A Comprehensive Investigation of Algorithms for Solving Mazes

Background: Path planning technology has a wide range of applications in many fields. Applications in the field of high technology include: autonomous and touchless action of robots; obstacle avoidance and defense flight of drones; cruise missiles to avoid radar search, anti- bouncing attacks, and to complete the task of sudden defense and demolition. Applications in daily life include: GPS navigation; road planning based on GIS system; urban road network planning and navigation. Applications in the field of decision-making management include: vehicle problem (VRP) in logistics management and similar resource allocation problems in resource management. Routing problems in the field of communication technology. Any planning problem that can be topologized as a point and line network can basically be solved by the method of path planning. Different intelligent algorithms have different characteristics, and their application scope and fields are also different, so it is of great significance for the development of path planning technology to study the path planning intelligent algorithms from the characteristics of the algorithms themselves and their applications. Objective: Analyze the advantages and disadvantages of various types of planning algorithms, look forward to the future development trend of mobile robot path planning, and provide certain ideas for the research of mobile robot path planning. Methods: Search journals, patents, conferences, and papers related to mobile robot path planning, and summarize and analyze the advantages and disadvantages of various planning algorithms. Results: Based on the research results of many scholars, this study summarizes different mobile robot path planning methods. It is divided into four categories: traditional planning, intelligent search, artificial intelligence and local obstacle avoidance. This paper introduces and analyzes the latest research results of these types, including their design ideas, advantages and disadvantages, and improvement measures. The research methods adopted are analyzed in order to maximize the advantages of each algorithm and expand the application field of robot path planning to provide ideas and references. Conclusion: This paper provides guidance for the design and optimization of robot path planning. Finally, this paper summarizes the future development trend of robot path planning, and looks forward to the future development trend and key areas of robot path planning.

This paper focuses on Dynamic Implementation using Array and Linked list. In the data structure concept, the list plays a major role in the allocation of data where it is a general overview of creating a node in a linked list using an array with the initialization of size. The node contains data and is interconnected to the next node in the sequence via their links which perform the search operation where the space and time complexity are not wasted in these analyses. Linked lists are a great way to store a theoretically infinite amount of data with a small and versatile amount of code. The amount of data stored increases linearly with the number of nodes in the list (space-o(n)) where linked lists are more efficient than arrays. It is observed that considering the time complexity when there is a variation in data set linked array is almost reaches close to array whenever dataset is high also linked array has better time complexity than linked list when the searching element is in middle and last. Considering the space complexity linked array is almost 2 times and 4 times less space is needed than array and linked list respectively.

Binary embedding refers to methods for embedding points in $\mathbb {R}^{d}$ into vertices in the Hamming cube of dimension ${\mathcal{O}}(d)$ , such that the normalized Hamming distance between the codes preserves a prespecified distance between vectors in the original space. A common approach to binary embedding is to use random projection , followed by one-bit quantization to produce binary codes. Of particular interest, in this letter, is sparse circulant binary embedding (SCBE), where a sparse random circulant matrix with random sampling at a rate of $1-s$ for $s\in (0, 1)$ is used for random projection. The SCBE has the space complexity ${\mathcal{O}}((1-s)d)$ , while unstructured random projection has the space complexity ${\mathcal{O}}(d^2)$ . We present an asymptotic analysis of SCBE, when $d$ approaches $\infty$ , showing that the performance of SCBE is comparable to that of binary embedding with unstructured random projection whilethe former has the space complexity ${\mathcal{O}}((1-s)d)$ and the time complexity ${\mathcal{O}}(d\log d)$ but the latter has both space and time complexities ${\mathcal{O}}(d^2)$ .

This paper introduces a new heuristic function with high efficiency for an optimum solving of the 8 - puzzle, this one being the double of the Chebyshev distance. The comparative study is realized among this new heuristic (Chebyshev heuristic), the Hamming h euristic and the Manhattan heuristic using A* algorithm implemented in Java. The Chebyshev heuristic function is more informed than Hamming and Manhattan heuristics. This paper also presents the necessary stages in object oriented development of an interactive software dedicated to simulate the A* search algorithm for 8 -puzzle. The modeling of the software is achieved through specific UML diagrams representing the phases of analysis, design and implementation, the system thus being described in a clear and practical manner. In order to confirm the obtained theoretical results which show that Chebyshev heuristic is more efficient, two performance criteria were used: space complexity and time complexity. The space complexity was measured by the number of generated nodes from the search tree, by the number of the expanded nodes and by the effective branching factor. The time complexity was measured by the running time. From the experimental results obtained by using the Chebyshev heuristic, improvements were obs erved regarding space and time complexity of A* algorithm versus Hamming and Manhattan heuristics.

Algorithms are the primary component of Artificial Intelligence(AI). The algorithm is the process in AI that imitates the human mind to solve problems. Currently evaluating the performance of AI is achieved by evaluating AI algorithms by metric scores on data sets. However the evaluation of algorithms in AI is challenging because the evaluation of the same type of algorithm has many data sets and evaluation metrics. Different algorithms may have individual strengths and weaknesses in evaluation metric scores on separate data sets, lacking the credibility and validity of the evaluation. Moreover, evaluation of algorithms requires repeated experiments on different data sets, reducing the attention of researchers to the research of the algorithms itself. Crucially, this approach to evaluating comparative metric scores does not take into account the algorithm’s ability to solve problems. And the classical algorithm evaluation of time and space complexity is not suitable for evaluating AI algorithms. Because classical algorithms input is infinite numbers, whereas AI algorithms input is a data set, which is limited and multifarious. According to the AI algorithm evaluation without response to the problem solving capability, this paper summarizes the features of AI algorithm evaluation and proposes an AI evaluation method that incorporates the problem-solving capabilities of algorithms.

WITHDRAWN: Spearman rank correlation based bagging ensemble bayes optimal classification for genomic predictive pattern analytics

We initiate the systematic study of the energy complexity of algorithms (in addition to time and space complexity) based on Landauer's Principle in physics, which gives a lower bound on the amount of energy a system must dissipate if it destroys information. We propose energy-aware variations of three standard models of computation: circuit RAM, word RAM, and transdichotomous RAM. On top of these models, we build familiar high-level primitives such as control logic, memory allocation, and garbage collection with zero energy complexity and only constant-factor overheads in space and time complexity, enabling simple expression of energy-efficient algorithms. We analyze several classic algorithms in our models and develop low-energy variations: comparison sort, insertion sort, counting sort, breadth-first search, Bellman-Ford, Floyd-Warshall, matrix all-pairs shortest paths, AVL trees, binary heaps, and dynamic arrays. We explore the time/space/energy trade-off and develop several general techniques for analyzing algorithms and reducing their energy complexity. These results lay a theoretical foundation for a new field of semi-reversible computing and provide a new framework for the investigation of algorithms.

This article emphasizes the importance of artificial intelligence in the tourism sector. Various technologies are being integrated to enhance customer services. The world is making significant efforts to strengthen industrial competitiveness using big data and artificial intelligence technologies, and tourism is facing increasing demands for new strategic solutions through the digital transformation of the existing tourism industry using cutting-edge technologies. Artificial intelligence quickly addresses customer requirements by providing timely information on critical factors such as hotels, airplanes, cathedrals, general facilities, and natural resources. This information can include interactive messages, virtual tours, interactive booking processes, language translations, and global positioning system technology. Artificial intelligence algorithms in the tourism industry help predict demand, revenue, and business trends. The tourism industry incorporates new technologies like virtual reality, chatbots, and language translation. Adopting artificial intelligence apps requires significant investments, including initial capital costs, maintenance, software updates, and staff training. Understanding the tourism and artificial intelligence industry will help build an artificial intelligence that will significantly transform the tourism industry in the future. The rise of artificial intelligence simplifies the process of making travel plans. Tourists used to decide their destinations and activities using pictures in a catalog or on the Internet. They have recently begun using artificial intelligence to find and tailor their specific requirements. Artificial intelligence is a valuable supplementary aspect to the future of the tourism industry. However, it cannot surpass the human touch, an essential determinant of experiential tourism.

We present the family of RedBlue algorithms, a collection of universal wait-free constructions for linearizable shared objects in an asynchronous shared-memory distributed system with n processes. The algorithms are adaptive and improve upon previous algorithms in terms of their time and/or space complexity. The first of the algorithms achieves better time complexity than all previously presented algorithms but it is impractical since it uses large LL/SC registers. This algorithm comprises the keystone for the design of the other RedBlue algorithms which are of practical interest. The second algorithm significantly reduces the size of the required registers and it is therefore practical in many cases. The last two algorithms work efficiently for large objects improving previous universal constructions for large objects presented by Anderson and Moir (PODC 1995).

Artificial intelligence (AI) and explainable artificial intelligence (XAI) are advancing rapidly, with the potential to deliver significant benefits to modern society. The healthcare sector, in particular, has experienced transformative changes; overall, these technologies are helping to address numerous challenges, such as cancer cell detection, tumour zone identification in animal bodies, predictions of major and minor diseases, diagnosis, and more. This article provides an in‐depth and detailed overview of AI and XAI, focusing on recent trends and their implications for advancing Healthcare 5.0 applications. Initially, the study examines the key concepts and exceptional features of AI, XAI, and Healthcare 5.0. Additional emphasis is placed on state‐of‐the‐art practices currently being implemented in healthcare, particularly those involving AI and XAI. Subsequently, it establishes a coherent link between AI and XAI in Healthcare 5.0, grounded in contemporary advancements. Based on the findings, algorithms are recommended to address initial obstacles to integrating AI into the Healthcare 5.0 framework. Proposals for further enhancing Healthcare 5.0 performance through the integration of XAI and its unique features are discussed in detail. The work also provides in‐depth implementation strategies and highlights model‐specific trends within AI and XAI frameworks in Healthcare 5.0. Particular attention is given to AI model predictions in healthcare settings, emphasising their contributions to improved patient feedback and the delivery of more sophisticated care. Most importantly, this research highlights the potential for AI and XAI to support sustainable advancements in Healthcare 5.0 applications. Finally, significant issues are analysed, and an open discussion is presented on future guidelines for the blending of AI with XAI, and Healthcare 5.0 applications.

The classic buffer insertion algorithm of van Ginneken has time and space complexity O(n/sup 2/), where n is the number of possible buffer positions. For more than a decade, van Ginneken's algorithm has been the foundation of buffer insertion. In this paper, we present a new algorithm that computes the same optimal buffer insertion, but runs much faster. For 2-pin nets, our time complexity is O(nlogn) and space complexity is O(n). For multipin nets, our time complexity is O(nlog/sup 2/n) and space complexity is O(nlogn). The speedup is achieved by four novel techniques: predictive pruning, candidate tree, fast redundancy check, and fast merging. On industrial test cases, the new algorithms is 2-80 times faster than van Ginneken's algorithm and uses 1/4-1/500 of the memory. Since van Ginneken's algorithm and its variations are used by most existing algorithms on buffer insertion and buffer sizing, our new algorithm significantly improves the performance of all these algorithms. The predictive pruning technique has been applied to buffer cost minimization (Shi et al., 2004), and significantly improved the running time.

This paper presents a survey of the most recent literature developments in the field of computational guidance and control for space applications. In recent years, the space sector has changed significantly, shifting towards solutions requiring increased performance and autonomy. This change has also been accelerated by the technical and economic needs of the new space approach. However, traditional guidance and control techniques cannot always meet the increasing requirements of autonomy and performance. For this reason, computational guidance and control has become of increasing interest to the aerospace sector. In contrast to traditional guidance and control, computational guidance and control generates commands in real time by using on-board numerical computation. It represents, therefore, a fundamental step on the way to system autonomy and autonomous operations, with the potential to increase system capabilities and reduce operational costs. On-board computation introduces, however, a series of challenges, including the need to develop algorithms which are computationally efficient, reliable and robust. The literature has classified computational guidance and control techniques into two major categories, namely model-based and data-based. The model-based category encompasses all techniques in which the guidance and control laws are explicitly related to the system dynamic and kinematic models. On the contrary, data-based techniques infer guidance and control laws from a set of data, derived from the models, but do not explicitly use such models in the algorithm itself. Model-based guidance and control includes optimal control, model predictive control and convex optimisation. For the specific case of space attitude re-orientation manoeuvres, additional techniques are path planning and artificial potential functions. The general concept behind the Model Predictive Control (MPC) framework is to solve an optimal control problem repeatedly, for example whenever new measurements are available. The MPC implements only the first element of the returned solution, and the process is then repeated. By doing so, the system is better able to cope autonomously with model uncertainties, failures, or errors. For spacecraft with limited on-board capability, having to repeatedly solve an optimisation problem on-board could represent a challenge. In this case, explicit implementations of the MPC have been proposed, in which the guidance and control laws are precomputed offline and implemented on-board using look-up tables or simple functions. If the optimal control problem of the MPC is convex, or if it can be convexified, convex optimisation can be used directly. If the problem cannot be convexified, the solution can be found by sequentially solving a convex approximation of the original non-convex problem. Convex optimisation can be used either repeatedly in the context of an MPC framework, or as a single function call to generate guidance and control laws, e.g. at the beginning of an attitude re-orientation manoeuvre. Convex optimisation has received increased attention in recent years, also due to its suitability to on-board implementation. In fact, if the optimal control problem is convex, it can be solved reliably and efficiently on-board. Convex optimisation has been successfully applied to the space sector with the notable examples of the SpaceX rocket booster landing and of the Mars pinpoint landing demonstration aboard the Masten Xombie sounding rocket. Model-based guidance techniques, not based on optimisation, have been increasingly explored in recent years, as guidance strategies employed in other engineering fields, such as robotics, are being applied to spacecraft. Among these strategies, of particular importance are path planning and artificial potential field methods, due to their applicability to spacecraft autonomous attitude manoeuvres. Path planning algorithms have been applied for decades to ground vehicles and robotic manipulators. These algorithms consist of techniques to find the best route between two points of a graph representing the search space of a problem. For spacecraft re-orientation applications, the literature has proposed different attitude representation conventions and various cost functions associated to the graph search algorithm. Each approach comes with advantages and disadvantages, which will be analysed in the paper. Along with path planning, Artificial Potential Function (APF) techniques have been developed in the field of robotics, and are now also considered for space applications, to track or maintain a specific reference under a set of constraints. The main idea behind these methods is to synthetise the controller using a potential function. This function is specifically built to grant asymptotic stability to the system, according to Lyapunov’s second stability theorem. This formulation grants flexibility to accommodate system constraints, in a computationally light-weight and efficient manner. Data-based guidance and control laws are mainly based on Artificial Intelligence (AI) technologies, and generate guidance commands after training an algorithm with a large set of data. The interest of the space sector towards AI has accelerated in the last years due to the greater maturity of the technology. Some of the most powerful examples lie in the area of machine learning, like reinforcement learning, supervised learning and deep learning. These algorithms are used to extract patterns from large amounts of data derived from complex nonlinear dynamical and kinematical models. Examples of such data can be numerical solutions of an optimal control problem. The patterns extracted from the data allow the system to identify, in a short computational time, the critical parameters needed to relate the state and control variables. Creating a sufficiently large dataset for training the algorithm requires a considerable computational effort, though, and for certain methods optimality cannot be guaranteed due to the lack of theoretical support derived from the optimality conditions. Advancements made in the last years span several research directions and open a wide variety of opportunities and challenges, which need to be examined to guide further exploration. In this paper, both model-based and data-based techniques and applications are presented. The considered techniques include model predictive control, convex optimisation and machine learning. In addition, for the implementation of autonomous attitude manoeuvres, path planning methods and artificial potential function methods are analysed. These techniques have been applied, in the literature, to a range of different applications. The applications discussed in this paper include, in addition to attitude manoeuvres, powered descent landing guidance, low-thrust orbital transfers, proximity operations and station keeping.

Artificial Intelligence (AI) has emerged as a transformative force across various domains, with its potential to revolutionize urban architecture gaining increasing recognition. This paper offers a detailed examination of AI's application in the construction of public buildings, emphasizing its achievements, challenges, and future outlook. The review spans all facets of civil engineering, including review processes, analysis, design, construction management, geotechnical engineering, transportation planning, and construction oversight. AI methods, such as machine learning and genetic algorithms, are employed in analysis and design to enhance processes, forecast material behavior, and advance healthcare applications. In construction management, AI is utilized for project scheduling, resource distribution, risk evaluation, and safety management. Geotechnical applications of AI provide precise soil property estimation, soil damage assessment, and foundation construction improvements. Advanced technologies aid in transportation planning, traffic prediction, intelligent transportation systems, and infrastructure enhancements. Additionally, AI plays a crucial role in monitoring and maintaining public infrastructure, including bridge inspections, pipeline integrity evaluations, and early defect detection through image processing and data analysis. Despite significant advancements, challenges persist regarding AI's widespread adoption in civil engineering, including data availability, AI model definitions, ethical issues, and the necessity for collaborative efforts. Addressing these challenges will require the joint efforts of researchers, practitioners, and policymakers. Ultimately, AI's integration into civil engineering demonstrates its potential to enhance the efficiency, safety, and sustainability of infrastructure systems. This review summarizes the current knowledge, highlights challenges, and proposes directions for future research to advance AI integration in civil engineering.

In order to solve efficiency of maze optimal path problem based on ant colony algorithm, we proposed a CUDA platform parallel programming model, executing the algorithm with GPU that significantly improve the calculation efficiency. A parallel matrix design method was introduced and the ant colony searching process was changed into parallel matrix operations. To reduce the access overhead and increase algorithm running speed, data were rationally organized and stored. Experiments of different scale maze matrix test show that speedup will be increased with the expansion of the matrix size. In our experiments, the maximum speedup is about 11.5. The maze problem is a typical search and traversal problem of graph. Many intelligent problems could be transformed into the maze optimal path problem, for example: chess game, strategy decision, robot path planning. The space and time complexity of the traditional algorithm will be growing exponentially with the expansion of the matrix size and complexity. So it's very hard using it to resolve the large scale cases. According to the character of maze optimal path problem, Reference (3) the application of ant colony algorithm to solve maze problem divide ants into two groups and set three life cycles, then get the solution by iterative cycle. But with the expansion of the matrix, the loops of iterative cycle will be greatly increased. Reference (4) an improvement to (3) algorithm set the distance to gate as basic parameter k, and then distributes ants at the position k. It will decrease the count of loops by ant colony algorithm. MMAS (Max-Min Ant System) algorithm is also applied to this process to avoid convergence too early. That algorithm avoids the search by every ant's taboo list, but it needs judgment to many conditions on each loop. This article wills analysis those algorithms mentioned before and post a parallel solution to solve the maze optimal path problem based on ant colony algorithm. It uses parallel computation for the data in each iterative cycle the processing speed of the algorithm mentioned in (4).

Artificial Intelligence (AI) systems increasingly shape many aspects of daily life, influencing our jobs, finances, healthcare, and online content. This expansion has led to the rise of human–AI systems, where humans communicate, collaborate, or otherwise interact with AI, such as using AI outputs to make decisions. While these systems have shown potential to enhance human capabilities and improve performance on benchmarks, evidence suggests that they often underperform compared to AI‐only or human‐only approaches in experiments and real‐world applications. Here, we argue that human–AI systems should be developed with a greater emphasis on human‐centered factors—such as usability, fairness, trust, and user autonomy—within the algorithmic design and evaluation process. We advocate for integrating human‐centered principles into AI development through human‐centered algorithmic design and contextual evaluation with real users. Drawing on interdisciplinary research and our tutorial at two major AI conferences, we highlight examples and strategies for AI researchers and practitioners to embed these principles effectively. This work offers a systematic synthesis that integrates technical, practical, and ethical insights into a unified framework. Additionally, we highlight critical ethical considerations, including fairness, labor, privacy, and human agency to ensure that systems meet performance goals while serving broader societal interests. Through this work, we aim to inspire the field to embrace a truly human‐centered approach to algorithmic design and deployment.