The Effect of Quantum and Charged Particles on the Performance of the Dynamic Vector-evaluated Particle Swarm Optimisation Algorithm
作者： Mardé Helbig , Andries P. Engelbrecht
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摘要: Many problems in the real-world have more than one objective, with at least two objectives conflict another. In addition, objective changes over time. These kinds of are called dynamic multi-objective optimisation (DMOOPs). Studies shown that both quantum particle swarm (QPSO) and charged (CPSO) algorithms perform well environments, since they maintain diversity. Therefore, this paper investigates effect using either QPSOs or CPSOs sub-swarms vector-evaluated (DVEPSO) algorithm. DVEPSO variations then compared against default algorithm uses gbest PSOs heterogeneous contain particles. Furthermore, all aforementioned configurations (DMOPSO) was winning a comprehensive comparative study algorithms. The results indicate particles improve performance DVEPSO, especially for DMOOPs deceptive POF non-linear POS.
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