Constrained optimization using CODEQ

Many real-world optimization problems are constrained problems that involve equality and inequality constraints. CODEQ is a new, parameter-free meta-heuristic algorithm that is a hybrid of concepts from chaotic search, opposition-based learning, differential evolution and quantum mechanics. The performance of the proposed approach when applied to five constrained benchmark problems is investigated and compared with other approaches proposed in the literature. The experiments conducted show that CODEQ provides excellent results with the added advantage of no parameter tuning. (C) 2009 Elsevier Ltd. All rights reserved.