Improved Slime-Mould-Algorithm with Fitness Distance Balance-based Guiding Mechanism for Global Optimization Problems

Autor: Enes Cengiz, Mehmet Fatih Işık, Hamdi Kahraman, Cemal Yılmaz, Çağrı Suiçmez
Jazyk: English<br />Turkish
Rok vydání: 2021
Předmět:
Zdroj: Düzce Üniversitesi Bilim ve Teknoloji Dergisi, Vol 9, Iss 6, Pp 40-54 (2021)
Druh dokumentu: article
ISSN: 2148-2446
DOI: 10.29130/dubited.1016209
Popis: In this study, the performance of Slime-Mould-Algorithm (SMA), a current Meta-Heuristic Search algorithm, is improved. In order to model the search process lifecycle process more effectively in the SMA algorithm, the solution candidates guiding the search process were determined using the fitness-distance balance (FDB) method. Although the performance of the SMA algorithm is accepted, it is seen that the performance of the FDB-SMA algorithm developed thanks to the applied FDB method is much better. CEC 2020, which has current benchmark problems, was used to test the performance of the developed FDB-SMA algorithm. 10 different unconstrained comparison problems taken from CEC 2020 are designed by arranging them in 30-50-100 dimensions. Experimental studies were carried out using the designed comparison problems and analyzed with Friedman and Wilcoxon statistical test methods. According to the results of the analysis, it has been seen that the FDB-SMA variations outperform the basic algorithm (SMA) in all experimental studies.
Databáze: Directory of Open Access Journals