Swarm intelligence
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Advanced fitness landscape analysis and the performance of memetic algorithms
Evolutionary Computation - Special issue on magnetic algorithms
PSO and multi-funnel landscapes: how cooperation might limit exploration
Proceedings of the 8th annual conference on Genetic and evolutionary computation
MALLBA: a software library to design efficient optimisation algorithms
International Journal of Innovative Computing and Applications
Convergence behavior of the fully informed particle swarm optimization algorithm
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Particle swarm CMA evolution strategy for the optimization of multi-funnel landscapes
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Empirical computation of the quasi-optimal number of informants in particle swarm optimization
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Fitness-probability cloud and a measure of problem hardness for evolutionary algorithms
EvoCOP'11 Proceedings of the 11th European conference on Evolutionary computation in combinatorial optimization
Global characterization of the CEC 2005 fitness landscapes using fitness-distance analysis
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
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
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In a previous work, it was empirically shown that certain numbers of informants different from the standard "two" and the expensive "all" may provide the Particle Swarm Optimization (PSO) with new essential information about the search landscape, leading this algorithm to perform more accurately than other existing versions of it. Here, we extend this study by analyzing the internal behavior of PSO from the point of view of the evolvability. Our motivation is to find evidences of why such number of 6+/-2 informant particles, perform better than other neighborhood formulations of PSO. For this task, we have evaluated different combinations of informants for an extensive set of problem functions. Using fitness-distance correlation and fitness-fitness cloud analyses we have tested the accuracy of the resulting landscape characterizations. The results suggest that, in spite of certain deviation to the global optimum, a number of 6 informants in PSO can generate new improved particles for a longer time, even in complex problems with multi-funnel landscapes.