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In EGLPSO, most of the particles would be concentrated in global search at the beginning. Along with the iteration, the particles would slowly focus on local search. A new updating strategy would be used for global search, and a partial mutation strategy is applied to the leader particle of local search for a better position. During each iteration, the best particle of global search would exchange information with some particles of local search. EGLPSO is tested on a set of 12 benchmark functions, and it is also compared with other four PSO variants and another six well-known PSO variants. The experimental results showed that EGLPSO can greatly improve the performance of traditional PSO in terms of search accuracy, search efficiency, and global optimality.<\/jats:p>","DOI":"10.1515\/jisys-2015-0153","type":"journal-article","created":{"date-parts":[[2016,4,21]],"date-time":"2016-04-21T07:26:40Z","timestamp":1461223600000},"page":"421-432","source":"Crossref","is-referenced-by-count":1,"title":["Particle Swarm Optimization with Enhanced Global Search and Local Search"],"prefix":"10.1515","volume":"26","author":[{"given":"Jie","family":"Wang","sequence":"first","affiliation":[{"name":"School of Electrical Engineering, Control Theory and Control Engineering , Zhengzhou University , 450001 Zhengzhou , China"}]},{"given":"Hongwen","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Control Theory and Control Engineering , Zhengzhou University , 450001 Zhengzhou , China"}]}],"member":"374","published-online":{"date-parts":[[2016,4,21]]},"reference":[{"key":"2025120523272179031_j_jisys-2015-0153_ref_001_w2aab3b7d230b1b6b1ab2ab1Aa","doi-asserted-by":"crossref","unstructured":"M. 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