A Novel Fast Path Planning Approach for Mobile Devices using Hybrid Quantum Ant Colony Optimization Algorithm

With IoT systems' increasing scale and complexity, maintenance of a large number of nodes using stationary devices is becoming increasingly difficult. Hence, mobile devices are being employed that can traverse through a set of target locations and provide the necessary services. In order to reduce energy consumption and time requirements, the devices are required to traverse following a Hamiltonian path. This problem can be formulated as a Travelling Salesman Problem (TSP), an NP-hard problem. Moreover, in emergency services, the devices must traverse in real-time, demanding speedy path planning from the TSP instance. Among the well-known optimization techniques for solving the TSP problem, Ant Colony Optimization has a good stronghold in providing good approximate solutions. Moreover, ACO not only provides near-optimal solutions for TSP instances but can also output optimal or near-optimal solutions for many other demanding hard optimization problems. However, to have a fast solution, the next node selection, which needs to consider all the neighbors for each selection, becomes a bottleneck in the path formation step. Moreover, classical computers are constrained to generate only pseudorandom numbers. Both these problems can be solved using quantum computing techniques, i.e., the next node can be selected with proper randomization, respecting the provided set of probabilities in just a single execution and single measurement of a quantum circuit. Simulation results of the proposed Hybrid Quantum Ant Colony Optimization algorithm on several TSP instances have shown promising results, thus expecting the proposed work to be important in implementing real-time path planning in quantum-enabled mobile devices

Intergrating the Fruin LOS into the Multi-Objective Ant Colony System

Building evacuation simulation provides the planners and designers an opportunity to analyse the designs and plan a precise, scenario specific instruction for disaster times. Nevertheless, when disaster strikes, the unexpected may happen and many egress paths may get blocked or the conditions of evacuees may not let the execution of emergency plans go smoothly. During disaster times, effective route-finding methods can help efficient evacuation process, in which the directors are able to react to the sudden changes in the environment. This research tries to integrate the highly accepted human dynamics methods proposed by Fruin into the Ant-Colony optimisation route-finding method. The proposed method is designed as a multi-objective ant colony system, which tries to minimize the congestions in the bottlenecks during evacuations, in addition to the egress time, and total traversed time by evacuees. This method embodies the standard crowd dynamics method in the literature, which are Fruin LOS and pedestrian speed. The proposed method will be tested against a baseline method, that is shortest path, in terms of the objective functions, which are evacuation time and congestion degree. The results of the experiment show that a multi-objective ant colony system performance is able to reduce both egress time and congestion degree in an effective manner, however, the method efficiency drops when the evacuee population is small. The integration of Fruin LOS also produces more meaningful results, as the load responds to the Level of Service, rather than the density of the crowd, and the Level of Service is specifically designed for the sake of measuring the ease of crowd movement

IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation

During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture

Pemilihan Jalur Evakuasi dalam Keadaan Darurat menggunakan Algoritma Quantum Ant-Colony

Evakuasi dalam keadaan darurat pada sebuah gedung sangatlah penting untuk menyelamatkan nyawa manusia. Pemilihan jalur evakuasi ketika terjadi suatu bencana sangatlah penting, pemilihan jalur evakuasi yang tepat dapat menekan jumlah korban jiwa yang berjatuhan. Berbagai metode dan algoritma simulasi penyeleksian jalur evakuasi telah banyak dikembangkan. Di antaranya algoritma ant-colony optimization ( ACO) dan artifisial bee-colony (ABC). Kedua algoritma tersebut mengadopsi perilaku individu terhadap lingkungan disekitarnya, sehingga cocok digunakan untuk seleksi jalur evakuasi. Pada penelitian ini digunakan algoritma quantum ant-colony (QACA) yang merupakan pengembangan dari algoritma ACO yang dikombinasikan dengan algoritma quantum-inspired evolutionary (QEA). Pada algoritma ini, algoritma QEA digunakan untuk memperbaharui feromon pada algoritma ACO untuk menghasilkan simulasi dengan solusi yang lebih optimal karena memiliki laju konvergensi yang cepat. Kata kunci: Jalur evakuasi, algoritma ant-colony optimization (ACO), algoritma quantum-inspired evolution (QEA), algoritma quantum ant-colony (QACA), feromon, simulasi

Cellular Automata Applications in Shortest Path Problem

Cellular Automata (CAs) are computational models that can capture the essential features of systems in which global behavior emerges from the collective effect of simple components, which interact locally. During the last decades, CAs have been extensively used for mimicking several natural processes and systems to find fine solutions in many complex hard to solve computer science and engineering problems. Among them, the shortest path problem is one of the most pronounced and highly studied problems that scientists have been trying to tackle by using a plethora of methodologies and even unconventional approaches. The proposed solutions are mainly justified by their ability to provide a correct solution in a better time complexity than the renowned Dijkstra's algorithm. Although there is a wide variety regarding the algorithmic complexity of the algorithms suggested, spanning from simplistic graph traversal algorithms to complex nature inspired and bio-mimicking algorithms, in this chapter we focus on the successful application of CAs to shortest path problem as found in various diverse disciplines like computer science, swarm robotics, computer networks, decision science and biomimicking of biological organisms' behaviour. In particular, an introduction on the first CA-based algorithm tackling the shortest path problem is provided in detail. After the short presentation of shortest path algorithms arriving from the relaxization of the CAs principles, the application of the CA-based shortest path definition on the coordinated motion of swarm robotics is also introduced. Moreover, the CA based application of shortest path finding in computer networks is presented in brief. Finally, a CA that models exactly the behavior of a biological organism, namely the Physarum's behavior, finding the minimum-length path between two points in a labyrinth is given.Comment: To appear in the book: Adamatzky, A (Ed.) Shortest path solvers. From software to wetware. Springer, 201

Algoritma Ant Colony Optimization (ACO) untuk Pemilihan Jalur Tercepat Evakuasi Bencana Gunung Lokon Sulawesi Utara

As one of the areas located on the ring of fire, North Sulawesi has a natural disaster threat level is high enough. Mount Lokon, located in the North Sulawesi region is an active volcano with fairly high seismic activity. Mount Lokon eruption disaster management such as casualty evacuation and early warning system towards the arrival of this eruption to be slow. This is caused by the difficulty of determining the fastest evacuation path resulting in many casualties. The purpose of this study was to determine the fastest track in the evacuation of the eruption of Mount Lokon. The method used is Ant-Colony Optimization is a method in computing, artificialintelligence and tried as one method of determining the shortest path effectively. The results obtained from this study is the Ant-Colony Algorithm method can be used to determine the fastest track in the evacuation process volcanic eruption where evacuation points and pathways that must be passed can be determined quickly and effectively in the evacuation process

The design and applications of the african buffalo algorithm for general optimization problems

Optimization, basically, is the economics of science. It is concerned with the need to maximize profit and minimize cost in terms of time and resources needed to execute a given project in any field of human endeavor. There have been several scientific investigations in the past several decades on discovering effective and efficient algorithms to providing solutions to the optimization needs of mankind leading to the development of deterministic algorithms that provide exact solutions to optimization problems. In the past five decades, however, the attention of scientists has shifted from the deterministic algorithms to the stochastic ones since the latter have proven to be more robust and efficient, even though they do not guarantee exact solutions. Some of the successfully designed stochastic algorithms include Simulated Annealing, Genetic Algorithm, Ant Colony Optimization, Particle Swarm Optimization, Bee Colony Optimization, Artificial Bee Colony Optimization, Firefly Optimization etc. A critical look at these ‘efficient’ stochastic algorithms reveals the need for improvements in the areas of effectiveness, the number of several parameters used, premature convergence, ability to search diverse landscapes and complex implementation strategies. The African Buffalo Optimization (ABO), which is inspired by the herd management, communication and successful grazing cultures of the African buffalos, is designed to attempt solutions to the observed shortcomings of the existing stochastic optimization algorithms. Through several experimental procedures, the ABO was used to successfully solve benchmark optimization problems in mono-modal and multimodal, constrained and unconstrained, separable and non-separable search landscapes with competitive outcomes. Moreover, the ABO algorithm was applied to solve over 100 out of the 118 benchmark symmetric and all the asymmetric travelling salesman’s problems available in TSPLIB95. Based on the successful experimentation with the novel algorithm, it is safe to conclude that the ABO is a worthy contribution to the scientific literature