## 1 Introduction
In addition, they can be used as a significant substitution for costly and time-consuming experimental optimization methods. A combined algorithm was proposed by [PERSON] et al. [16] to design composite plates subjected to in-plane loadings with minimum thickness. The model mixed charged system search (CSS) with PSO and used fiber orientation and the number of plies as the algorithm's variables. [PERSON] et al. [17] proposed a combined BA-GA optimization method to ratchet the specific strength and capacity of single-lap and double-lap mixed adhesive joints. The algorithm was focused on geometry and manufacturing restrictions to find the optimal joint design. [PERSON] et al. proposed an evolutionary structural optimization (ESO) model for optimizing single-lap and double-lap joints [18]. The focus of the ESO model is on reducing peak stresses by shaping the adherends contour under a [PERSON] rejection criterion. [PERSON] et al. [19] surveyed the effects of joint characteristics on its strength. They used a genetic algorithm and topology optimization to optimize the joint's geometry. According to the results, the type of load was important in the effectiveness of the geometry. Topology optimization (TOP) presented a greater strength geometry; overall, GA suggested a better strength-to-weight ratio solution. [PERSON] et al. [20] used machine learning to introduce a model, a multi-objective gray wolf optimizer combined with a support vector machine, for predicting the properties of composite materials. The model used six datasets to infer properties from available data. The model's accuracy ranged from 0.14% to 5.574%, depending on the quality of input data. [PERSON] et al. [4] proposed an optimization method based on the bees algorithm (BA) for reducing the peel and shear stresses in a single lap joint under bending moment by rearranging the layup of its adherends. A finite element model was used to verify the analytically derived stress equations. [PERSON] et al. [21] proposed a method based on structural health monitoring (SHM) to detect and classify the damages in the composite structures due to low-velocity impact (LVI). The study revealed that it improved the computational time of conventional methods by optimizing their computational techniques by utilizing optimization algorithms, such as GA, PSO, and neural networks. [PERSON] and [PERSON] [22] proposed a novel method for maximizing the stiffness of composite structures made of fiber-reinforced cylindrical bars at a constant amount of material. The method is based on topology optimization and validated by numerical examples. [PERSON] et al. [23] implemented fish swarm and frog leaping optimization methods to detect the damages of carbon fiber reinforced plastic \(\pi\) adhesive-bonded joints. [PERSON] et al. [24] introduced a combined PSO-FEM model to optimize the design of composite laminates to have minimum peel stress. [PERSON] et al. [25] used a particle swarm optimization algorithm to predict the behavior of single and double timber composite joints subjected to tensile force. One of the common fractures in adhesive joints is over-allowed peel stress in the adhesive area. Previous studies investigated the various methods to calculate peel stress magnitude in adhesive-bonded joints, but the lack of a tool for optimizing the offered arrangement of adherend layers for reducing peel stress was obvious. So, the main purpose of this paper is to introduce an intelligence optimization method instead of expensive experimental ones for finding the best ply stacking sequence that leads to the highest peel strength in the adherends of a double lap joint using the analytically derived peel stress Equation [12] as the objective function of the particle swarm optimization algorithm. This algorithm was selected due to its feasibility and convergence. Adherend layers were assumed to be orthotropic, and the joint was symmetric and under tensile force. Additionally, the effect of joint geometric characteristics on this stress was investigated.