Results 1  10
of
19
On the runtime analysis of the 1ANT ACO algorithm
 IN GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, GECCO 2007, PROCEEDINGS
, 2007
"... The runtime analysis of randomized search heuristics is a growing field where, in the last two decades, many rigorous results have been obtained. These results, however, apply particularly to classical search heuristics such as Evolutionary Algorithms (EAs) and Simulated Annealing. First runtime ana ..."
Abstract

Cited by 17 (11 self)
 Add to MetaCart
(Show Context)
The runtime analysis of randomized search heuristics is a growing field where, in the last two decades, many rigorous results have been obtained. These results, however, apply particularly to classical search heuristics such as Evolutionary Algorithms (EAs) and Simulated Annealing. First runtime analyses of modern search heuristics have been conducted only recently w. r. t. a simple Ant Colony Optimization (ACO) algorithm called 1ANT. In particular, the influence of the evaporation factor in the pheromone update mechanism and the robustness of this parameter w. r. t. the runtime behavior have been determined for the example function OneMax. This paper puts forward the rigorous runtime analysis of the 1ANT on example functions, namely on the functions LeadingOnes and BinVal. With respect to EAs, such analyses have been essential to develop methods for the analysis on more complicated problems. The proof techniques required for the 1ANT, unfortunately, differ significantly from those for EAs, which means that a new reservoir of methods has to be built up. Again, the influence of the evaporation factor is analyzed rigorously, and it is proved that its choice can be very crucial to allow efficient runtimes. Moreover, the analyses provide insight into the working principles of ACO algorithms and, in terms of their robustness, describe essential differences to other randomized search heuristics.
Rigorous Analyses of FitnessProportional Selection for Optimizing Linear Functions
"... Rigorous runtime analyses of evolutionary algorithms (EAs) mainly investigate algorithms that use elitist selection methods. Two algorithms commonly studied are Randomized Local Search (RLS) and the (1+1) EA and it is well known that both optimize any linear pseudoBoolean function on n bits within ..."
Abstract

Cited by 14 (4 self)
 Add to MetaCart
(Show Context)
Rigorous runtime analyses of evolutionary algorithms (EAs) mainly investigate algorithms that use elitist selection methods. Two algorithms commonly studied are Randomized Local Search (RLS) and the (1+1) EA and it is well known that both optimize any linear pseudoBoolean function on n bits within an expected number of O(n log n) fitness evaluations. In this paper, we analyze variants of these algorithms that use fitness proportional selection. A wellknown method in analyzing the local changes in the solutions of RLS is a reduction to the gambler’s ruin problem. We extend this method in order to analyze the global changes imposed by the (1+1) EA. By applying this new technique we show that with high probability using fitness proportional selection leads to an exponential optimization time for any linear pseudoBoolean function with nonzero weights. Even worse, all solutions of the algorithms during an exponential number of fitness evaluations differ with high probability in linearly many bits from the optimal solution. Our theoretical studies are complemented by experimental investigations which confirm the asymptotic results on realistic input sizes.
Refined runtime analysis of a basic ant colony optimization algorithm
 In IEEE Congress on Evolutionary Computation 2007
, 2007
"... Neumann and Witt (2006) analyzed the runtime of the basic ant colony optimization (ACO) algorithm 1Ant on pseudoboolean optimization problems. For the problem OneMax they showed how the runtime depends on the evaporation factor. In particular, they proved a phase transition from exponential to poly ..."
Abstract

Cited by 11 (1 self)
 Add to MetaCart
(Show Context)
Neumann and Witt (2006) analyzed the runtime of the basic ant colony optimization (ACO) algorithm 1Ant on pseudoboolean optimization problems. For the problem OneMax they showed how the runtime depends on the evaporation factor. In particular, they proved a phase transition from exponential to polynomial runtime. In this work, we simplify the view on this problem by an appropriate translation of the pheromone model. This results in a profound simplification of the pheromone update rule and, by that, a refinement of the results of Neumann and Witt. In particular, we show how the exponential runtime bound gradually changes to a polynomial bound inside the phase of transition. 1
Mathematical runtime analysis of aco algorithms: survey on an emerging issue
 Swarm Intelligence
, 1999
"... Abstract: The paper gives an overview on the status of the theoretical analysis of Ant Colony Optimization (ACO) algorithms, with a special focus on the analytical investigation of the runtime required to find an optimal solution to a given combinatorial optimization problem. First, a general framew ..."
Abstract

Cited by 8 (0 self)
 Add to MetaCart
(Show Context)
Abstract: The paper gives an overview on the status of the theoretical analysis of Ant Colony Optimization (ACO) algorithms, with a special focus on the analytical investigation of the runtime required to find an optimal solution to a given combinatorial optimization problem. First, a general framework for studying questions of this type is presented, and three important ACO variants are recalled within this framework. Secondly, two classes of formal techniques for runtime investigations of the considered type are outlined. Finally, some available runtime complexity results for ACO variants, referring to elementary test problems that have been introduced in the theoretical literature on evolutionary algorithms, are cited and discussed. 1
Running Time Analysis of Ant Colony Optimization for Shortest Path Problems
, 2011
"... Ant Colony Optimization (ACO) is a modern and very popular optimization paradigm inspired by the ability of ant colonies to find shortest paths between their nest and a food source. Despite its popularity, the theory of ACO is still in its infancy and a solid theoretical foundation is needed. We pre ..."
Abstract

Cited by 5 (4 self)
 Add to MetaCart
Ant Colony Optimization (ACO) is a modern and very popular optimization paradigm inspired by the ability of ant colonies to find shortest paths between their nest and a food source. Despite its popularity, the theory of ACO is still in its infancy and a solid theoretical foundation is needed. We present bounds on the running time of different ACO systems for shortest path problems. First, we improve previous results by Attiratanasunthron and Fakcharoenphol [Information Processing Letters, 105(3):88–92, 2008] for singledestination shortest paths and extend their results from DAGs to arbitrary directed graphs. Our upper bound is asymptotically tight for large evaporation factors, holds with high probability, and transfers to the allpairs shortest paths problem. There, a simple mechanism for exchanging information between ants with different destinations yields a significant improvement. A comparison with evolutionary and genetic approaches indicates that ACO is among the best known metaheuristics for the allpairs shortest paths problem.
Running Time Analysis of ACO Systems for Shortest Path Problems
"... Ant Colony Optimization (ACO) is inspired by the ability of ant colonies to find shortest paths between their nest and a food source. We analyze the running time of different ACO systems for shortest path problems. First, we improve running time bounds by Attiratanasunthron and Fakcharoenphol [Infor ..."
Abstract

Cited by 4 (2 self)
 Add to MetaCart
(Show Context)
Ant Colony Optimization (ACO) is inspired by the ability of ant colonies to find shortest paths between their nest and a food source. We analyze the running time of different ACO systems for shortest path problems. First, we improve running time bounds by Attiratanasunthron and Fakcharoenphol [Information Processing Letters, 105(3):88–92, 2008] for singledestination shortest paths and extend their results for acyclic graphs to arbitrary graphs. Our upper bound is asymptotically tight for large evaporation factors, holds with high probability, and transfers to the allpairs shortest paths problem. There, a simple mechanism for exchanging information between ants with different destinations yields a significant improvement. Our results indicate that ACO is the best known metaheuristic for the allpairs shortest paths problem.
Simple maxmin ant systems and the optimization of linear pseudoboolean functions
 Proc. of Foundations of Genetic Algorithms (FOGA
, 2011
"... With this paper, we contribute to the understanding of ant colony optimization (ACO) algorithms by formally analyzing their runtime behavior. We study simple MAXMIN ant systems on the class of linear pseudoBoolean functions defined on binary strings of length n. Our investigations point out how ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
(Show Context)
With this paper, we contribute to the understanding of ant colony optimization (ACO) algorithms by formally analyzing their runtime behavior. We study simple MAXMIN ant systems on the class of linear pseudoBoolean functions defined on binary strings of length n. Our investigations point out how the progress according to function values is stored in the pheromones. We provide a general upper bound of O((n3 logn)/ρ) on the running time for two ACO variants on all linear functions, where ρ determines the pheromone update strength. Furthermore, we show improved bounds for two wellknown linear pseudoBoolean functions called OneMax and BinVal and give additional insights using an experimental study.
Implementation of Different Ant based Techniques for Network Load Analysis
, 2013
"... Network Load balancing is a technique of balancing at each node the number of packets received and the number of packets forward to the other node so that the chance of network congestion problem has been reduced and bandwidth is utilized. Although there are many techniques implemented for the balan ..."
Abstract
 Add to MetaCart
(Show Context)
Network Load balancing is a technique of balancing at each node the number of packets received and the number of packets forward to the other node so that the chance of network congestion problem has been reduced and bandwidth is utilized. Although there are many techniques implemented for the balancing of nodes based on maintaining a routing table at each node and is updated as the packet get forward from that node. Ant Colony Optimization is one of the techniques used in the network for the balancing of number of packets at each node. Here in this paper is proposed a comparative study of different ant colony optimization techniques implemented for the analysis of the network load balancing. Here the ant based techniques are implemented are simulated for different conditions and on the basis of which proposed the best ant based techniques for the network load balancing.