Randomized algorithms
Swarm intelligence
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
Introduction to Algorithms
Evolutionary Algorithms and the Maximum Matching Problem
STACS '03 Proceedings of the 20th Annual Symposium on Theoretical Aspects of Computer Science
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
A rigorous analysis of the compact genetic algorithm for linear functions
Natural Computing: an international journal
Minimum spanning trees made easier via multi-objective optimization
Natural Computing: an international journal
Randomized local search, evolutionary algorithms, and the minimum spanning tree problem
Theoretical Computer Science
On the runtime analysis of the 1-ANT ACO algorithm
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Evolutionary algorithms and matroid optimization problems
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Information Processing Letters
First steps to the runtime complexity analysis of ant colony optimization
Computers and Operations Research
Why standard particle swarm optimisers elude a theoretical runtime analysis
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Ant colony optimization for stochastic shortest path problems
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Worst-case and average-case approximations by simple randomized search heuristics
STACS'05 Proceedings of the 22nd annual conference on Theoretical Aspects of Computer Science
A few ants are enough: ACO with iteration-best update
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Running time analysis of Ant Colony Optimization for shortest path problems
Journal of Discrete Algorithms
The use of tail inequalities on the probable computational time of randomized search heuristics
Theoretical Computer Science
Fixed budget computations: a different perspective on run time analysis
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
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We investigate the runtime of a binary Particle Swarm Optimizer (PSO) for optimizing pseudo-Boolean functions f:{0,1}^n-R. The binary PSO maintains a swarm of particles searching for good solutions. Each particle consists of a current position from {0,1}^n, its own best position and a velocity vector used in a probabilistic process to update its current position. The velocities for a particle are then updated in the direction of its own best position and the position of the best particle in the swarm. We present a lower bound for the time needed to optimize any pseudo-Boolean function with a unique optimum. To prove upper bounds we transfer a fitness-level argument that is well-established for evolutionary algorithms (EAs) to PSO. This method is applied to estimate the expected runtime for the class of unimodal functions. A simple variant of the binary PSO is considered in more detail for the test function OneMax, showing that there the binary PSO is competitive to EAs. An additional experimental comparison reveals further insights.