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Volume 1, Number 1, January 2016, pages 87-103 DOI: http://dx.doi.org/10.12989/acd.2016.1.1.087 |
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An efficient multi-objective cuckoo search algorithm for design optimization |
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A. Kaveh and T. Bakhshpoori
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| Abstract | ||
| This paper adopts and investigates the non-dominated sorting approach for extending the singleobjective Cuckoo Search (CS) into a multi-objective framework. The proposed approach uses an archive composed of primary and secondary population to select and keep the non-dominated solutions at each generation instead of pairwise analogy used in the original Multi-objective Cuckoo Search (MOCS). Our simulations show that such a low computational complexity approach can enrich CS to incorporate multiobjective needs instead of considering multiple eggs for cuckoos used in the original MOCS. The proposed MOCS is tested on a set of multi-objective optimization problems and two well-studied engineering design optimization problems. Compared to MOCS and some other available multi-objective algorithms such as NSGA-II, our approach is found to be competitive while benefiting simplicity. Moreover, the proposed approach is simpler and is capable of finding a wide spread of solutions with good coverage and convergence to true Pareto optimal fronts. | ||
| Key Words | ||
| multi-objective optimization; engineering design; cuckoo search; metaheuristic | ||
| Address | ||
| A. Kaveh and T. Bakhshpoori: Centre of Excellence for Fundamental Studies in Structural Engineering, Iran University of Science and Technology, Narmak, Tehran-16, Iran | ||