###### Abstract
Therefore, the optimizers and the ways of handling complex constraints are crucial. The efficiency of the optimizers to provide the most suitable solution quality depends on the way of handling constraints. In constrained optimization problems, Constraint-Handling Techniques (CHTs) have been incorporated [24; 25; 26]. In the solution of constrained optimization problems, there are five types of constraint-handling techniques; these are: penalty functions, special representations and operators, repair algorithms, separation of objective function and constraints, and hybrid methods [24]. Currently, the Penalty Function (PF), Feasibility Rules (FR), Stochastic Ranking (SR), and \(\varepsilon\)-Constraint (\(\epsilon\)C) are popular constraint-handling techniques used in metaheuristic algorithms for the synthesis of mechanisms [27; 28; 29]. The PF transforms a constrained optimization problem into an unconstrained one [30]. In the PF, the constraints are penalized and incorporated into the objective function, and the penalty factor in constraints is usually high. Therefore, the search for feasible regions is prioritized over the objective function minimization, for instance, in [5; 6; 8; 17; 31; 32; 33; 34; 35; 36; 37; 38; 39]. The FR have three rules for handling constraints. In this method, the search for feasible regions is also prioritized over the objective function minimization [3; 7; 21; 40; 41]. The SR works by using a pseudo-random ordering of solutions where the first ones in the ranking are selected [29; 42]. A probabilistic parameter controls the order of solutions to prioritize the search for feasible regions or the minimization of the objective function. The \(\epsilon\)C relaxes the constraints to increase the probability of selecting solutions close to feasible regions. This constraint-handling technique prioritizes the search for feasible regions over the minimization of the objective functions. The \(\epsilon\)C also has a gradient-based mutation mechanism to find feasible regions using the gradient of unfeasible solutions [29; 43]. Through the empirical study of four CHTs (PF, FR, SR, and \(\epsilon\)C) for the solution of mechanism synthesis given in [29], it is confirmed that the most suitable solution quality depends on the way of handling constraints and consequently impacts the efficiency of optimizers. In that work, the feasibility rules usually led to efficient optimization in the solution of mechanism synthesis for rehabilitation. The summary of metaheuristic algorithms and their constraint-handling techniques used in the mechanism synthesis problem in a broad context and for specific rehabilitation purposes is given in Table 1. The PF and the FR are the most promising and widely used CHTs for the mechanism synthesis problem for rehabilitation. It is also observed that the performance of SR and \(\epsilon\)C have only been investigated a little. According to recent literature [44], the trend to improve the solution accuracy for the dimensional synthesis problem is to make modifications of metaheuristics by hybridizing different algorithms or modifying their structure, as they are shown in Table 1. However, the efficient and effective use of handling constraints based on neuronal networks has not been investigated in the dimensional synthesis problem for rehabilitation. This research direction can significantly influence the metaheuristic behavior.