽  
Traveling Salesman ◽  
Problem Instance ◽  
Ant Colony Optimization Algorithm ◽  
Object Oriented Design ◽  
Multi Level ◽  
The Traveling Salesman Problem ◽  
Central Data

This article deals with the pyramidal framework designed to be used in the parallelization of the ant-like algorithms. Such algorithms have several things in common: they run in cycles and the process can be divided among different "processing units". The parallel implementation of the Ant Colony Optimization algorithm for the Traveling Salesman Problem is an application of this system. The topology of the framework architecture is similar to a B-tree and contains three types of processing nodes: a single master (the root), several sub-masters corresponding to the internal nodes of the tree and several slaves as leaves. First the master reads the problem instance, wraps it up in a message that is sent to all the other processing nodes and initializes the central data structures. Then, the slaves take over the control by starting the algorithm while the master and the sub-masters are waiting for requests to update the data. The framework has an object-oriented design and was implemented in C++, using the MPI library.

Omicron ACO. A New Ant Colony Optimization Algorithm

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Benjamın Baran ◽  
Osvaldo Gomez
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Problems ◽  
Population Based ◽  
Analytical Tool ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Ant Colonies ◽  
Combinatorial Optimization Problems ◽  
The Traveling Salesman Problem

Ant Colony Optimization (ACO) is a metaheuristic inspired by the foraging behavior of ant colonies that has been successful in the resolution of hard combinatorial optimization problems like the Traveling Salesman Problem (TSP). This paper proposes the Omicron ACO (OA), a novel population-based ACO alternative originally designed as an analytical tool. To experimentally prove OA advantages, this work compares the behavior between the OA and the MMAS as a function of time in two well-known TSP problems. A simple study of the behavior of OA as a function of its parameters shows its robustness.

scholarly journals Opposition-Based Ant Colony Optimization Algorithm for the Traveling Salesman Problem

Mathematics ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1650
Author(s):  
Zhaojun Zhang ◽  
Zhaoxiong Xu ◽  
Shengyang Luan ◽  
Xuanyu Li ◽  
Yifei Sun
Keyword(s):  
Ant Colony Optimization ◽  
Traveling Salesman Problem ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Combinatorial Optimization Problems ◽  
Opposition Based Learning ◽  
The Traveling Salesman Problem

Opposition-based learning (OBL) has been widely used to improve many swarm intelligent optimization (SI) algorithms for continuous problems during the past few decades. When the SI optimization algorithms apply OBL to solve discrete problems, the construction and utilization of the opposite solution is the key issue. Ant colony optimization (ACO) generally used to solve combinatorial optimization problems is a kind of classical SI optimization algorithm. Opposition-based ACO which is combined in OBL is proposed to solve the symmetric traveling salesman problem (TSP) in this paper. Two strategies for constructing opposite path by OBL based on solution characteristics of TSP are also proposed. Then, in order to use information of opposite path to improve the performance of ACO, three different strategies, direction, indirection, and random methods, mentioned for pheromone update rules are discussed individually. According to the construction of the inverse solution and the way of using it in pheromone updating, three kinds of improved ant colony algorithms are proposed. To verify the feasibility and effectiveness of strategies, two kinds of ACO algorithms are employed to solve TSP instances. The results demonstrate that the performance of opposition-based ACO is better than that of ACO without OBL.

scholarly journals Meta-Heuristic Algorithm based on Ant Colony Optimization Algorithm and Project Scheduling Problem (PSP) for the Traveling Salesman Problem

2021 ◽  
Vol 15 ◽  
pp. 68-77
Author(s):  
Alejandro Fuentes-Penna ◽  
Jorge A. Ruiz-Vanoye ◽  
Marcos S. González-Ramírez
Keyword(s):  
Ant Colony Optimization ◽  
Traveling Salesman Problem ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Time Cost ◽  
Ant Colony Optimization Algorithm ◽  
Main Target ◽  
Project Scheduling Problem ◽  
Time Distance ◽  
The Traveling Salesman Problem

The main target of Traveling Salesman Problem (TSP) is to construct the path with the lowest time between different cities, visiting every one once. The Scheduling Project Ant Colony Optimization (SPANCO) Algorithm proposes a way to solve TSP problems adding three aspects: time, cost effort and scope, where the scope is the number of cities, the effort is calculated multiplying time, distance and delivering weight factors and dividing by the sum of them and optimizing the best way to visit the cities graph.

scholarly journals Modeling of transportation logistics processes for the urban environment

2019 ◽  
Vol 91 ◽  
pp. 05021
Author(s):  
Petr Romanov ◽  
Irina Romanova
Keyword(s):  
Traveling Salesman Problem ◽  
Minimum Spanning Tree ◽  
Heuristic Algorithms ◽  
Road Transport ◽  
Traveling Salesman ◽  
Ant Colony Optimization Algorithm ◽  
Software Environment ◽  
Greedy Approach ◽  
Trading Network ◽  
The Traveling Salesman Problem

The article deals with the approach to modeling the road transport movement in large cities (with a population of over 100 thousand people) for the delivery of goods from a large warehouse to stores belonging to a trading network company, with the task of optimizing these movements. The main purposes of this optimization task are: to reduce the transportation time; to conduct a rational distribution of vehicles; to reduce the number of required vehicles involved in these transportations; to reduce operating costs for the maintenance of vehicles. The problem statement and its solution on the basis of heuristic algorithms of the Traveling Salesman Problem are given. The article presents a comparative analysis of the most popular methods for solving the Traveling Salesman Problem (Greedy Approach, Modified Greedy Approach, Minimum Spanning Tree, Monte Carlo Simplification Model, Ant Colony Optimization, Algorithm of Little) on the basis of experimental research and simulation. As a result of the analysis, it is proposed to use the Algorithm of Little for optimizing of road transport movement in the delivery of goods. The article provides an example of solving a specific problem using the developed calculation procedure and a computer program “Traveling Salesman Problem” (developed in Pascal in the software environment Delphi 7).

scholarly journals Developing Programming Tools to Handle Traveling Salesman Problem by the Three Object-Oriented Languages

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Hassan Ismkhan ◽  
Kamran Zamanifar
Keyword(s):  
Traveling Salesman Problem ◽  
State Of The Art ◽  
Hybrid Genetic Algorithm ◽  
Object Oriented ◽  
Traveling Salesman ◽  
Abstract Data Type ◽  
Programming Tools ◽  
Abstract Data ◽  
The Traveling Salesman Problem ◽  
Object Oriented Languages

The traveling salesman problem (TSP) is one of the most famous problems. Many applications and programming tools have been developed to handle TSP. However, it seems to be essential to provide easy programming tools according to state-of-the-art algorithms. Therefore, we have collected and programmed new easy tools by the three object-oriented languages. In this paper, we present ADT (abstract data type) of developed tools at first; then we analyze their performance by experiments. We also design a hybrid genetic algorithm (HGA) by developed tools. Experimental results show that the proposed HGA is comparable with the recent state-of-the-art applications.

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