## 1 Introduction
Due to the lighter weight of CNT--almost 7 times less than that of steel--where its specific strength is also much higher than that of steel, it paves a way for the capable use of reinforcement for polymer composites. The film formation of CNT onto the surface during the interaction of the metallic counterface also reduces the friction parameters. Combination of CNT and graphene-based UHMWPE composites has led to a substantial reduction in wear properties because of the nano-carbon-based particles present in it. These particles have a self-lubricating behavior and the same has been observed by other researchers [16; 32]. This research work highlights the inventive works that implement AI techniques in regular trial techniques (experimentation) to design UHMWPE composites with multiple reinforcements. The regular trial techniques are monotonous, and do not get commendable outcomes, such as discovering reinforcement materials of expected structure and size of the particles by designing such composites with complex micro/nano particles coupled as reinforcements. In addition to this, unwinding the determined associations between the different kinds of particles and volume fraction will help to develop productive UHMWPE composites that present a supreme performance. It is additionally very challenging to formulate actual models that are dependent on the complicated associations between its structure, composition, and properties. Therefore, a data-informative-based modelling technique, in particular, ANN, is used, which is a natural computing technique utilized in the designing of materials [33; 34; 35; 36; 37]. In order to have a better understanding of the impact of nanoparticles when utilized as reinforcements for UHMWPE composites, ANN is utilized to model the same. The process of modelling includes a collection of data from the published literature involving discrete augmentations of MWCNT, graphene, carbon fiber (CF), and HAP, and in mixture. Another natural computing technique, Genetic Algorithm (GA), has been utilized [38], involving the created ANN models as objective functions in order to optimize the parameters and for the process of designing composites that have greater enhancement in the required properties. GA is successfully applied both in a single and multi-objective fashion for the development of composites with esteem performance [39; 40; 41]. The utilization of ANN models as fitness functions in optimization using GA are greatly in use for the design of materials for numerous applications [42; 43]. Comparative methodology of utilizing different artificial intelligence techniques is used for the design of composites for prosthetic and dental applications [40; 44]. UHMWPE composites with two nanoparticles were designed and developed with the improved mechanical and tribological properties by the current authors in the previous study [16]. Addition of multiple type of fillers can overcome the shortcomings of any single type of filler, and even provide a synergistic effect. In the current work, the four multiscale particles, namely, MWCNT, graphene, carbon fiber, and hydroxyapatite, are added to the framework, to highlight the improved tribo-mechanical behavior. Thus, the objective of this study is to employ a natural computing-based design of novel hybrid UHMWPE composites with complex reinforcements, such as combined micro and nano particles, which display unrivaled mechanical and wear properties, for utilization in the acetabular cup of an artificial hip joint.