What Makes a Problem Hard for a Genetic Algorithm? Some Anomalous Results and Their Explanation
Machine Learning - Special issue on genetic algorithms
What makes an optimization problem hard?
Complexity
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Hyperplane Ranking in Simple Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
PSO and multi-funnel landscapes: how cooperation might limit exploration
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Neighborhood topologies in fully informed and best-of-neighborhood particle swarms
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
A comparison of predictive measures of problem difficulty inevolutionary algorithms
IEEE Transactions on Evolutionary Computation
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
The fully informed particle swarm: simpler, maybe better
IEEE Transactions on Evolutionary Computation
On the computation of all global minimizers through particle swarm optimization
IEEE Transactions on Evolutionary Computation
A Cooperative approach to particle swarm optimization
IEEE Transactions on Evolutionary Computation
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
Locating and tracking multiple dynamic optima by a particle swarm model using speciation
IEEE Transactions on Evolutionary Computation
Evolving Problems to Learn About Particle Swarm Optimizers and Other Search Algorithms
IEEE Transactions on Evolutionary Computation
A hierarchical particle swarm optimizer and its adaptive variant
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Modified PSO Structure Resulting in High Exploration Ability With Convergence Guaranteed
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Particle Swarm Optimization With Recombination and Dynamic Linkage Discovery
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Understanding TSP difficulty by learning from evolved instances
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
Review: Measuring instance difficulty for combinatorial optimization problems
Computers and Operations Research
Discovering the suitability of optimisation algorithms by learning from evolved instances
Annals of Mathematics and Artificial Intelligence
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Steep gradients as a predictor of PSO failure
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
A survey of techniques for characterising fitness landscapes and some possible ways forward
Information Sciences: an International Journal
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This paper studies the problem difficulty for a popular optimization method - particle swarm optimization (PSO), particularly for the PSO variant PSO-cf (PSO with constriction factor), and analyzes its predictive measures. Some previous measures and related issues about other optimizers, mainly including deception and modality, are checked for PSO. It is observed that deception is mainly the combination of three factors - the measure ratios of attraction basins, the relative distance of attractors and the relative difference of attractors' altitudes. Multimodality and multi-funnel are proved not to be the essential factors contributing to the problem difficulty for PSO. The counterexamples and comparative experiments in this paper can be taken as a reference for further researches on novel comprehensive predictive measures of problem difficulty for PSO.