Recently, Nature-Inspired Algorithms (NIAs) have received significant attention in the literature reviews and applications. This algorithmic family mimics nature-based processes to tackle complex optimization problems. Many problems in the industry, science, medicine, and engineering are becoming optimization problem challenges. More than 500 algorithms have been proposed till today to tackle specific problems in the different articles. Many researchers call these algorithms novel; these algorithms will be explaining in this study. New taxonomy and classification would be described. It has been found that nature-based algorithms have some problems and time complexity limits that could lead to a new direction for them in the future. This research would be used to study and understand the time complexity and parameter affection the computation time of recently developed nature-based algorithms for multi-modal problems and applications.

In this paper, the multi-objective Shrike Optimization Algorithm is analyzed to show the implementation steps and performance. The method primarily draws inspiration from the swarming behaviors of shrike birds in nature. The shrike birds undertake migration from their habitat as a means of ensuring their survival. The optimization process involves two components: exploration and exploitation. The Shrike Optimization Algorithm was thoroughly examined and implemented in both theoretical and practical models. It was specifically applied to a straightforward optimization issue with a basic multi-objective function using a weighted sum strategy for multi-objective with different parameters and performance compared with the Fox optimization algorithm. We concluded that SHOA multi-population, which leads to finding a multi-local solution, will enhance search and convergence rates.

A document by Hanan Abdulkarim. Click on the document to view its contents.