| Abstract: |
| The coot optimizer is basically inspired by natural behaviour of the search engine (birds or coots). This optimizer originally was developed for tackling large-scale optimization applications. However, it faces lots of demerits like slow and premature convergence, due to which it gets trapped in local optima. To overcome these demerits the levy flight strategy has been integrated with coot optimizer, its called LFCOOT. The random walk phase of the levy flight is so familiar due to their high jumps. It helps in ignoring the local optima and enhancing the location of each search agent during the optimization process. Levy flight also plays an important role in making the balance between exploration and exploitation phases. To evaluate the performance of the LFCOOT have been considered into account IEEE CEC` 2017 and CEC` 2020 standards benchmark suites. In addition, its accuracy has also been verified on internet vehicle routing and some real-world engineering applications. Experimental outcomes reveal that the LFCOOT is able to offer the finest quality of solutions than the competitors. |
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