In this paper, a generic methodology based on swarm algorithms using Artificial Bee Colony (ABC) algorithm is proposed for combined cost and weight optimization of laminated composite structures. Two approaches, namely Vector Evaluated Design Optimization (VEDO) and Objective Switching Design Optimization (OSDO), have been used for solving constrained multi-objective optimization problems. The ply orientations, number of layers, and thickness of each lamina are chosen as the primary optimization variables. Classical lamination theory is used to obtain the global and local stresses for a plate subjected to transverse loading configurations, such as line load and hydrostatic load. Strength of the composite plate is validated using different failure criteria\u2014Failure Mechanism based failure criterion, Maximum stress failure criterion, Tsai-Hill Failure criterion and the Tsai-Wu failure criterion. The design optimization is carried for both variable stacking sequences as well as standard stacking schemes and a comparative study of the different design configurations evolved is presented. Performance of Artificial Bee Colony (ABC) is compared with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for both VEDO and OSDO approaches. The results show ABC yielding a better optimal design than PSO and GA.<\/p>","DOI":"10.4018\/jamc.2011070101","type":"journal-article","created":{"date-parts":[[2011,10,19]],"date-time":"2011-10-19T16:04:00Z","timestamp":1319040240000},"page":"1-26","source":"Crossref","is-referenced-by-count":10,"title":["Vector Evaluated and Objective Switching Approaches of Artificial Bee Colony Algorithm (ABC) for Multi-Objective Design Optimization of Composite Plate Structures"],"prefix":"10.4018","volume":"2","author":[{"given":"S. N.","family":"Omkar","sequence":"first","affiliation":[{"name":"Indian Institute of Science, India"}]},{"given":"G. Narayana","family":"Naik","sequence":"additional","affiliation":[{"name":"Indian Institute of Science, India"}]},{"given":"Kiran","family":"Patil","sequence":"additional","affiliation":[{"name":"Indian Institute of Science, India"}]},{"given":"Mrunmaya","family":"Mudigere","sequence":"additional","affiliation":[{"name":"Indian Institute of Science, India"}]}],"member":"2432","reference":[{"key":"jamc.2011070101-0","doi-asserted-by":"publisher","DOI":"10.1162\/evco.1993.1.1.1"},{"key":"jamc.2011070101-1","doi-asserted-by":"crossref","unstructured":"Chafekar, D., Xuan, J., & Rasheed, K. (2003). Constrained multi-objective optimization using steady state genetic algorithms. In E. Cant\u00fa-Paz, J. A. Foster, K. Deb, L. D. Davis, R. Roy, U.-M. O\u2019Reilly et al. 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