The Fifteen Puzzle problem is one of the most classical problems that has captivated mathematics enthusiasts for centuries. This is mainly because of the huge size of the state space with approximately 1013 states that have to be explored, and several algorithms have been applied to solve the Fifteen Puzzle instances. In this paper, to manage this large state space, the bidirectional A* (BA*) search algorithm with three heuristics, such as Manhattan distance (MD), linear conflict (LC), and walking distance (WD), has been used to solve the Fifteen Puzzle problem. The three mentioned heuristics will be hybridized in a way that can dramatically reduce the number of states generated by the algorithm. Moreover, all these heuristics require only 25 KB of storage, but help the algorithm effectively reduce the number of generated states and expand fewer nodes. Our implementation of the BA* search can significantly reduce the space complexity, and guarantee either optimal or near-optimal solutions.

Abstract:In developing web sites there are some rules that developers should depend on in order to create a site suitable to the users' needs and also to make them as comfort as possible when they surf it. Before creating any website or operating any application, it is important for developers to address the functionality, design, usability and security of the work according to the demands. Every developer has his/her own way to develop a website, some prefer to use website builders and while others prefer to what they have primarily formed in their mind What they have primarily formed in their mind preferred software and programming languages. Therefore, this paper will compare the web based sites and open source projects in terms of functionality, usability, design and security in order to help academic staffs or business organization for choosing the best way for developing an academic or e-commerce web site.

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Technically, software is a part of electronic devices that is responsible for managing hardware. The signaling communication between hardware and software ultimately controls electronic devices, and is known as Operating System (OS). For the purpose of selecting the best platform for clients, users should study functionalities, securities, graphic interfaces and usability of the different OS platforms. Therefore, this research focuses on choosing the suitable OS platform for user in academic and non-academic environments, according to demanding users' companionability. It also explains the first type of OS which can be utilized openly. In addition to that, it concentrates on some general significant aspects that are useful for OS users.

This discusses a case study on Fitness Dependent Optimizer or so-called FDO and adapting its parameters to the Internet of Things (IoT) healthcare. The reproductive way is sparked by the bee swarm and the collaborative decision-making of FDO. As opposed to the honey bee or artificial bee colony algorithms, this algorithm has no connection to them. In FDO, the search agent's position is updated using speed or velocity, but it's done differently. It creates weights based on the fitness function value of the problem, which assists lead the agents through the exploration and exploitation processes. Other algorithms are evaluated and compared to FDO as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) in the original work. The key current algorithms—The Salp-Swarm Algorithms (SSA), Dragonfly Algorithm (DA), and Whale Optimization Algorithm (WOA) have been evaluated against FDO in terms of their results. Using these FDO experimental findings, we may conclude that FDO outperforms the other techniques stated. There are two primary goals for this chapter: first, the implementation of FDO will be shown step-by-step so that readers can better comprehend the algorithm method and apply FDO to solve real-world applications quickly. The second issue deals with how to tweak the FDO settings to make the meta-heuristic evolutionary algorithm better in the IoT health service system at evaluating big quantities of information. Ultimately, The target of this chapter's enhancement is to adapt the IoT healthcare framework based on FDO to spawn effective IoT healthcare applications for reasoning out real-world optimization, aggregation, prediction, segmentation, and other technological problems.