Modelling the Social Interactions in Ant Colony Optimization

2019 ◽  
pp. 216-224
Author(s):  
Nishant Gurrapadi ◽  
Lydia Taw ◽  
Mariana Macedo ◽  
Marcos Oliveira ◽  
Diego Pinheiro ◽  
...  
Keyword(s):  
Social Interactions ◽  
Ant Colony Optimization ◽  
Ant Colony ◽  
The Social

ACO powered by Local Searches Algorithms for the Solution of TSP Problems

2020 ◽  
Vol 1 (1) ◽  
pp. 1-10
Author(s):  
Nisreen L. Ahmed
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Technique ◽  
General Purpose ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
New Approach ◽  
Local Searches ◽  
Probabilistic Technique ◽  
The Social ◽  
Great Deluge Algorithm

Swarm intelligence is a relatively new approach to problem solving that takes inspiration from the social behaviors of insects and other animals. Ants, in particular, have inspired a number of methods and techniques among which the most studied and successful is the general-purpose optimization technique, also known as ant colony optimization, In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs.  Ant Colony Optimization (ACO) algorithm is used to arrive at the best solution for TSP. In this article, the researcher has introduced ways to use a great deluge algorithm with the ACO algorithm to increase the ability of the ACO in finding the best tour (optimal tour). Results are given for different TSP problems by using ACO with great deluge and other local search algorithms.

An Improved Personnel Evacuation Cellular Automata Model Based on the Ant Colony Optimization Algorithm

2014 ◽  
Vol 513-517 ◽  
pp. 3287-3291 ◽  
Author(s):  
Dan Qing Wang ◽  
Qing Ge Gong ◽  
Xiao Fei Shen
Keyword(s):  
Cellular Automata ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Evacuation Simulation ◽  
Simulation Research ◽  
Cellular Automata Model ◽  
The Social ◽  
Improved Model

Nowadays, public safety has already attracted great attention, especially when natural disasters and other emergencies happen more and more frequently. So, personnel evacuation simulation research in the populated areas has become one of the core issues to reduce the social damage. To improve the simulation theory, this paper puts forward an improved cellular automata model using some idea of the classic Ant Colony Optimization Algorithm for reference when making rules for the evacuating personnel. And the improved model takes the interaction among the crowd and the influences exerted by the evacuating personnel upon the environment into account. The new model cares more specific details of both environment and the personnel, so it simulates the crowd psychology successfully and provides a more reliable theory that is to expand and improve the cellular automaton simulation model on personnel evacuation.

scholarly journals Cryptanalysis of Merkle-Hellman cipher using ant colony optimization

2021 ◽  
Vol 10 (2) ◽  
pp. 490
Author(s):  
Hicham Grari ◽  
Siham Lamzabi ◽  
Ahmed Azouaoui ◽  
Khalid Zine-Dine
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony ◽  
Public Key ◽  
Excellent Performance ◽  
Swarm Optimization ◽  
Ant Colonies ◽  
Public Key Cryptosystems ◽  
The Social ◽  
Subset Sum ◽  
Np Hard Problem

<p class="Abstract"><span id="docs-internal-guid-d3fe8e21-7fff-17fc-df0e-00893428243c"><span>The Merkle-Hellman (MH) cryptosystem is one of the earliest public key cryptosystems, which is introduced by Ralph Merkle and Martin Hellman in 1978 based on an NP-hard problem, known as the subset-sum problem. Furthermore, ant colony optimization (ACO) is one of the most nature-inspired meta-heuristic optimization, which simulates the social behaviour of ant colonies. ACO has demonstrated excellent performance in solving a wide variety of complex problems. In this paper, we present a novel ant colony optimization (ACO) based attack for cryptanalysis of MH cipher algorithm, where two different search techniques are used. Moreover, experimental study is included, showing the effectiveness of the proposed attacking scheme. The results show that ACO based attack is more suitable than many other algorithms like genetic algorithm (GA) and particle swarm optimization (PSO).</span></span></p>

Parallel Ant Colony Optimization on the University Course-Faculty Timetabling Problem in MSU-IIT

2012 ◽  
Author(s):  
Earth B. Ugat ◽  
Jennifer Joyce M. Montemayor ◽  
Mark Anthony N. Manlimos ◽  
Dante D. Dinawanao
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony ◽  
Timetabling Problem ◽  
University Course ◽  
The University

Achievement of Cost-Effective Datasets Transference in The Cloud Through Ant Colony Optimization

2012 ◽  
Vol 3 (3) ◽  
pp. 122-125
Author(s):  
THAHASSIN C THAHASSIN C ◽  
◽  
A. GEETHA A. GEETHA ◽  
RASEEK C RASEEK C
Keyword(s):  
Ant Colony Optimization ◽  
Cost Effective ◽  
Ant Colony

Value-driven issues throughout the development of sociological theory

2020 ◽  
pp. 147-160
Author(s):  
Gulbarshyn Chepurko ◽  
Valerii Pylypenko
Keyword(s):  
Social Interactions ◽  
Social System ◽  
Sociological Theory ◽  
Social Scientists ◽  
Social Sphere ◽  
Technological Advances ◽  
The Social ◽  
The Individual ◽  
Different Levels

The paper examines and compares how the major sociological theories treat axiological issues. Value-driven topics are analysed in view of their relevance to society in times of crisis, when both societal life and the very structure of society undergo dramatic change. Nowadays, social scientists around the world are also witnessing such a change due to the emergence of alternative schools of sociological thought (non-classical, interpretive, postmodern, etc.) and, subsequently, the necessity to revise the paradigms that have been existed in sociology so far. Since the above-mentioned approaches are often used to address value-related issues, building a solid theoretical framework for these studies takes on considerable significance. Furthermore, the paradigm revision has been prompted by technological advances changing all areas of people’s lives, especially social interactions. The global human community, integral in nature, is being formed, and production of human values now matters more than production of things; hence the “expansion” of value-focused perspectives in contemporary sociology. The authors give special attention to collectivities which are higher-order units of the social system. These units are described as well-organised action systems where each individual performs his/her specific role. Just as the role of an individual is distinct from that of the collectivity (because the individual and the collectivity are different as units), so too a distinction is drawn between the value and the norm — because they represent different levels of social relationships. Values are the main connecting element between the society’s cultural system and the social sphere while norms, for the most part, belong to the social system. Values serve primarily to maintain the pattern according to which the society is functioning at a given time; norms are essential to social integration. Apart from being the means of regulating social processes and relationships, norms embody the “principles” that can be applied beyond a particular social system. The authors underline that it is important for Ukrainian sociology to keep abreast of the latest developments in the field of axiology and make good use of those ideas because this is a prerequisite for its successful integration into the global sociological community.

The Map-Based Shortest Route Selection by Using Ant Colony Optimization Algorithm

2018 ◽  
Vol 1 (1) ◽  
pp. 15
Author(s):  
Achmad Fanany Onnilita Gaffar ◽  
Agusma Wajiansyah ◽  
Supriadi Supriadi
Keyword(s):  
Ant Colony Optimization ◽  
Shortest Path ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Google Earth ◽  
Ant Colony ◽  
Food Sources ◽  
Shortest Route ◽  
Study Results ◽  
Destination Point

The shortest path problem is one of the optimization problems where the optimization value is a distance. In general, solving the problem of the shortest route search can be done using two methods, namely conventional methods and heuristic methods. The Ant Colony Optimization (ACO) is the one of the optimization algorithm based on heuristic method. ACO is adopted from the behavior of ant colonies which naturally able to find the shortest route on the way from the nest to the food sources. In this study, ACO is used to determine the shortest route from Bumi Senyiur Hotel (origin point) to East Kalimantan Governor's Office (destination point). The selection of the origin and destination points is based on a large number of possible major roads connecting the two points. The data source used is the base map of Samarinda City which is cropped on certain coordinates by using Google Earth app which covers the origin and destination points selected. The data pre-processing is performed on the base map image of the acquisition results to obtain its numerical data. ACO is implemented on the data to obtain the shortest path from the origin and destination point that has been determined. From the study results obtained that the number of ants that have been used has an effect on the increase of possible solutions to optimal. The number of tours effect on the number of pheromones that are left on each edge passed ant. With the global pheromone update on each tour then there is a possibility that the path that has passed the ant will run out of pheromone at the end of the tour. This causes the possibility of inconsistent results when using the number of ants smaller than the number of tours.

Modeling and optimizing logistics, information, economic and financial intercluster cooperation using the ant colony optimization algorithm

2020 ◽  
Vol 26 (11) ◽  
pp. 2427-2447
Author(s):  
S.N. Yashin ◽  
E.V. Koshelev ◽  
S.A. Borisov
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Population Decline ◽  
Innovation System ◽  
Federal District ◽  
Original Model ◽  
Ant Colony ◽  
Industrial Agglomeration ◽  
Ant Colony Optimization Algorithm

Subject. This article discusses the issues related to the creation of a technology of modeling and optimization of economic, financial, information, and logistics cluster-cluster cooperation within a federal district. Objectives. The article aims to propose a model for determining the optimal center of industrial agglomeration for innovation and industry clusters located in a federal district. Methods. For the study, we used the ant colony optimization algorithm. Results. The article proposes an original model of cluster-cluster cooperation, showing the best version of industrial agglomeration, the cities of Samara, Ulyanovsk, and Dimitrovgrad, for the Volga Federal District as a case study. Conclusions. If the industrial agglomeration center is located in these three cities, the cutting of the overall transportation costs and natural population decline in the Volga Federal District will make it possible to qualitatively improve the foresight of evolution of the large innovation system of the district under study.

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