In many objective optimization problems (MaOPs), more than three distinct objectives are optimized. The challenging part in MaOPs is to get the Pareto approximation (PA) with high diversity and good convergence. In Literature, in order to solve the issue of diversity and convergence in MaOPs, many approaches are proposed using different multi objective evolutionary algorithms (MOEAs). Moreover, to get better results, the researchers use the sets of reference points to differentiate the solutions and to model the search process, it further evaluates and selects the non-dominating solutions by using the reference set of solutions. Furthermore, this technique is used in some of the swarm-based evolutionary algorithms. In this paper, we have used some effective adaptations of bat algorithm with the previous mentioned approach to effectively handle the many objective problems. Moreover, we have called this algorithm as many objective bat algorithm (MaOBAT). This algorithm is a biologically inspired algorithm, which uses echolocation power of micro bats. Each bat represents a complete solution, which can be evaluated based on the problem specific fitness function and then based on the dominance relationship, non-dominated solutions are selected. In proposed MaOBAT, dominance rank is used as dominance relationship (dominance rank of a solution means by how many other solutions a solution dominated). In our proposed strategy, dynamically allocated set of reference points are used, allowing the algorithm to have good convergence and high diversity pareto fronts (PF). The experimental results show that the proposed algorithm has significant advantages over several state-of-the-art algorithms in terms of the quality of the solution.

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.

The pickup and delivery problem (PDP) is a very common and important problem, which has a large number of real-world applications in logistics and transportation. In PDP, customers send transportation requests to pick up an object from one place and deliver it to another place. This problem is under the focus of researchers since the last two decades with multiple variations. In the literature, different variations of PDP with different number of objectives and constraints have been considered. Depending on the number of objectives, multi and many-objective evolutionary algorithms have been applied to solve the problem and to study the conflicts between objectives. In this paper, PDP is formulated as a many-objective pickup and delivery problem (MaOPDP) with delay time of vehicle having six criteria to be optimized. To the best of our knowledge, this variation of PDP has not been considered in the literature. To solve the problem, this paper proposes a memetic I-DBEA (Improved Decomposition Based Evolutionary Algorithm), which is basically the modification of an existing many-objective evolutionary algorithm called I-DBEA. To demonstrate the superiority of our approach, a set of experiments have been conducted on a variety of small, medium and large-scale problems. The quality of the results obtained by the proposed approach is compared with five existing multi and many-objective evolutionary algorithms using three different multi-objective evaluation measures such as hypervolume (HV), inverted generational distance (IGD) and generational distance (GD). The experimental results demonstrate that the proposed algorithm has significant advantages over several state-of-the-art algorithms in terms of the quality of the obtained solutions.

No abstract

No abstract