Ranked-based mechanism-assisted Biogeography optimization: Application of global optimization problems

• Published in: Advances in Engineering Software
• Main Author: Tao H.; Al-Aragi N.M.H.; Ahmadianfar I.; Naser M.H.; Shehab R.H.; Zain J.M.; Halder B.; Yaseen Z.M.
• Format: Article
• Language: English
• Published: Elsevier Ltd 2022
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139592390&doi=10.1016%2fj.advengsoft.2022.103301&partnerID=40&md5=1537a4763d6da5b273870bcc139f1233

The resolution of an optimization issue is a topic studied and debated by academics from a wide variety of areas continuously. Without an optimal solution, a lot of time and resources are likely to be wasted, and the issue will remain unsolved in the worst-case scenario. Due to these concerns, researchers are increasingly resorting to the development of robust meta-heuristic optimization techniques to improve current methods while creating new population-based approaches capable of exploring the required feature space. Therefore, biogeography-based optimization (BBO) is proposed in this paper, which is impacted by species' movement and emigration across islands in quest of better favorable environments. The BBO is indeed a population-based optimization approach used to solve complicated optimization issues. However, due to the nature of its operators, this technique might become stuck in sub-optimal solutions, which slows down convergence and increases the amount of computing time needed to find optimal solutions. In order to address these issues, this research proposes a BBO version that incorporates a ranked-based strategy (RBS), an exponential dynamic Brownian random differential (EBD) mechanism, and a successful adaptive random differential mutation (SarDM) mechanism to find a more effective solution in the feasible region. RBBO is the name given to the suggested approach. It has been evaluated for addressing four standard engineering problems with restrictions and 23 benchmark functions (seven unimodal, eight multimodal, and ten composite functions). The experimental findings and evaluations show that the suggested technique outperforms the conventional BBO's efficiency and accuracy. In the case of engineering problems, for instance, the four-reservoir problem, the proposed RBBO can provide superior results in terms of the average objective function (308.71) and standard deviation (0.16) over 30 different runs compared with the other optimization methods. We expect the community to use the suggested BBO-based technique to solve more challenging tasks. © 2022