Editing Particle swarm optimization (section)

=== Adaptive mechanisms ===
Without the need for a trade-off between convergence ('exploitation') and divergence ('exploration'), an adaptive mechanism can be introduced. Adaptive particle swarm optimization (APSO) <ref name=zhan09adaptive/> features better search efficiency than standard PSO. APSO can perform global search over the entire search space with a higher convergence speed. It enables automatic control of the inertia weight, acceleration coefficients, and other algorithmic parameters at the run time, thereby improving the search effectiveness and efficiency at the same time. Also, APSO can act on the globally best particle to jump out of the likely local optima. However, APSO will introduce new algorithm parameters, it does not introduce additional design or implementation complexity nonetheless.

Besides, through the utilization of a scale-adaptive fitness evaluation mechanism, PSO can efficiently address computationally expensive optimization problems.<ref>{{cite journal |last1=Wang |first1=Ye-Qun |last2=Li |first2=Jian-Yu |last3=Chen |first3=Chun-Hua |last4=Zhang |first4=Jun |last5=Zhan |first5=Zhi-Hui |title=Scale adaptive fitness evaluation-based particle swarm optimisation for hyperparameter and architecture optimisation in neural networks and deep learning |journal=CAAI Transactions on Intelligence Technology |date=September 2023 |volume=8 |issue=3 |page=849-862 |doi=10.1049/cit2.12106 |doi-access=free }}</ref>