Novel multiobjective particle swarm optimization based on ranking and cyclic distance strategy
Yanmin Liu
School of Mathematics, Zunyi Normal University, Zunyi, China
Search for more papers by this authorShihua Wang
School of Mathematics and Statistics, Guizhou University, Guiyang, China
Search for more papers by this authorCorresponding Author
Xi Song
Department of Management Science and Engineering, College of Management, Shenzhen University, Shenzhen, China
Correspondence Xi Song, College of Management, Shenzhen University, 518060 Shenzhen, China.
Email: [email protected]
Search for more papers by this authorJie Yang
School of Mathematics, Zunyi Normal University, Zunyi, China
Search for more papers by this authorYanmin Liu
School of Mathematics, Zunyi Normal University, Zunyi, China
Search for more papers by this authorShihua Wang
School of Mathematics and Statistics, Guizhou University, Guiyang, China
Search for more papers by this authorCorresponding Author
Xi Song
Department of Management Science and Engineering, College of Management, Shenzhen University, Shenzhen, China
Correspondence Xi Song, College of Management, Shenzhen University, 518060 Shenzhen, China.
Email: [email protected]
Search for more papers by this authorJie Yang
School of Mathematics, Zunyi Normal University, Zunyi, China
Search for more papers by this authorAbstract
To effectively improve the convergence and diversity of the multiobjective particle swarm optimization (MOPSO), we proposed a novel MOPSO based on ranking and cyclic distance (RCDMOPSO) that comprehensively considers the spatial target and congestion information of particles. RCDMOPSO introduced a method namely global proportional ranking (GPR) which differs from nondominated ranking under the Pareto framework, and designed a novel external archive maintenance and the global selection strategies of learning sample by combining GPR with cyclic distance. In this paper, RCDMOPSO together with eight classic and state-of-the-art algorithms were examined on ZDT, UF, and DTLZ series to test functions. The results show that RCDMOPSO is highly competitive in achieving the objectives of both convergence and diversity. RCDMOPSO outperformed other popular algorithms such as MOPSOs and multiobjective genetic algorithms based on comprehensive performance evaluation indicators inverted generational distance and hypervolume, thus supporting that RCDMOPSO is an effective approach to tackle multiobjective optimization problems.
Open Research
DATA AVAILABILITY STATEMENT
Research data are not shared.
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