Quantum Ant Colony Algorithm Research Articles

DQN-enabled content caching and quantum ant colony-based computation offloading in MEC

Computation-intensive and time-delay-sensitive tasks show explosive growth, which makes mobile user devices put forward higher requirements for such tasks. Mobile user devices are difficult to meet the task requirements due to limited computing capacity. To handle this situation, a mobile edge computing system requires to be constructed. Nevertheless, in the mobile edge computing system, if no popular content is cached in the edge server, users will repeatedly send requests to a remote cloud data center when requesting content, which will cause delay and backhaul link load. To handle this situation, this article plans to put forward a content caching policy. In this policy, the caching benefit in cache replacement, delay in transmission and backhaul link load is taken into account in the content cache. Then DQN is used to handle the problem to obtain the optimal content caching scheme. In addition, in the mobile edge computing environment, the computing capacity of mobile user devices is limited, so task data needs to be unloaded to the edge server for local or remote execution to improve the computing capacity. To handle the above problems, this article plans to put forward a quantum ant colony based computing offloading strategy. This strategy takes into account three aspects: delay, energy consumption and server cost. Quantum ant colony algorithm is used to handle the situation. Experimental results indicate that the content caching strategy based on DQN can effectively improve cache hit percentage, and cut down transmission delay and return link load. Quantum ant colony based computational offloading strategies can increase task completion rates, and decrease task completion delays, equipment energy consumption and total system cost.

Effective multi-controller management and adaptive service deployment strategy in multi-access edge computing environment

With the popularity of emerging mobile services such as virtual reality and online gaming, users expect higher quality immersive experiences. In response to the communication reliability and latency performance problems between controllers and switches due to single link failure in the network, a framework for multi-access edge computing systems based on software-defined networking (SDN) has emerged, and this paper proposes a reliable placement method for controllers based on deployment cost and link failure probability. Reliability and worst-case propagation delay model based on link failure probability is constructed. A controller placement strategy with minimum network cost is solved by a controller layout algorithm based on density-peak clustering. A mobile-aware service adaptive deployment method is also proposed in this paper to address the problems of long user-perceived delays and high migration costs due to the limitations of edge server resources and coverage areas and the dynamic nature of user movement. A mobility model based on the probability density function of sojourn time and a service deployment overhead model based on user mobility are constructed. A quantum ant colony algorithm is used to solve the service adaptive deployment scheme. The proposed algorithm is compared with the benchmark algorithm using the vehicle detection benchmark application service. Experimental results show that the proposed controller placement algorithm weighs the deployment cost, worst-case propagation delay and reliability of controllers and gives a reasonable number of controllers and their locations. The proposed deployment algorithm can optimise the quality of the user experience.

A Quantum Computing Based Numerical Method for Solving Mixed-Integer Optimal Control Problems

Mixed-integer optimal control problems (MIOCPs) usually play important roles in many real-world engineering applications. However, the MIOCP is a typical NP-hard problem with considerable computational complexity, resulting in slow convergence or premature convergence by most current heuristic optimization algorithms. Accordingly, this study proposes a new and effective hybrid algorithm based on quantum computing theory to solve the MIOCP. The algorithm consists of two parts: (i) Quantum Annealing (QA) specializes in solving integer optimization with high efficiency owing to the unique annealing process based on quantum tunneling, and (ii) Double-Elite Quantum Ant Colony Algorithm (DEQACA) which adopts double-elite coevolutionary mechanism to enhance global searching is developed for the optimization of continuous decisions. The hybrid QA/DEQACA algorithm integrates the strengths of such algorithms to better balance the exploration and exploitation abilities. The overall evolution performs to seek out the optimal mixed-integer decisions by interactive parallel computing of the QA and the DEQACA. Simulation results on benchmark functions and practical engineering optimization problems verify that the proposed numerical method is more excel at achieving promising results than other two state-of-the-art heuristics.

A quantum computing-based numerical method of mixed-integer optimal control problems under uncertainty for alkali–surfactant–polymer flooding

ABSTRACT This article presents a numerical method based on quantum computing to solve two unresolved key issues for alkali–surfactant–polymer (ASP) flooding in oil exploitation: uncertainties affecting development planning and a switch control regarding injection oil wells. First, a fuzzy multi-objective mixed-integer optimal control model describing the mechanism of ASP flooding is established. Then, an improved possibilistic programming algorithm is presented to remove fuzziness and transform the model into a deterministic single-objective mixed-integer nonlinear programming (MINLP) model. On this basis, a bi-level quantum computing algorithm including quantum annealing and quantum ant colony algorithm is proposed to solve the MINLP and acquire the optimal mixed-integer control variables. The superiority of the proposed algorithm is verified by testing a number of benchmark examples. Finally, the numerical method based on quantum computing is adopted to solve successfully a mixed-integer optimal control problem for ASP flooding under uncertainty.

The Inverse Solution Algorithm and Trajectory Error Analysis of Robotic Arm Based on MQACA-RBF Network

In order to improve the position accuracy and trajectory accuracy of a 6R robotic arm, a robot arm inverse solution algorithm based on the MQACA- (improved quantum ant colony-) RBF network is proposed. This algorithm establishes the prediction model through the neural network and uses the quantum ant colony algorithm to optimize the output weight. In order to solve the problem that the quantum ant colony algorithm has low convergence precision and easy to fall into the local optimal solution in the inverse solution algorithm of the multifreedom robotic arm, improved measures such as 2-opt local optimization and maximum minimum pheromone limit and variation are adopted. By comparing the simulation results of the 6R robotic arm simulation results and the simulation results based on ACA, QACA, and RBF neural networks on the position and motion trajectory of the space point, the advantages in precision are obvious. This proves the feasibility and effectiveness of the scheme.

A Novel Deep Feature Learning Method Based on the Fused-Stacked AEs for Planetary Gear Fault Diagnosis

Planetary gear is the key component of the transmission system of electromechanical equipment for energy industry, and it is easy to damage, which affects the reliability and operation efficiency of electromechanical equipment of energy industry. Therefore, it is of great significance to extract the useful fault features and diagnose faults based on raw vibration signals. In this paper, a novel deep feature learning method based on the fused-stacked autoencoders (AEs) for planetary gear fault diagnosis was proposed. First, to improve the data learning ability and the robustness of feature extraction process of AE model, the sparse autoencoder (SAE) and the contractive autoencoder (CAE) were studied, respectively. Then, the quantum ant colony algorithm (QACA) was used to optimize the specific location and key parameters of SAEs and CAEs in deep learning architecture, and multiple SAEs and multiple CAEs were stacked alternately to form a novel deep learning architecture, which gave the deep learning architecture better data learning ability and robustness of feature extraction. The experimental results show that the proposed method can address the raw vibration signals of planetary gear. Compared with other deep learning architectures and shallow learning architecture, the proposed method has better diagnosis performance, and it is an effective method of deep feature learning and fault diagnosis.

Global Path Planning for Unmanned Surface Vehicle Based on Improved Quantum Ant Colony Algorithm

As a tool to monitor marine environments and to perform dangerous tasks instead of manned vessels, unmanned surface vehicles (USVs) have extensive applications. Because most path planning algorithms have difficulty meeting the mission requirements of USVs, the purpose of this study was to plan a global path with multiple objectives, such as path length, energy consumption, path smoothness, and path safety, for USV in marine environments. A global path planning algorithm based on an improved quantum ant colony algorithm (IQACA) is proposed. The improved quantum ant colony algorithm is an algorithm that benefits from the high efficiency of quantum computing and the optimization ability of the ant colony algorithm. The proposed algorithm can plan a path considering multiple objectives simultaneously. The simulation results show that the proposed algorithm’s obtained minimum was 2.1–6.5% lower than those of the quantum ant colony algorithm (QACA) and ant colony algorithm (ACA), and the number of iterations required to converge to the minimum was 11.2–24.5% lower than those of the QACA and ACA. In addition, the optimized path for the USV was obtained effectively and efficiently.

Fidelity-Based Ant Colony Algorithm with Q-learning of Quantum System

Quantum ant colony algorithm (ACA) has potential applications in quantum information processing, such as solutions of traveling salesman problem, zero-one knapsack problem, robot route planning problem, and so on. To shorten the search time of the ACA, we suggest the fidelity-based ant colony algorithm (FACA) for the control of quantum system. Motivated by structure of the Q-learning algorithm, we demonstrate the combination of a FACA with the Q-learning algorithm and suggest the design of a fidelity-based ant colony algorithm with the Q-learning to improve the performance of the FACA in a spin-1/2 quantum system. The numeric simulation results show that the FACA with the Q-learning can efficiently avoid trapping into local optimal policies and increase the speed of convergence process of quantum system.

Design of Trusted Security Routing in Wireless Sensor Networks Based on Quantum Ant Colony Algorithm

To design an effective secure routing of trusted nodes in wireless sensor networks, quantum ant colony algorithm is applied to the design of large-scale wireless sensor network routing. The trustworthy network is used as the pheromone distribution strategy.Then, the pheromone is encoded by the quantum bit. The pheromone is updated by the quantum revolving door, and the energy consumption prediction is carried out to select the path. Finally, the trusted security routing algorithm of the wireless sensor network based on the global energy balance is realized. The quantum ant colony algorithm is superior to the traditional ant colony algorithm in algorithm convergence speed and global optimization. It can balance the energy consumption of the network node and can effectively resist the attacks such as Wormholes.It is very promising to apply the quantum ant colony algorithm to the routing algorithm of large scale wireless sensor networks.

Investigation on Optimal Control of Hybrid Vehicle Energy Management System Based on Multi-source Traffic Information

Energy shortage and air pollution is an important problem facing the world. One of the effective ways to mitigate it is to develop hybrid vehicles and other energy resource vehicles. However, traffic decision information needed by hybrid vehicle energy management system has become the major bottleneck to restricting hybrid vehicle's popularization, which shows the characteristics of multi-source, time varying and fuzzy. Hybrid vehicle Energy management problem using multi- source traffic information cycle of s is taken as the aim, numerical simulations and laboratory tests are the two methods to solve it in this project. First, dynamic extraction mechanism of multi-source traffic information, such as dynamic link travel time and vehicle path are studied based on support vector regression theory and quantum ant colony algorithm. And then, Markov theory is adopted to study the system level energy optimization modeling of hybrid vehicle energy management system based on multi-source traffic information. Finally, using multi-source traffic information, quantitative control model of energy optimization performance is constructed .This topic is aim to reveal the energy optimal law in hybrid vehicle energy management system using multi-source traffic information, which would lay the theoretical and technical basis for developing new intelligent energy management system suitable for the conditions of multi-source traffic information.

Evacuation path optimization based on quantum ant colony algorithm

Evacuation planning contains more than a few decisions which have to be made in a very short period of time and in the most appropriate way. Evacuation path optimization has vital importance in reducing the human and social harm and saving the aid time. Significant research efforts have been made in the literature to deal with evacuation optimization on the basis of deterministic optimization model, nevertheless the stochastic aspects or uncertainty of real-world evacuation have not been taken into account comprehensively. Inspired by the promising performance of heuristic algorithms to solve combinatorial problems, this paper proposes an improved quantum ant colony algorithm (QACA) for exhaustive optimization of the evacuation path that people can evacuate from hazardous areas to safe areas. In comparison with ACO (ant colony optimization) based method, QACA has the capability of finding a good solution faster using fewer individuals and possesses strong robustness, as a result of the quantum representation and updating of pheromone. Experiment results show that the proposed approach executes more effectively during evacuation.

An Improved Quantum Ant Colony Algorithm of Solving Nonlinear Equation Groups

In this paper, according to the characteristics of nonlinear equations. A new algorithm, Quantum Ant Colony Algorithm (QACA) is proposed. The core is that Q-bit and quantum rotation gate adopted in QEA are introduced into ACS to represent and update the pheromone respectively, so it has better diversity and global search capacity. The experimental result demonstrates that QACA can get better solutions to nonlinear equations.

Image segmentation Based on Chaotic Quantum Ant Colony Algorithm

Ant colony algorithm is a new type of biomimetic evolutionary algorithm, which has some outstanding characteristics like good robustness, parallelism and positive feedback. It has been widely used in many fields but it still has some weaknesses, such as slow-convergence and easily-falling into local extreme value. Therefore we propose a new algorithm which combines the quantum evolutionary algorithm and ant colony algorithm together based on these shortcomings, this new type of algorithm considers the two quantum bit probability amplitudes as ants’ current location information. The searching space will be doubled when ants’ numbers remain the same. Both introducing the pixel point gradient into quantum revolving door, and dynamically changing rotation angle are to achieve local-searching. Searching by chaotic quanta near candidates with optimal solution is to improve the global optimization. Meanwhile, adopting a NAND gate is to achieve mutating operation, and to avoid algorithm’s precocity. Compared with the traditional algorithm, the new algorithm has a better population diversity and an outstanding ability to overcome the precocity and stagnation in the optimization process. It has been proved that this improved algorithm is effectively to the problems like slow-convergence and easily-falling into local extreme value, and vividly increase the speed and accuracy of the image segmentation.

Image segmentation Based on Chaotic Quantum Ant Colony Algorithm

Image segmentation Based on Chaotic Quantum Ant Colony Algorithm

Solving the Tramp Scheduling Problem with Quantum Ant Colony Algorithm

Solving the Tramp Scheduling Problem with Quantum Ant Colony Algorithm

Improved Quantum Ant Colony Algorithm based on Bloch Coordinates

The Ant Colony Algorithm is an effective method for solving combinatorial optimization problems. However, in practical applications, there also exist issues such as slow convergence speed and easy to fall into local extremum. This paper proposes an improved Quantum Ant Colony Algorithm based on Bloch coordinates by combining Quantum Evolutionary Algorithm with Ant Colony Algorithm. In this algorithm, the current position information of ants is represented by the Bloch spherical coordinates of qubits; position update, position variation and random behavior of ants are all achieved with quantum rotation gate. Simulations of function extremum problem, TSP problem and QoS multicast routing problem were conducted, the results indicated that the algorithm could overcome prematurity, with a faster convergence speed and higher solution accuracy.

Image Edge Detection Using Quantum Ant Colony Optimization

Ant colony optimization algorithm (ACO) which performs well in discrete optimization has already been used widely and successfully in digital image processing. Slow convergence, however, is an obvious drawback of the traditional ACO. A quantum ant colony algorithm (QACO), based on the concept and principles of quantum computing can overcome this defect. In this study, a QACO-based edge detection algorithm is proposed. Quantum bit (qubit) and quantum rotation gate are introduced into QACO to represent and update the pheromone respectively. Experiments and comparisons show that QACO is an efficient and effective approach in image edge detection.

An Improved Quantum Ant Colony Algorithm and its Application

The algorithm in this paper is based on the combination of Quantum Evolutionary algorithm (QEA) and Ant Colony System (ACS), a new algorithm, Quantum Ant Colony Algorithm (QACA) is proposed in this paper. The core is that Q-bit and quantum rotation gate adopted in QEA are introduced into ACS to represent and update the pheromone respectively, so it has better diversity and global search capacity. The experimental result demonstrates that QACA can get better solutions to some Traveling Salesman Problems (TSP) than the solutions given in TSPLIB