## 1 Introduction
In a different study [35], the authors assessed the performance of the Savonius hydrokinetic turbine using soft computing techniques such as CatBoost, ANN, Random Forests (RFs), Multivariate Adaptive Regression Splines (MARSs), Adaptive Neuro-Fuzzy Inference System (ANFIS), and several hybrid ANFIS models (ANFIS-GA, ANFIS-SMA, and ANFIS-MPA), along with Linear Regression (LR). They aimed to predict the power coefficient and evaluate the impact of input parameters on the turbine's performance. Most of the methods successfully predicted the power coefficient with acceptable accuracy. Articles [36; 37; 38; 39] presented optimization using genetic algorithms, highlighting techniques and applications across various domains. These include solving multi-objective problems, optimizing wave energy converter array layouts, image optimization, and techno-economic optimization. Articles [40; 41; 42] utilized support vector machines for optimization across a range of domains. A comprehensive review of computational intelligence techniques was carried out in [43], including neural, fuzzy, and evolutionary computations, applied to applications of wave energy. Their review covered the estimation of resources as well as the control and design and control of wave energy converters (WECs). Most applications of neural computation techniques focused on predicting various parameters of wave energy with the multilayer perceptron (MLP) algorithm. The MLP is a type of ANN with multiple layers of neurons. This algorithm is based on a feedforward neural network with training performed using the error backpropagation method. The authors in [44] used an MLP regression of the ANN model to optimize the design of an asymmetric WEC to optimize the average extracted power. The multi-criteria decision analysis (MCDA) is a decision-making framework used to evaluate and prioritize multiple conflicting criteria. The weighted sum model (WSM) is a quantitative decision-making tool used in multi-criteria decision-making approach to evaluate and prioritize different alternatives based on multiple criteria. The authors in [45] conducted a multi-criteria site selection for offshore renewable energy platforms using WSM. Specialized decision-support tools have been developed to facilitate flexible, multi-criteria site selection for combined wind-wave energy platforms, with a focus on available energy resources. WSM has been employed in [46] to determine special allocation funds recipients. This method effectively generates rankings among the proposed alternatives in a special allocation fund. Article [47] used the WSM in titanium alloy selection for biomedical applications. The developed methodology was recognized for its applicability across various applications. This study aims to optimize design parameters related to a wave absorber with an inclined porous plate to reduce the reflected waves while also reducing the spatial footprint of the porous plate. To achieve this, a computationally efficient and technically robust optimization framework is developed by integrating the dual boundary element method (DBEM), artificial neural networks (ANNs), and the weighted sum model (WSM) within a multi-criteria decision analysis (MCDA) framework. This ensemble approach facilitates objective-driven design and supports informed decision-making in the context of ocean engineering. The Boundary Value Problem (BVP) was formulated and solved using the DBEM, which addresses two distinct boundary integral equations--singular and hyper-singular--at coincident source points along each side of the degenerate boundary. The design parameters affecting the performance of an inclined porous wave absorber are the porosity, inclination angle, and length of a porous plate. In the present study, porosity is excluded from design parameters because the optimal porosity has already been clarified to be around 0.1 in previous articles [5; 15; 28].