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To better solve MaOPs, this paper presents a novel angular-guided particle swarm optimizer (called AGPSO). A novel velocity update strategy is designed in AGPSO, which aims to enhance the search intensity around the particles selected based on their angular distances. Using an external archive, the local best particles are selected from the surrounding particles with the best convergence, while the global best particles are chosen from the top 20% particles with the better convergence among the entire particle swarm. Moreover, an angular-guided archive update strategy is proposed in AGPSO, which maintains a consistent population with balanceable convergence and diversity. To evaluate the performance of AGPSO, the WFG and MaF test suites with 5 to 10 objectives are adopted. The experimental results indicate that AGPSO shows the superior performance over four current MOPSOs (SMPSO, dMOPSO, NMPSO, and MaPSO) and four competitive evolutionary algorithms (VaEA, <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\"><mml:mi>\u03b8<\/mml:mi><\/mml:math>-DEA, MOEA\\D-DD, and SPEA2-SDE), when solving most of the test problems used.<\/jats:p>","DOI":"10.1155\/2020\/6238206","type":"journal-article","created":{"date-parts":[[2020,4,17]],"date-time":"2020-04-17T23:30:54Z","timestamp":1587166254000},"page":"1-18","source":"Crossref","is-referenced-by-count":3,"title":["A Novel Angular-Guided Particle Swarm Optimizer for Many-Objective Optimization Problems"],"prefix":"10.1155","volume":"2020","author":[{"given":"Fei","family":"Chen","sequence":"first","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China"},{"name":"National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, China"},{"name":"Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, China"}]},{"given":"Shuhuan","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China"},{"name":"National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, China"},{"name":"Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1667-5371","authenticated-orcid":true,"given":"Fang","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China"},{"name":"National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, China"},{"name":"Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), 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