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Investigating pedestrian evacuation using ant algorithms

PRAMANA-JOURNAL OF PHYSICS 2018年第5期91卷 62-62页

作者： Gokce, Sibel Cetin, Ahmet Kibar, Rana Manisa Celal Bayar Univ Fac Arts & Sci Dept Phys TR-45140 Muradiye Manisa Turkey

ants communicate with each other by depositing a chemical called pheromone on the substrate while they crawl forward. By this way, they follow their predecessor and large trail systems are built. Inspired by the communication via chemical signals of ants, we have proposed a model to investigate the collective motion in humans during an emergency. It is considered that pedestrians use some kind of virtual chemotaxis to find the shortest way to the exit. This basic idea is implemented with the floor field model which is the most popular cellular automata model. The dependence of the evacuation from a room on the virtual chemotaxis evaporating rate f and the presence of the obstacle are investigated in this paper. The simulation results show that the increase in evaporation rate has been seen to slow down the evacuation. Moreover, it is found that positioning the obstacles in the room could lead to the phase transitions and decrease the evacuation time.

关键词： Emergency evacuation collective motion ant algorithm cellular automata

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Orthogonal Methods Based ant Colony Search for Solving Continuous Optimization Problems

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Journal of Computer Science & Technology 2008年第1期23卷 2-18页

作者：胡晓敏张军李耘 Department of Computer Science Sun Yat-Sen University Department of Electronics and Electrical Engineering University of Glasgow

Research into ant colony algorithms for solving continuous optimization problems forms one of the most significant and promising areas in swarm computation. Although traditional ant algorithms are designed for combinatorial optimization, they have shown great potential in solving a wide range of optimization problems, including continuous optimization. Aimed at solving continuous problems effectively, this paper develops a novel ant algorithm termed ＂continuous orthogonal ant colony＂（COAC）, whose pheromone deposit mechanisms would enable ants to search for solutions collaboratively and effectively. By using the orthogonal design method, ants in the feasible domain can explore their chosen regions rapidly and efficiently. By implementing an ＂adaptive regional radius＂ method, the proposed algorithm can reduce the probability of being trapped in local optima and therefore enhance the global search capability and accuracy. An elitist strategy is also employed to reserve the most valuable points. The performance of the COAC is compared with two other ant algorithms for continuous optimization -API and CACO by testing seventeen functions in the continuous domain. The results demonstrate that the proposed COAC algorithm outperforms the others.

关键词： ant algorithm function optimization orthogonal design

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Development Of Road Anomaly Data Transmission Using ant Colony Optimization algorithm in a Vehicle-To-Vehicle Communication

Development Of Road Anomaly Data Transmission Using Ant Colo...

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International Conference on Electronics, Computer and Computation (ICECCO)

作者： Muktar MT. Othman Steve A. Adeshina Moussa M. Boukar Department of Electrical and Electronics Engineering Nile University of Nigeria Abuja Nigeria Department of Computer Engineering Nile University of Nigeria Abuja Nigeria

ISBN: (数字)9781728151601

ISBN: (纸本)9781728151618

This study aim is to design a road anomaly transmission algorithms using ant Colony optimization (ACO) based Technique in a Vehicle-to-Vehicle (V2V) and Vehicle to Infrastructure (V2I) Communication. The developed VACO also uses the features of VANET to find out the optimal path by considering a minimum number of nodes and cost parameters, which provides information related to accidents, speed of neighbouring vehicle and weather to help users in making informed decisions. Vehicle routing protocol based on ACO (VACO) also ensures to mitigate issues by combining the reactive and proactive approach and considers the parameters affecting the Quality of Service (QoS) such as latency, bandwidth, and delivery ratio in evaluating the algorithms.

关键词： ant colony optimisation quality of service routing protocols vehicular ad hoc networks Quality of service V2V ant colony optimization Routing protocols Vehicle to vehicle communications VEHICULAR COMMUNICATIONS ant algorithm vehicle anomaly Road ADMINISTRATIVE CONSENT ORDER data transmission algorithms

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Research of a Self-adaptive Mixed-Variable Multi-objective ant Colony Optimization algorithm

Research of a Self-adaptive Mixed-Variable Multi-objective A...

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IEEE International Conference on Green Computing and Communications

作者： Yiguang GONG Weixue WANG Siqi GONG Jingsu Coborive Innovion Cener of Aosheric Environen nd Equien Technoogy (CICAEET) Schoo of Auoion Nnjing Universiy of Inforion Science TechnoogySchoo of Auoion Nnjing Universiy of Inforion Science TechnoogySchoo of Wer Resources nd Hydroower Engineering WuHn Universiy

ISBN: (数字)9781728176475

ISBN: (纸本)9781728176482

Multi-objective optimization (MOO) problems are often encountered in engineering calculations. Commonly used MOO algorithms include classical algorithms and modern heuristic algorithms. Modern heuristic algorithms can find the Pareto solution set, and are used most widely at present. This paper improves the ACOmv algorithm which is only suitable for solving mixed-variable single-objective optimization (SOO) problems, and proposes a MOACOmv algorithm suitable for solving mixed-variable MOO problems. And aiming at the dependence of MOACOmv algorithm performance on parameter setting, a SAMOACOmv algorithm using self-adaptive parameter setting scheme is proposed. Furthermore, the paper also designs one mixed-variable MOO benchmark for purpose to test and compare the performance of SAMOACOmv algorithm. The experiments indicate that SAMOACOmv algorithm has excellent comprehensive performance and is an ideal choice for solving mixed variable MOO problems.

关键词： Optimization Heuristic algorithms Partitioning algorithms Linear programming Mathematical model Search problems Probabilistic logic Heuristic algorithms Partitioning algorithms Probabilistic logic tabu search linear programming Mathematical Model algorithms Solution Set self-adaptive ant algorithm parameter setting

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Structural topology optimization using ant colony methodology

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ENGINEERING STRUCTURES 2008年第9期30卷 2559-2565页

作者： Kaveh, A. Hassani, B. Shojaee, S. Tavakkoli, S. M. Iran Univ Sci & Technol Dept Civil Engn Tehran 16 Iran

In this paper ant Colony Optimization (ACO) and finite element analysis are employed in topology optimization of 2D and 3D structural models. The aim is to find the stiffest structure with a certain amount of material, based on the element's contribution to the strain energy. The ACO algorithm provides a suitable tool to handle the problem as an on-off discrete optimization. In this method, the analogy with ants determining the shortest path to food is the decision for an element to be eliminated or to remain. The main effect of using this approach lies in the fact that the problems of topology optimization can be reformulated as discrete problems with no limitation. Examples of 2D and 3D models are provided to show the applicability of the present method. (c) 2008 Elsevier Ltd. All rights reserved.

关键词： ant algorithm topology optimization finite elements continuum

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Research on Tourist Route based on a Novel ant Colony Optimization algorithm

Research on Tourist Route based on a Novel Ant Colony Optimi...

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IEEE International Conference on Power, Intelligent Computing and Systems

作者： Naixin Yang Yuliang Shi School of Beijing University of Technology

ISBN: (纸本)9781728137216

ant Colony Optimization (ACO) algorithm is a kind of subject-based intelligent algorithm with positive feedback mechanism and strong ability to find and optimize. The travel recommendation system is recognized as one of the technologies that can effectively solve the problem of information overload. This paper introduces an improved ant colony algorithm based on multi-objective decision-making to recommend the travel route to meet the needs of users. In this algorithm, the ant uses the attraction rating information that meets the user's needs and the actual distance of the attraction to find the shortest path. In order to prove the accuracy of the algorithm recommendation, an experiment is conducted on a network of attractions. The experimental results show that the algorithm can provide high quality solutions and can be effectively applied in the travel recommendation system.

关键词： Travel recommendation Multi-objective decision ACO algorithm Attraction rating ant algorithm algorithms ant colony optimization Recommender systems actual distance travel Attraction shortest path Consumers multi-objective control Pareto optimisation feedback mechanism

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A routing cooperative selection method based on ant colony optimization algorithm

A routing cooperative selection method based on ant colony o...

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International Conference on Cyberspace Technology (CCT 2014)

作者： Sun Dandan Zhang Hongxin School of Electronic Engineering Beijing University of Posts and Telecommunications 100876 China Sch. of Electron. Eng. Beijing Univ. of Posts & Telecommun. Beijing China

ISBN: (纸本)9781510802704

Cooperative routing selection algorithm is based on the limited bandwidth and collaborative remain energy, according to pheromone concentration in ant colony optimization algorithm. The ant colony optimization algorithm is applied to improving collaboration network routing problems to reduce network congestion and reduce node energy consumption. Simulation demonstrate that the algorithm obtains a global optimization and makes an average 60% reduction in end to end delay, increased the utilization of network resources.

关键词： ant colony optimization algorithm Pheromone density The Cooperative routing ant algorithm ant colony optimization Network routing Web resources Optimization algorithms Network Congestion selection algorithm Global optimization algorithms selection methods node energy

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Efficient Transit Schedule Design of timing points: A comparison of ant Colony and Genetic algorithms

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TRANSPORTATION RESEARCH PART B-METHODOLOGICAL 2012年第1期46卷 217-234页

作者： Mazloumi, Ehsan Mesbah, Mahmoud Ceder, Avi Moridpour, Sara Currie, Graham Monash Univ Dept Civil Engn Inst Transport Studies Clayton Vic 3800 Australia Univ Auckland Civil & Environm Engn Dept Auckland 1 New Zealand RMIT Univ Sch Civil Environm & Chem Engn Melbourne Vic Australia Univ Queensland Sch Civil Engn Brisbane Qld 4072 Australia

This work defines Transit Schedule Design (TSD) as an optimization problem to construct the transit schedule with the decision variables of the location of timing points and the amount of slack time associated with each timing point. Two heuristic procedures, ant Colony and Genetic algorithms, are developed for constructing optimal schedules for a fixed bus route. The paper presents a comparison of the fundamental features of the two algorithms. They are then calibrated based on data generated from micro-simulation of a bus route in Melbourne, Australia, to give rise to (near) optimal schedule designs. The algorithms are compared in terms of their accuracy and efficiency in providing the minimum cost solution. Although both procedures prove the ability to find the optimal solution, the ant Colony procedure demonstrates a higher efficiency by evaluating less schedule designs to arrive at a 'good' solution. Potential benefits of the developed algorithms in bus route planning are also discussed. Crown Copyright (C) 2011 Published by Elsevier Ltd. All rights reserved.

关键词： Transit Schedule Design ant algorithm Genetic algorithm

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ant Colony Search algorithm for the Optimal Power Flow Problem

Ant Colony Search Algorithm for the Optimal Power Flow Probl...

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IEEE Power and Energy Society General Meeting

作者： J. Soares T. Sousa Z. A. Vale H. Morais P. Faria GECAD - Knowledge Engineering and Decision-Support Research Group of the Electrical Engineering Institute of Porto - Polytechnic Institute of Porto (ISEP/IPP)

ISBN: (纸本)9781457710001

To maintain a power system within operation limits, a level ahead planning it is necessary to apply competitive techniques to solve the optimal power flow (OPF). OPF is a non-linear and a large combinatorial problem. The ant Colony Search (ACS) optimization algorithm is inspired by the organized natural movement of real ants and has been successfully applied to different large combinatorial optimization problems. This paper presents an implementation of ant Colony optimization to solve the OPF in an economic dispatch context. The proposed methodology has been developed to be used for maintenance and repairing planning with 48 to 24 hours antecipation. The main advantage of this method is its low execution time that allows the use of OPF when a large set of scenarios has to be analyzed. The paper includes a case study using the IEEE 30 bus network. The results are compared with other well-known methodologies presented in the literature.

关键词： ant colony search Artificial intelligence Economic dispatch Optimal power flow Optimization method optimal load flow Artificial Intelligence ants execution time optimization method ant algorithm Bus networks ant colony optimization power systems SEARCHING algorithm

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Using PSO to improve ant colony optimization algorithm

Using PSO to improve ant colony optimization algorithm

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International Conference on Software Intelligence Technologies and Applications & International Conference on Frontiers of Internet of Things 2014

作者： Hong Guo Dandan Han Hongguo Zhang School of Software Harbin University of Science and Technology 150040 China

ant colony optimization (ACO) is a swarm intelligence algorithm and it has been successfully applied to several NP-hard combinatorial problems such as traveling salesman, quadratic assignment problem (QAP), job-shop scheduling, vehicle routing and telecommunication networks. Howere, the ants' solutions are not guaranteed to be optimal with respect to local changes. In this paper, an improved ACO algorithm is proposed. Particle swarm optimization (PSO) has been applied to improve the performances of ACO. ACO is firstly used to find optimal solutions. Then PSO is used to optimize local optimal solutions searched by ACO. In order to check the performance of the proposed method, the proposed algorithm is utilized to solve QAP. The improved ACO algorithm and ACO algorithm are respectively implemented on some instances extracted from QAPLIB. The experimental results demonstrate that the improved ACO algorithm has better performance in terms of the quality of the returned solution than the original ones.

关键词： ant Colony Optimization Optimal solutions Particle Swarm Optimization Quadratic Assignment Problem ant colony optimization optimal solution ADMINISTRATIVE CONSENT ORDER Particle swarm optimization parametric subharmonic oscillator algorithms channel assignment problem ant algorithm Telecommunication Networks

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