Task optimization and scheduling of distributed cyber–physical system based on improved ant colony algorithm
TL;DR: The simulation results show that the proposed algorithm model enhances the local search ability and improves the quality of the task scheduling problem, and has good effectiveness, stability and adaptability.
About: This article is published in Future Generation Computer Systems.The article was published on 2020-08-01. It has received 92 citations till now. The article focuses on the topics: Ant colony optimization algorithms & Job shop scheduling.
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TL;DR: In this article, an improved ant colony algorithm is proposed for robot path planning under a static environment, where the grid method is used to establish workspace model of the robot, and the strategy of backspace from traps and the meeting strategy are applied to path planning of mobile robot, so it avoided path-locked situation as well as improved the efficiency of planning optimal path.
Abstract: An improved ant colony algorithm is proposed for robot path planning under a static environment.Grid method is used to establish workspace model of the robot.Furthermore,the strategy of backspace from traps and the meeting strategy were applied to path planning of mobile robot,so it avoided path-locked situation as well as improved the efficiency of planning optimal path.The simulation results show that the performance of path planning can be obviously improved by the proposed ant colony algorithm,and the algorithm is very simple and efficient.
123 citations
TL;DR: A multi-stage game model for FDIA is proposed based on attacker’s perspective, putting particular emphasis on data tampering, strategic adjustments, and multi-path attacks in a multi- stage game.
29 citations
TL;DR: An effective triple-phase generate route method (TPGR) is proposed to produce a feasible multimodal transport path sequence based on an AOG and an improved ant colony algorithm is developed to effectively generate a set of Pareto solutions for route selection, which are used for the dual-objective multimodAL transport route optimization problem.
Abstract: Path sequence selection is important for multimodal transport processes. AND/OR graphs (AOG) have been applied to describe practical multimodal transport route planning problems by using `AND' and `OR' matrices. An AOG-based multimodal transport route planning problem is an NP-hard combinatorial optimization problem. Heuristic evolution methods can be adopted to handle it. While adjacency (AND) relationship issues can be addressed, contradiction (OR) relations are not well addressed by existing multimodal transport route planning methods. Thus, an ineffective result may be obtained in practice. The OR matrix is a conflict matrix that describes the choice of mode of transport in the process of multimodal transport. By using a contradiction matrix together with an adjacency matrix and tabu list, an approach used in existing work, this paper proposes an effective triple-phase generate route method (TPGR) to produce a feasible multimodal transport path sequence based on an AOG. This paper uses energy consumption to evaluate the multimodal transport energy efficiency. The information entropy is applied to describe the risks of the transport process. The energy consumption and the information entropy lead to a novel dual-objective optimization model where route energy consumption and route risk are minimized. An improved ant colony algorithm is developed to effectively generate a set of Pareto solutions for route selection, which are used for the dual-objective multimodal transport route optimization problem. This methodology is applied to practical multimodal transport route selection processes on two maps to verify its effectiveness and feasibility.
16 citations
TL;DR: Wang et al. as mentioned in this paper designed an adaptive task scheduling algorithm for cloud computing based on ant colony algorithm, and a pheromone adaptive update adjustment mechanism was added to improve the convergence speed of the algorithm and effectively avoid the emergence of local optimal solutions.
15 citations
TL;DR: In this paper, a novel urban tourism path planning method based on a multiobjective genetic algorithm is proposed, which enhances the accuracy of the GA by adopting new parameters and selecting the optimal tourism path by combining external and internal tourist site potentials.
Abstract: One of the most important variables that leads to effective individual and group tours is the tourism route planning approach, which enables tourists to engage with tourism with ease, speed, and safety. However, current methods of designing tourist routes have some glitches, such as relying only on external objectives to find the best route. In this paper, a novel urban tourism path planning method based on a multiobjective genetic algorithm is proposed. The main goal of this paper is to enhance the accuracy of the genetic algorithm (GA) by adopting new parameters and selecting the optimal tourism path by combining external and internal tourist site potentials. Moreover, the GA and analytical hierarchy process (AHP) were used in our proposed approach to evaluate urban tourism route planning under multiple conflicting objectives. To visualize and execute the proposed approach, the geographic information system (GIS) environment was used. Our suggested approach has been applied to develop the tourist road network of Chengdu City in China. Compared with existing tourism path planning approaches, our proposed approach is more accurate and straightforward than other approaches used to choose routes.
10 citations
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TL;DR: An overview of recent advances on security control and attack detection of industrial CPSs is presented, and robustness, security and resilience as well as stability are discussed to govern the capability of weakening various attacks.
663 citations
TL;DR: The experiment results show that the proposed ICMPACO algorithm can effectively obtain the best optimization value in solving TSP and effectively solve the gate assignment problem, obtain better assignment result, and it takes on better optimization ability and stability.
Abstract: In this paper, an improved ant colony optimization (ICMPACO) algorithm based on the multi-population strategy, co-evolution mechanism, pheromone updating strategy, and pheromone diffusion mechanism is proposed to balance the convergence speed and solution diversity, and improve the optimization performance in solving the large-scale optimization problem. In the proposed ICMPACO algorithm, the optimization problem is divided into several sub-problems and the ants in the population are divided into elite ants and common ants in order to improve the convergence rate, and avoid to fall into the local optimum value. The pheromone updating strategy is used to improve optimization ability. The pheromone diffusion mechanism is used to make the pheromone released by ants at a certain point, which gradually affects a certain range of adjacent regions. The co-evolution mechanism is used to interchange information among different sub-populations in order to implement information sharing. In order to verify the optimization performance of the ICMPACO algorithm, the traveling salesmen problem (TSP) and the actual gate assignment problem are selected here. The experiment results show that the proposed ICMPACO algorithm can effectively obtain the best optimization value in solving TSP and effectively solve the gate assignment problem, obtain better assignment result, and it takes on better optimization ability and stability.
421 citations
TL;DR: A review of the state-of-the-art of distributed filtering and control of industrial CPSs described by differential dynamics models is presented and some challenges are raised to guide the future research.
Abstract: Industrial cyber-physical systems (CPSs) are large-scale, geographically dispersed, and life-critical systems, in which lots of sensors and actuators are embedded and networked together to facilitate real-time monitoring and closed-loop control. Their intrinsic features in geographic space and resources put forward to urgent requirements of reliability and scalability for designed filtering or control schemes. This paper presents a review of the state-of-the-art of distributed filtering and control of industrial CPSs described by differential dynamics models. Special attention is paid to sensor networks, manipulators, and power systems. For real-time monitoring, some typical Kalman-based distributed algorithms are summarized and their performances on calculation burden and communication burden, as well as scalability, are discussed in depth. Then, the characteristics of non-Kalman cases are further disclosed in light of constructed filter structures. Furthermore, the latest development is surveyed for distributed cooperative control of mobile manipulators and distributed model predictive control in industrial automation systems. By resorting to droop characteristics, representative distributed control strategies classified by controller structures are systematically summarized for power systems with the requirements of power sharing and voltage and frequency regulation. In addition, distributed security control of industrial CPSs is reviewed when cyber-attacks are taken into consideration. Finally, some challenges are raised to guide the future research.
376 citations
TL;DR: The concept and characteristics of CPS are introduced and the present situation of CPS researches are analyzed; the development of CPS is discussed from perspectives of system model, information processing technology and software design.
Abstract: Cyber-physical systems U+0028 CPS U+0029 are complex systems with organic integration and in-depth collaboration of computation, communications and control U+0028 3C U+0029 technology. Subject to the theory and technology of existing network systems and physical systems, the development of CPS is facing enormous challenges. This paper first introduces the concept and characteristics of CPS and analyzes the present situation of CPS researches. Then the development of CPS is discussed from perspectives of system model, information processing technology and software design. At last it analyzes the main obstacles and key researches in developing CPS.
305 citations
TL;DR: A digital twin-driven manufacturing cyber-physical system (MCPS) for parallel controlling of smart workshop under mass individualization paradigm is presented and addresses a bi-level online intelligence in proactive decision making for the organization and operation of manufacturing resources.
Abstract: With increasing diverse product demands, the manufacturing paradigm has been transformed into a mass-individualized one, among which one bottleneck is to achieve the interoperability between physical world and the digital world of manufacturing system for the intelligent organizing of resources. This paper presents a digital twin-driven manufacturing cyber-physical system (MCPS) for parallel controlling of smart workshop under mass individualization paradigm. By establishing cyber-physical connection via decentralized digital twin models, various manufacturing resources can be formed as dynamic autonomous system to co-create personalized products. Clarification on the MCPS concept, characteristics, architecture, configuration, operating mechanism and key enabling technologies are elaborated, respectively. A demonstrative implementation of the digital twin-driven parallel controlling of board-type product smart manufacturing workshop is also presented. It addresses a bi-level online intelligence in proactive decision making for the organization and operation of manufacturing resources.
292 citations