Comprehensive learning particle swarm optimizer with guidance vector selection
Swarm Intelligence(2013)
摘要
In this paper, comprehensive learning particle swarm optimizer (CLPSO) is integrated with guidance vector selection. To update a particle's velocity and position, several candidate guidance positions are constructed based on all particles' best positions. Then the candidate guidance vector with the best fitness is selected to guide the particle. Simulation study is performed on CEC 2005 benchmark problems and the results show that the CLPSO with guidance vector selection has better performance when solving shifted and rotated optimization problems.
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关键词
benchmark testing,particle swarm optimisation,vectors,CEC 2005 benchmark problems,CLPSO,candidate guidance positions,candidate guidance vector,comprehensive learning particle swarm optimizer,guidance vector selection,particle position,particle velocity,rotated optimization problem,shifted optimization problem,CLPSO,guidance vector selection
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