## 1 Introduction  
Meta-heuristics that solve optimisation problems involve some set of control parameters that influence the behaviour of the heuristic algorithm. In many cases, the algorithm can be extremely sensitive to different values of parameters and careful consideration is needed when selecting the parameter values. A structured approach must be followed to find an optimal assignment of control parameter values (known as a control parameter configuration) for a specific problem. This procedure is called control parameter tuning. When tuning the parameters of an algorithm, the performance of the algorithm for a given parameter configuration must be evaluated. This requires some metric that can be used to measure the performance of the algorithm. Modern tuning approaches only consider a single performance metric when determining the performance of an algorithm. However, focusing on one performance objective completely neglects other objectives. The optimisation of one objective may even worsen the performance of the algorithm with respect to other objectives. For example, when the objective of the algorithm is to find a very accurate solution, it might take a long time to execute. Alternatively, an algorithm that focuses on finding a solution as quickly as possible might not find a very accurate solution. A multi-objective tuning approach calculates multiple optimal parameter configurations, each representing a trade-off between different performance objectives. Few tuning approaches that account for multiple objectives are available in the literature, and existing methods face challenges including unknown numbers of optimal solutions and the recursive problem of tuning the tuning algorithms themselves. [PERSON] et al. [1] developed a multi-objective tuning algorithm called tuning multi-objective particle swarm optimisation (tMOPSO), which utilised multi-objective particle swarm optimisation to tune meta-heuristics on a range of objective function evaluation budgets. [PERSON] and [PERSON] [2] used a multi-objective evolutionary algorithm called evolutionary multi-objective parameter tuning (EMOPaT) to tune meta-heuristics under both accuracy and efficiency. These meta-heuristic-based approaches, however, use algorithms that also have control parameters. Hence, algorithms that are not tuned are being used to tune the desired algorithm. [PERSON] et al. [3] presented a racing algorithm called S-race that was used for automatic selection of support vector machine classifiers using multiple performance objectives. While this approach was not explicitly designed for tuning, it could be applied to select optimal parameter configurations. This study proposes M-race, a racing algorithm for tuning meta-heuristic parameters based on multiple performance objectives. While existing tuning approaches typically optimise for a single objective, M-race discovers a set of non-dominated parameter configurations that represent different trade-offs between competing objectives. This allows practitioners to select configurations that align with their specific performance priorities. The main contributions of this work are as follows:  
1. A multi-objective racing algorithm with minimal configuration requirements: M-race adapts the racing methodology to multi-objective settings without requiring algorithmic parameters to be tuned, unlike meta-heuristic-based approaches such as tMOPSO and EMOPaT. 2. Application of non-dominated sorting to parameter tuning: M-race incorporates non-dominated sorting from multi-objective optimisation to rank parameter configurations across multiple objectives simultaneously, extending traditional single-objective racing methods. 3. Multi-objective statistical elimination procedure: The approach extends the Friedman test for multi-objective comparison and employs regression-based early stopping to determine when sufficient non-dominated configurations have been identified. 4.