Abstract
In the real-world, symmetry or asymmetry widely exists in various problems. Some of them can be formulated as constrained multi-objective optimization problems (CMOPs). During the past few years, handling CMOPs by evolutionary algorithms has become more popular. Lots of constrained multi-objective optimization evolutionary algorithms (CMOEAs) have been proposed. Whereas different CMOEAs may be more suitable for different CMOPs, it is difficult to choose the best one for a CMOP at hand. In this paper, we propose an ensemble framework of CMOEAs that aims to achieve better versatility on handling diverse CMOPs. In the proposed framework, the hypervolume indicator is used to evaluate the performance of CMOEAs, and a decreasing mechanism is devised to delete the poorly performed CMOEAs and to gradually determine the most suitable CMOEA. A new CMOEA, namely ECMOEA, is developed based on the framework and three state-of-the-art CMOEAs. Experimental results on five benchmarks with totally 52 instances demonstrate the effectiveness of our approach. In addition, the superiority of ECMOEA is verified through comparisons to seven state-of-the-art CMOEAs. Moreover, the effectiveness of ECMOEA on the real-world problems is also evaluated for eight instances.
Highlights
We propose an ensemble framework of constrained multi-objective optimization evolutionary algorithms (CMOEAs) that aims to achieve better versatility on handling diverse Constrained multi-objective optimization problems (CMOPs)
In this paper, we propose an ensemble framework of CMOEAs, which aims to generate an adaptively selecting guided CMOEA filter to enhance the versatility of existing CMOEAs
Experimental results on five benchmarks of totally 52 instances have verified the effectiveness of the proposed framework, and demonstrated the superiority or at least competitiveness of ECMOEA compared to seven state-of-the-art CMOEAs
Summary
Constrained multi-objective optimization problems (CMOPs) widely exist in real-world applications and scientific research fields, such as the multi-objective caching optimization [1], vehicle routing problems [2], web service location allocation problems [3], 3D indoor redeployment in IoT collection networks [4], asymmetric UAV trajectory planning [5], multiobjective testing resource allocation problems [6], and ring road bus lines and fare design [7]. Due to the population-based feature, which accords to the requirement of a CMOP for a set of tradeoff solutions, and independence of prior knowledge or problem structure, evolutionary algorithms (EAs) are widely used in solving multi-objective optimization problems (MOPs) [9], many-objective optimization problems (MaOPs) [10] and CMOPs [11], for which we termed multi-objective optimization evolutionary algorithms (MOEAs), many-objective optimization evolutionary algorithms (MaOEAs) and constrained multi-objective optimization evolutionary algorithms (CMOEAs) They mainly include the dominance-based [12], decomposition-based [13] and indicator-based [14] ones. Given the fact that a CMOP in real-world situation may have different features and poses different challenges to CMOEA, enhancing the versatility of CMOEA is expected To overcome this drawback, in this paper, we propose an ensemble framework of CMOEAs, which aims to generate an adaptively selecting guided CMOEA filter to enhance the versatility of existing CMOEAs. The main contributions can be summarized as follows: 2.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
