Future Generation Computer Systems
Swarm intelligence
Ant Colony Optimization
Ant colony optimization theory: a survey
Theoretical Computer Science
Worst case and probabilistic analysis of the 2-Opt algorithm for the TSP: extended abstract
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Information Processing Letters
First steps to the runtime complexity analysis of ant colony optimization
Computers and Operations Research
Runtime analysis of binary PSO
Proceedings of the 10th annual conference on Genetic and evolutionary computation
How Single Ant ACO Systems Optimize Pseudo-Boolean Functions
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Rigorous Analyses for the Combination of Ant Colony Optimization and Local Search
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
Ant Colony Optimization and the Minimum Spanning Tree Problem
Learning and Intelligent Optimization
Why standard particle swarm optimisers elude a theoretical runtime analysis
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Running Time Analysis of ACO Systems for Shortest Path Problems
SLS '09 Proceedings of the Second International Workshop on Engineering Stochastic Local Search Algorithms. Designing, Implementing and Analyzing Effective Heuristics
Runtime analysis of an ant colony optimization algorithm for TSP instances
IEEE Transactions on Evolutionary Computation
Runtime analysis of a binary particle swarm optimizer
Theoretical Computer Science
A few ants are enough: ACO with iteration-best update
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Ant colony optimization and the minimum cut problem
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Ant colony optimization for stochastic shortest path problems
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Theoretical properties of two ACO approaches for the traveling salesman problem
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
Runtime analysis of the 1-ANT ant colony optimizer
Theoretical Computer Science
Using markov-chain mixing time estimates for the analysis of ant colony optimization
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
Simple max-min ant systems and the optimization of linear pseudo-boolean functions
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
Runtime analysis of a simple ant colony optimization algorithm
ISAAC'06 Proceedings of the 17th international conference on Algorithms and Computation
Hi-index | 0.00 |
The theory of swarm intelligence has made rapid progress in the last 5 years. Following a very successful line of research in evolutionary computation, various results on the computational complexity of swarm intelligence algorithms have appeared. These results shed light on the working principles of swarm intelligence algorithms, help to identify the impact of parameters and other design choices on performance, and contribute to a solid theoretical foundation of swarm intelligence. This tutorial will give a comprehensive overview of theoretical results on swarm intelligence algorithms, with an emphasis on their computational complexity. In particular, it will be shown how techniques for the analysis of evolutionary algorithms can be used to analyze swarm intelligence algorithms and how the performance of swarm intelligence algorithms compares to that of evolutionary algorithms. The tutorial will be divided into a first, larger part on ant colony optimization (ACO) and a second, smaller part on particle swarm optimization (PSO). For ACO we will consider simple variants of the MAX-MIN ant system. Investigations of example functions in pseudo-Boolean optimization demonstrate that the choice of the pheromone update strategy and the evaporation rate has a drastic impact on the running time, even for very simple functions like ONEMAX. We will also elaborate on the effect of using local search within the ACO framework. In terms of combinatorial optimization problems, we will look at the performance of ACO for minimum spanning trees, shortest path problems, and the TSP. For particle swarm optimization, the tutorial will cover results on PSO for pseudo-Boolean optimization as well as a discussion of theoretical results in continuous spaces.