- Inspired by the intelligent behaviors of peafowls swarm, the design of POA includes effective and efficient exploratory and exploitative searching operators to provide a proper trade-off between global exploration and local exploitation to avoid local optimums, e.g., unique rotation dancing operations of peacocks, adaptive searching behaviors of peahens and peafowl cubs in different searching stages, along with interactions among different peacocks;
- The five peacocks that represent the current optimal solutions will also search in the nearby searching space through rotation dancing mechanism instead of standing still. The unique rotation dancing mechanism of peacocks contains two different rotation modes, i.e., in-situ rotating and circling around the food source. Note that such mechanism that the current optimal solutions will still undertake a nearby search is firstly employed which has never been considered in prior algorithms, which is beneficial for jumping out of local optimums;
- Peahens and peafowl cubs both tend to undertake an adaptive searching and approaching mechanism during the whole searching process to dynamically adjust their behaviors in different stages, upon which a proper balance between local exploitation and global exploration can be realized.
Wang Jingbo (2022). Peafowl (Pavo Muticus/Cristatus) Optimization Algorithm (https://www.mathworks.com/matlabcentral/fileexchange/102809-peafowl-pavo-muticus-cristatus-optimization-algorithm), MATLAB Central File Exchange. Retrieved .
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