The landscape adaptive particle swarm optimizer
Applied Soft Computing
A random velocity boundary condition for robust particle swarm optimization
LSMS'07 Proceedings of the Life system modeling and simulation 2007 international conference on Bio-Inspired computational intelligence and applications
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
Perspectives on the Field of Cognitive Informatics and its Future Development
International Journal of Cognitive Informatics and Natural Intelligence
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To control the swarm to fly inside the limited search space and deal with the problems of slow search speed and premature convergence in particle swarm optimization algorithm, the authors applied the theory of topology, and proposed a novel quotient space-based boundary condition named QsaBC by using the properties of quotient space and homeomorphism in this paper. In QsaBC, Search space-zoomed factor and Attractor factor are introduced according to analyzing the dynamic behavior and stability of particles, which not only reduce the subjective interference and enforce the capability of global search, but also enhance the power of local search and escaping from an inferior local optimum. Four CEC'2008 benchmark functions were selected to evaluate the performance of QsaBC. Comparative experiments show that QsaBC can get the satisfactory optimization solution with fast convergence speed. Furthermore, QsaBC is more effective to do with errant particles, easier to calculate and has better robustness than other experienced methods.