English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Bare bones particle swarm optimization is a parameter-free swarm intelligence algorithm which is famous for easy applying. It has aroused wide concern of academic circle on its principles and applications in recent years. However, losing the diversity quickly still causes the premature convergence in the iteration process. Hence, a pair-wise bare bones particle swarm optimization algorithm is proposed in this paper to balance the exploration and exploitation. Moreover, a separate iteration strategy is used in pair-wise operator to enhance the diversity of the swarm. A pair of particles will be placed in different groups and will be applied with different evolutionary strategies. Also, to verify the performance of the proposed algorithm, a set of well-known nonlinear benchmark functions are used in the experiment. Furthermore, several evolutionary algorithms are also evaluated on the same functions as the control group. Finally, the experiment results and statistical analysis confirm the performance of PBBPSO with nonlinear functions.

A Pair-wise Bare Bones Particle Swarm Optimization Algorithm for Nonlinear Functions