An effective teaching-learning-based cuckoo search algorithm for parameter optimization problems in structure designing and machining processes

• We propose a teaching-learning-based cuckoo search algorithm.
• Solving the parameter optimization in structure designing and machining process.
• Several famous cases are adopted and solved to illustrate the effectiveness of the proposed algorithm.
• The experimental results show that the proposed approach outperforms other algorithms and has achieved significant improvement.

The optimum selection of parameters is great important for the final quality of product in modern industrial manufacturing process. In order to achieve highly product quality, an effective optimization technique is indispensable. In this paper, a new hybrid algorithm named teaching-learning-based cuckoo search (TLCS) is proposed for parameter optimization problems in structure designing as well as machining processes. The TLCS combines the Lévy flight with teaching-learning process, then evolves with a co-evolutionary mechanism: for solutions to be abandoned in the cuckoo search will perform Lévy flight to generate new solutions, while for other better solutions, the teaching-learning process is used to improve the local searching ability of the algorithm. Then the proposed TLCS method is adopted into several well-known engineering parameter optimization problems. Experimental results show that TLCS obtains some solutions better than those previously reported in the literature, which reveals that the proposed TLCS is a very effective and robust approach for the parameter optimization problems.

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