The process parameter optimization of laser cladding using a bio-inspired algorithm is a hot issue and attracts the attention of many scholars. The biggest difficulty, at present, is the lack of accurate information regarding the function relationship between objectives and process parameters. In this study, a novel process parameter optimization approach for laser cladding is proposed based on a multiobjective slime mould algorithm (MOSMA) and support vector regression (SVR). In particular, SVR is used as a bridge between target and process parameters for solving the problem of lacking accurate information regarding the function relationship. As a new metaheuristic algorithm, MOSMA is to obtain the Pareto solution sets and fronts. The Pareto solution sets are optimized process parameters, and the Pareto fronts are optimized objectives. Users can select the corresponding optimized process parameters according to their needs for the target. The performance of the proposed approach was evaluated by the TOPSIS method, based on actual laser cladding data and compared with several well known approaches. The results indicate that the optimal process parameters obtained by the proposed approach have better process performance.

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