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Particle swarm optimization method is further adopted to conduct global search as per the multi-objective approach. The results of simulation tests on 24 classic test functions and three engineering constrained optimization problems show that compared with other algorithms, our proposed algorithm is effective and feasible, which can offer highly accurate solutions with good robustness. <\/jats:p>","DOI":"10.1142\/s0218001415590090","type":"journal-article","created":{"date-parts":[[2015,6,22]],"date-time":"2015-06-22T03:34:16Z","timestamp":1434944056000},"page":"1559009","source":"Crossref","is-referenced-by-count":9,"title":["An Adaptive Hybrid PSO Multi-Objective Optimization Algorithm for Constrained Optimization Problems"],"prefix":"10.1142","volume":"29","author":[{"given":"Hongzhi","family":"Hu","sequence":"first","affiliation":[{"name":"School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, P. R. 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