The pickup and delivery problem (PDP) is considered one of the key optimization problems. PDP is an NP-Hard problem; consequently, researchers tried to solve it using evolutionary algorithms. In literature, different variations of the problem have been studied using evolutionary algorithms. In this paper, we consider the many-objective variation of the PDP known as MaOPDP with six objectives as it is similar to real-life PDP. To solve the problem, we considered 15 different low-level heuristics (LLHs) divided between perturbation and local search phases and optimized the search between LLHs using a cross-domain technique known as Hyper-heuristics (HHs). To effectively solve MaOPDP, a q-learning-based HH named Reinforcement learning-based Selection Hyper-heuristic (RL_SHH) is proposed. According to our knowledge, the considered version of MaOPDP has not been optimized using HHs in the literature. A high-level selection criterion covering exploration and exploitation is proposed to choose between LLHs. To prove the effectiveness of our approach, benchmark data sets have been taken in small, medium, and large sizes and contrasted with state-of-the-art HHs and meta-heuristics. RL-SHH has produced significantly better results on 69 out of 72 instances while using Hypervolume (HV). Additionally, $\mu$ norm mean values (a cross-domain indicator) have been taken into consideration, and RL-SHH has dominated a state-of-the-art HH known as HH-ILS by 646.7% and 100% using HV and Additive Epsilon Indicator (AEI) respectively.

The Semantic Web (Web 3.0) is an advancement of the existing web in which knowledge is given well-defined importance, allowing people and machines to operate better. The Semantic Web is the next step in the evolution of the Web. The semantic web improves online technologies in need of generating, distributing, and linking material. In literature, multiple surveys have been done on the semantic web (Web 3.0), but those surveys are limited to some specific topics. According to the best of our understanding, none of the surveys provides a comprehensive study about the applications, challenges, and future of the semantic web along with its relationship with the Internet of things (IoT). The previous surveys focused on the Web 3.0 without touching on applications or challenges or focused on only the application prospect of the web 3.0, focused on the just the challenges, or focused on web 3.0 relationship with either internet of things or knowledge graphs but failed to touch the other important factors i.e., failed to provide comprehensive web 3.0 survey. This survey paper covers the gaps created from the previous survey papers in the same field and provides a comprehensive survey about web 3.0, a comparison between web 1.0, 2.0, and 3.0, the study of application and challenges in web 3.0, the relationship between web 3.0 with IoT and knowledge graph. Moreover, it focuses on the evolution of the web, and semantic web along with an explanation of the various layers, ontology tools, and semantic web tools with their comparison and semantic web service search. Despite all the shortcomings and challenges, the semantic web is moving in the right direction, and it is the future of the web.