Swarm intelligence
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Designing Evolutionary Algorithms for Dynamic Environments
Designing Evolutionary Algorithms for Dynamic Environments
Adaptively choosing niching parameters in a PSO
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Particle swarm with speciation and adaptation in a dynamic environment
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Population structure and particle swarm performance
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Measuring fitness degradation in dynamic optimization problems
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
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Existing metrics for dynamic optimisation are designed primarily to rate an algorithm's overall performance. These metrics show whether one algorithm is better than another, but do not indicate any specific aspects of the performance. In this paper we split the offline error metric into two component parts. We propose a new metric to measure convergence speed, and show how this, when combined with a population diversity metric, correlates strongly with the overall performance. We then use these metrics to analyse several optimisation algorithms, yielding new insight into both the test function and how the algorithms' characteristics can be improved.