Orchard Algorithm (OA): A new meta-heuristic algorithm for solving discrete and continuous optimization problems

Meta-heuristic algorithms have been widely used to solve different optimization problems. There have always been ongoing efforts to develop new and efficient algorithms. In this paper, the Orchard Algorithm (OA) is designed and introduced, inspired by fruit gardening. In this process, various actions such as irrigation, fertilization, trimming, and grafting lead to a fruit orchard where most trees grow and produce fruit adequately. In OA, both explorations of the search space and exploitation of the best solutions are achieved using personal and social behavior. By introducing various operators such as annual growth, screening, and grafting, the algorithm can efficiently search and explore the search space. The performance of the proposed OA algorithm was evaluated on CEC2005, IEEE CEC06 2019,test functions, and five real-world engineering problems compared with 13 widely used and competitive algorithms. Thirty benchmark functions were used to compare the capabilities of the OA algorithm with other research. The OA yields far better results in many aspects than the other algorithms. The results show the OA's superiority and this algorithm's capability in solving optimization problems. (c) 2022 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.

Automated summary

This paper introduces the design and implementation of the Orchard Algorithm (OA), which is inspired by fruit gardening. The OA algorithm utilizes various actions such as irrigation, fertilization, trimming, and grafting to achieve optimal tree growth and fruit production. It combines personal and social behavior to explore and exploit the search space efficiently. Comparative analysis with 13 other algorithms demonstrates the superior performance of OA in solving optimization problems.