Comparative evaluation of large-scale many-objective algorithms on complex optimization problems

Iranian Journal of Numerical Analysis and Optimization

مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 04 شهریور 1404

نوع مقاله: Research Article

شناسه دیجیتال (DOI): 10.22067/ijnao.2025.91210.1569

نویسندگان

Ritika Chaudhary؛ Amarjeet Prajapati^*

Department of Computer Science, Engineering, and Information Technology, Jaypee Institute of Information Technology, Noida, India.

چکیده

In the field of optimization, there has been an enormous surge in interest in addressing large-scale many-objective problems. Numerous academicians and practitioners have contributed to evolutionary computation by developing a variety of optimization algorithms tailored to tackle computationally challenging optimization problems. Recently, various large-scale many-objective optimization algorithms (LSMaOAs) have been proposed to address complex large-scale many-objective optimization problems (LSMaOPs). These LSMaOAs have shown remarkable performance in addressing a variety of LSMaOPs. However, there is a pressing need to further investigate their performance in comparison to each other on different classes of LSMaOPs. In this study, we conducted a comparative investigation of three established LSMaOAs, namely, LMEA, LMOCSO, and S3CMAES, over rigorous benchmarking on DTLZ, LSMOP, UF9-10, and WFG test suites, encompassing problem sets with 3 to 10 objectives and varying numbers of variables between 100 and 500. Additionally, we assessed the algorithms’ efficacy on a test suite specifically designed for Large-scale Multi/Many-objective Problems (100-1000 decision variables). In addition, we propose Hybrid-LMEA, a light hybrid that integrates decision-variable clustering with competitive learning to improve both convergence and diversity. The hybrid works especially well on high-dimensional LSMOP problems, with better performance in 8 and 12 out of 27 test cases for IGD and GD, respectively. The outcomes of the experiments indicate the relative efficacy and effectiveness of the different algorithms in addressing large-scale many-objective problems. Researchers can leverage this comparative data to make informed decisions about which algorithms to employ for particular optimization problem domains.

کلیدواژه‌ها

Optimization Problems؛ Large-scale Multi-objective optimization؛ Large-scale Many-objective optimization