DBCC2: an improved difficulty-based cooperative co-evolution for many-modal optimization
Complex & Intelligent Systems, vol. 9, pp. 4403–4423
Abstract
Abstract Evolutionary multimodal optimization algorithms aim to provide multiple solutions simultaneously. Many studies have been conducted to design effective evolutionary algorithms for solving multimodal optimization problems. However, optimization problems with many global and acceptable local optima have not received much attention. This type of problem is undoubtedly challenging. In this study, we focus on problems with many optima, the so-called many-modal optimization problems, and this study is an extension of our previous conference work. First, a test suite including additively nonseparable many-modal optimization problems and partially additively separable many-modal optimization problems is designed. Second, an improved difficulty-based cooperative co-evolution algorithm (DBCC2) is proposed, which dynamically estimates the difficulties of subproblems and allocates the computational resources during the search. Experimental results show that DBCC2 has competitive performance.
Authors 5
-
University of Science and Technology of China
Affiliation as printed
School of Computer Science and Technology, University of Science and Technology of China, Hefei, 230027, Anhui, China
-
Wenjian Luo corresponding
Harbin Institute of Technology
Affiliation as printed
School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, 518055, Guangdong, China
-
University of Science and Technology of China
Affiliation as printed
School of Computer Science and Technology, University of Science and Technology of China, Hefei, 230027, Anhui, China
-
University of Science and Technology of China
Affiliation as printed
School of Computer Science and Technology, University of Science and Technology of China, Hefei, 230027, Anhui, China
-
Mike Preuß Aachen
Affiliation as printed
Leiden Institute of Advanced Computer Science (LIACS) at Leiden University, Leiden, The Netherlands
Cited by 3 stored of 3
3 results
No patents citing this paper on Lens.org (checked 2026-10-11).