Lupeng Hao , Weihang Peng , Junhua Liu , Wei Zhang , Yuan Li , Kaixuan Qin
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引用次数: 0
Abstract
In recent years, the emergence of constrained multi-objective evolutionary algorithms (CMOEAs) has made it increasingly difficult to balance between the diversity and convergence of algorithms. To address this challenge, this paper proposes a competition-based two-stage evolutionary algorithm, named CP-TSEA, for constrained multi-objective problems. In the first stage, a constraint boundary relaxation learning mechanism was applied to the auxiliary population. This mechanism not only improved the diversity of the population but also enhanced the global search capability by relaxing the constraints, allowing infeasible solutions with higher fitness rankings to participate in the evolution. In the second stage, an equal-probability competitive strategy was used to select high-quality parents from the elite mating pool to ensure that the population could converge quickly to the optimal solution. The two-stage approach not only improved the exploration ability of the algorithm, but also was able to select higher quality solutions and prevent them from falling into local optima. Additionally, the solution selection in the elite environment employed a three-criteria ranking method to maintain a balance between population diversity and convergence. In terms of experiments, CP-TSEA was compared with seven advanced CMOEAs across five test suites, and the comprehensive data showed that CP-TSEA significantly outperformed its competitors. In addition, CP-TSEA also achieved the best values in six real-world problems, which further confirmed its scalability in real-world applications.
期刊介绍:
The aim of the journal is to provide an international forum for the dissemination of up-to-date information in the fields of the mathematics and computers, in particular (but not exclusively) as they apply to the dynamics of systems, their simulation and scientific computation in general. Published material ranges from short, concise research papers to more general tutorial articles.
Mathematics and Computers in Simulation, published monthly, is the official organ of IMACS, the International Association for Mathematics and Computers in Simulation (Formerly AICA). This Association, founded in 1955 and legally incorporated in 1956 is a member of FIACC (the Five International Associations Coordinating Committee), together with IFIP, IFAV, IFORS and IMEKO.
Topics covered by the journal include mathematical tools in:
•The foundations of systems modelling
•Numerical analysis and the development of algorithms for simulation
They also include considerations about computer hardware for simulation and about special software and compilers.
The journal also publishes articles concerned with specific applications of modelling and simulation in science and engineering, with relevant applied mathematics, the general philosophy of systems simulation, and their impact on disciplinary and interdisciplinary research.
The journal includes a Book Review section -- and a "News on IMACS" section that contains a Calendar of future Conferences/Events and other information about the Association.