QALO-MOR: Improved antlion optimizer based on quantum information theory for model order reduction

• Published in: Journal of intelligent & fuzzy systems Vol. 41; no. 5; pp. 5747 - 5757
• Main Authors: Pradhan, Rosy, Khan, Mohammad Rafique, Sethy, Prabir Kumar, Majhi, Santosh Kumar
• Format: Journal Article
• Language: English
• Published: Amsterdam IOS Press BV 01.01.2021
• Subjects: Algorithms; Global optimization; Heuristic methods; Information theory; Model reduction; Optimization; Quantum phenomena; Quantum theory
• ISSN: 1064-1246; 1875-8967
• DOI: 10.3233/JIFS-189894

The field of optimization science is proliferating that has made complex real-world problems easy to solve. Metaheuristics based algorithms inspired by nature or physical phenomena based methods have made its way in providing near-ideal (optimal) solutions to several complex real-world problems. Ant lion Optimization (ALO) has inspired by the hunting behavior of antlions for searching for food. Even with a unique idea, it has some limitations like a slower rate of convergence and sometimes confines itself into local solutions (optima). Therefore, to enhance its performance of classical ALO, quantum information theory is hybridized with classical ALO and named as QALO or quantum theory based ALO. It can escape from the limitations of basic ALO and also produces stability between processes of explorations followed by exploitation. CEC2017 benchmark set is adopted to estimate the performance of QALO compared with state-of-the-art algorithms. Experimental and statistical results demonstrate that the proposed method is superior to the original ALO. The proposed QALO extends further to solve the model order reduction (MOR) problem. The QALO based MOR method performs preferably better than other compared techniques. The results from the simulation study illustrate that the proposed method effectively utilized for global optimization and model order reduction.