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Runtime Analysis of a Simple Ant Colony Optimization Algorithm

Frank Neumann, Carsten Witt
2007 Algorithmica  
We present the first runtime analysis of an ACO algorithm, which transfers many rigorous results with respect to the runtime of a simple evolutionary algorithm to our algorithm.  ...  Ant Colony Optimization (ACO) has become quite popular in recent years. In contrast to many successful applications, the theoretical foundation of this randomized search heuristic is rather weak.  ...  Like EAs, these heuristics imitate optimization processes from nature, in this case the search of an ant colony for a common source of food.  ... 
doi:10.1007/s00453-007-9134-2 fatcat:mmoejhm65nb3vihy54j4lygyxm

Runtime Analysis of a Simple Ant Colony Optimization Algorithm [chapter]

Frank Neumann, Carsten Witt
2006 Lecture Notes in Computer Science  
We present the first runtime analysis of an ACO algorithm, which transfers many rigorous results with respect to the runtime of a simple evolutionary algorithm to our algorithm.  ...  Ant Colony Optimization (ACO) has become quite popular in recent years. In contrast to many successful applications, the theoretical foundation of this randomized search heuristic is rather weak.  ...  Like EAs, these heuristics imitate optimization processes from nature, in this case the search of an ant colony for a common source of food.  ... 
doi:10.1007/11940128_62 fatcat:x4wcsy4kybezvncehjbuq27mfy

Special issue on ant colony optimization

Karl F. Doerner, Daniel Merkle, Thomas Stützle
2008 Swarm Intelligence  
Ant Colony Optimization (ACO) is one of the most successful techniques in the wider field of swarm intelligence.  ...  ACO is inspired by the pheromone trail laying and following behavior of some ant species, a behavior that was shown to allow real ant colonies to find shortest paths between their colony and food sources  ...  Acknowledgments The production of a special issue would not be possible without the help of a number of people.  ... 
doi:10.1007/s11721-008-0025-1 fatcat:4v6ctj2qbncy5cfm4j6efhsfxe

Theory of swarm intelligence

Dirk Sudholt
2012 Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion - GECCO Companion '12  
The collective intelligence of such animals is known as swarm intelligence and it has inspired popular and very powerful optimization paradigms, including ant colony optimization (ACO) and particle swarm  ...  Remarkably, these animals in many cases use very simple, decentralized communication mechanisms that do not require a single leader.  ...  The tutorial will be divided into a first, larger part on ACO and a second, smaller part on PSO. For ACO we will consider simple variants of the MAX-MIN ant system.  ... 
doi:10.1145/2330784.2330938 dblp:conf/gecco/Sudholt12a fatcat:waymu6febngwrfn4qndrtzjcbi

OPTIMIZED PARTICLE SWARM OPTIMIZATION BASED DEADLINE CONSTRAINED TASK SCHEDULING IN HYBRID CLOUD

Dhananjay Kumar, Kavitha B., Padmavathy M., Harshini B., Preethi E., Varalakshmi P.
2016 ICTACT Journal on Soft Computing  
The proposed algorithm called Optimized Particle Swarm Optimization (OPSO) combines the two metaheuristic algorithms namely Particle Swarm Optimization and Ant Colony Optimization (ACO).  ...  These metaheuristic algorithms are used for the purpose of optimization in the search space of the required solution, to find the best resource from the pool of resources and to obtain maximum profit even  ...  ACKNOWLEDGEMENT We would like to express our sincere thanks and deep sense of gratitude to the Department of Information Technology, Anna University, MIT Campus, Chennai for providing necessary infrastructure  ... 
doi:10.21917/ijsc.2016.0155 fatcat:22nbevlktvfclpuqyzrsirwlxm

Runtime analysis of the 1-ANT ant colony optimizer

Benjamin Doerr, Frank Neumann, Dirk Sudholt, Carsten Witt
2011 Theoretical Computer Science  
First runtime analyses of ant colony optimization (ACO) have been conducted only recently. In these studies simple ACO algorithms such as the 1-ANT are investigated.  ...  This work puts forward the rigorous runtime analysis of the 1-ANT on the example functions LeadingOnes and BinVal.  ...  Acknowledgements The third and fourth authors were partly supported by the German Science Foundation (DFG) as a part of the Collaborative Research Center ''Computational Intelligence'' (SFB 531).  ... 
doi:10.1016/j.tcs.2010.12.030 fatcat:7olxmsv3evbexoezmwdlbzatru

Dynamically Updating the Exploiting Parameter in Improving Performance of Ant-Based Algorithms [chapter]

Hoang Trung Dinh, Abdullah Al Mamun, Hieu T. Dinh
2005 Lecture Notes in Computer Science  
The utilization of pseudo-random proportional rule to balance between the exploitation and exploration of the search process was shown in Ant Colony System (ACS) algorithm.  ...  In ACS, this rule is governed by a parameter so-called exploiting parameter which is always set to a constant value. Besides, all  ...  ACO-based algorithm either omit this rule or applying it with a fixed value of the exploiting parameter during the runtime of algorithms.  ... 
doi:10.1007/11496199_37 fatcat:jnig74hacjgwpcc5xmimtmwdya

Ant Colony Optimization and the Minimum Spanning Tree Problem [chapter]

Frank Neumann, Carsten Witt
2008 Lecture Notes in Computer Science  
We present the first comprehensive rigorous analysis of a simple ACO algorithm for a combinatorial optimization problem.  ...  Ant Colony Optimization (ACO) is a kind of metaheuristic that has become very popular for solving problems from combinatorial optimization.  ...  We present the first comprehensive rigorous analysis of a simple ACO algorithm for a combinatorial optimization problem.  ... 
doi:10.1007/978-3-540-92695-5_12 fatcat:ffqgehgspvcf5gtpx5eq6ttarq

Ant Colony Optimization and the minimum spanning tree problem

Frank Neumann, Carsten Witt
2010 Theoretical Computer Science  
We present the first comprehensive rigorous analysis of a simple ACO algorithm for a combinatorial optimization problem.  ...  Ant Colony Optimization (ACO) is a kind of metaheuristic that has become very popular for solving problems from combinatorial optimization.  ...  We present the first comprehensive rigorous analysis of a simple ACO algorithm for a combinatorial optimization problem.  ... 
doi:10.1016/j.tcs.2010.02.012 fatcat:cxlb3dl7k5gf7fsyp7t4tdmnom

Using ACO Metaheuristic for MWT Problem

M. G. Dorzan, E. O. Gagliardi, M. G. Leguizamon, G. H. Penalver
2011 2011 30th International Conference of the Chilean Computer Science Society  
This paper shows how the Ant Colony Optimization (ACO) metaheuristic can be used to find high quality triangulations.  ...  Globally optimal triangulations are difficult to be found by deterministic methods as, for most type of criteria, no polynomial algorithm is known.  ...  ANT COLONY OPTIMIZATION METAHEURISTIC -ACO Ant Colony Optimization [5] is a metaheuristic approach for solving hard combinatorial optimization problems.  ... 
doi:10.1109/sccc.2011.30 dblp:conf/sccc/DorzanGLH11 fatcat:btiu74lhajdq5m5uv6zyr7of3a

Theory of Randomized Search Heuristics

Anne Auger, Carsten Witt
2012 Algorithmica  
The paper A Simple Ant Colony Optimizer for Stochastic Shortest Path Problems by Sudholt and Thyssen (doi:10.1007/s00453-011-9606-2) puts forward the running time analysis of ant colony optimization in  ...  By shedding light on a fundamental design principle of CMA-ES and linking it to a broader class of algorithms, this theoretical result certainly explains a big part of the success of the CMA-ES algorithm  ... 
doi:10.1007/s00453-012-9686-7 fatcat:zknf7fjutvcobcevqjvd6swvvy

Refined runtime analysis of a basic ant colony optimization algorithm

Benjamin Doerr, Daniel Johannsen
2007 2007 IEEE Congress on Evolutionary Computation  
analyzed the runtime of the basic ant colony optimization (ACO) algorithm 1-Ant on pseudoboolean optimization problems.  ...  In particular, we show how the exponential runtime bound gradually changes to a polynomial bound inside the phase of transition.  ...  The technique of ACO is based on a natural optimization process, namely the search of an ant colony for a shortest path to a source of food.  ... 
doi:10.1109/cec.2007.4424512 dblp:conf/cec/DoerrJ07 fatcat:znix5ylbgjc4djssegfkdagmny

Swarm intelligence theory: A snapshot of the state of the art

Eric Bonabeau, David Corne, Joshua Knowles, Riccardo Poli
2010 Theoretical Computer Science  
There have been many attempts already to undertake (for example) runtime analyses of simplified versions of ant colony optimization, for simple optimization landscapes.  ...  For ant colony optimization in particular, Dorigo and Blum [1] provides a comprehensive recent account of theoretical progress.  ...  this framework directly to ant colony optimization algorithms, which usually incorporate sophistications such as local search and heuristic bias that confound the analysis.  ... 
doi:10.1016/j.tcs.2010.03.001 fatcat:ha3vq5asynazhd5fdbqupa5my4

A Pheromone-Rate-Based Analysis on the Convergence Time of ACO Algorithm

Han Huang, Chun-Guo Wu, Zhi-Feng Hao
2009 IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)  
, convergence proofs, and runtime analysis.  ...  Ant colony optimization (ACO) has widely been applied to solve combinatorial optimization problems in recent years.  ...  Yao for his general introduction and comment to the primary result of the presented paper in the international conference SEAL 2006 and Dr. X. W. Yang for his suggestion on the revision of this paper.  ... 
doi:10.1109/tsmcb.2009.2012867 pmid:19380276 fatcat:3ckjsmfew5gqjopa52nwygnuj4

A review: bug localization using ant colony optimization

2016 International Journal of Latest Trends in Engineering and Technology  
From our observations, we conclude that by using Ant colony Optimization technique we can easily localize the shortest path for finding a bug and obtain minimum reach time by optimizing the path of a bug  ...  ANT COLONY OPTIMIZATION FLOWCHART (ACO) A scheme of an ACO flowchart in figure 1 is given in the following. 1. Represent the solution space by a construction graph. 2.  ... 
doi:10.21172/1.74.022 fatcat:doxpigspxba6hnxfcohosbib34
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