An Adaptive Dynamic Ant System Based on Acceleration for TSP

To address the critical industry pain points in streaming data scenarios across industrial production, intelligent transportation and other related fields—namely continuous performance degradation of classification models induced by concept drift, the absence of dedicated closed-loop drift adaptation processing capability in mainstream stream processing systems, poor engineering implementability of specialized tools, and the inherent difficulty in balancing real-time performance and classification accuracy—this paper designs and implements a Dynamic Adaptive Concept Drift Processing System (DACPS) for streaming data classification. The system adopts a hierarchical modular architecture, integrating five core functional modules: streaming data preprocessing, real-time concept drift detection, dynamic sample optimization, incremental model training, and full-process management and control. This architecture enables fully automated and adaptive processing for the end-to-end workflow of streaming data classification and prediction. Extensive multi-dimensional functional tests, specialized performance tests, and scenario-based validation demonstrate that the system can stably adapt to diverse types of concept drift scenarios, and meets all design specifications in terms of classification accuracy, convergence speed, operating efficiency, and resource occupation control. Meanwhile, the system is equipped with an accessible visual interactive interface, making it widely adaptable to the streaming data classification and processing requirements across multiple application domains.

It is challenging to design, develop, and validate a dynamically adaptive system (DAS) that satisfies requirements, particularly when requirements can change at run time. Testing at design time can help verify and validate that a DAS satisfies its specified requirements and constraints. While offline tests may demonstrate that a DAS is capable of satisfying its requirements before deployment, a DAS may encounter unanticipated system and environmental conditions that can prevent it from achieving its objectives. In working towards a requirements-aware DAS, this paper proposes run-time monitoring and adaptation of tests as another technique for evaluating whether a DAS satisfies, or is even capable of satisfying, its requirements given its current execution context. To this end, this paper motivates the need and identifies challenges for adaptively testing a DAS at run time, as well as suggests possible methods for leveraging offline testing techniques for verifying run-time behavior.

This is where the abstract should be placed. It should consist of one paragraph and a concise summary of the material discussed in the article below. It is preferable not to use footnotes in the abstract or the title. The acknowledgement for funding organisations etc. is placed in a separate section at the end of the text. We wish you success with the preparation of your manuscript. The ant colony algorithm (ACA ) is a simulated evolutionary algorithm , which is inspired by real ants foraging in natural world. In this paper, it has effectively solved the problem of precocity and halting of the ant colony algorithm, taking use of the global and rapidity of the PSO. Meanwhile, it can also judge the standard of the route by use of the eliminating- cross. Through classic experiments about Traveling Salesman Problem, the optimization algorithm has the better astringency, robustness and efficiency.

Self-adaptation is emerging as an increasingly important capability for many applications, particularly those deployed in dynamically changing environments, such as ecosystem monitoring and disaster management. One key challenge posed by Dynamically Adaptive Systems (DASs) is the need to handle changes to the requirements and corresponding behavior of a DAS in response to varying environmental conditions. Berry et al. previously identified four levels of RE that should be performed for a DAS. In this paper, we propose the Levels of RE for Modeling that reify the original levels to describe RE modeling work done by DAS developers. Specifically, we identify four types of developers: the system developer, the adaptation scenario developer, the adaptation infrastructure developer, and the DAS research community. Each level corresponds to the work of a different type of developer to construct goal model(s) specifying their requirements. We then leverage the Levels of RE for Modeling to propose two complementary processes for performing RE for a DAS. We describe our experiences with applying this approach to GridStix, an adaptive flood warning system, deployed to monitor the River Ribble in Yorkshire, England.

The shortest path between the start node and end node plays an important role in city’s road traffic network analysis system. The basic ant colony system algorithm which is a novel simulated evolutionary algorithm is studied to solve the shortest path problem. But the basic ant colony system algorithm is easy to run into the local optimum solution for shortest path. In order to solve the problem, the improved ant colony system algorithm is proposed. The improvement methods for selection strategy, local search, and information quantity modification of basic ant colony system are discussed in detail. The experiments are done in Beijing road network in China. The results of experiments show that comparing with the basic ant colony algorithm, the improved algorithm can easily converge at the global optimum for the shortest path.

Little has been written in the new product development literature about the simulation technique agent‐based modeling, which is a by‐product of recent explorations into complex adaptive systems in other disciplines. Agent‐based models (ABM) are commonly used in other social sciences to represent individual actors (or groups) in a dynamic adaptive system. The social system may be a marketplace, an organization, or any type of system that acts as a collective of individuals. Agents represent autonomous decision‐making entities that interact with each other and/or with their environment based on a set of rules. These rules dictate the behavioral choices of the agents. In these simulation models, heterogeneous agents interact with each other in a repetitive process. It is from the interactions between agents that aggregate macroscale behaviors or trends emerge. The simulated environment can be thought of as a “virtual” society in which actions taken by one agent may have an effect on the resulting actions of another agent.This article is an introduction to the ABM methodology and its possible uses for innovation and new product development researchers. It explores the benefits and issues with modeling dynamic systems using this methodology. Benefits of ABMs found in sociology and management studies have found that as the heterogeneity of individuals increase in a system or as network effects become more important in a system, the effectiveness of ABMs as a methodology increases. Additionally, the more adaptive a system or the more the system evolves over time, the greater the opportunity to learn more about the adaptive system using ABMs. Limitations to using this methodology include some knowledge of computer‐programming techniques.Three potential areas of research are introduced: diffusion of innovations, organizational strategy, and knowledge and information flows. A common use of ABMs in the extant literature has been the modeling of the diffusion process between networked heterogeneous agents. ABMs easily allow the modeling of different types of networks and the impact of these networks on the diffusion process. A demonstrative example of an agent‐based model to address the research question of how should manufacturers allocate resources to research (exploration) and development (exploitation) projects is provided. Future courses of study using ABMs also are explored.

With the continuous development of mobile robot technology, its application fields are becoming increasingly widespread, and path planning is one of the most important topics in the field of mobile robot research. This paper focused on the study of the path planning problem for mobile robots in a complex environment based on the ant colony optimization (ACO) algorithm. In order to solve the problems of local optimum, susceptibility to deadlocks, and low search efficiency in the traditional ACO algorithm, a novel parallel ACO (PACO) algorithm was proposed. The algorithm constructed a rank-based pheromone updating method to balance exploration space and convergence speed and introduced a hybrid strategy of continuing to work and killing directly to address the problem of deadlocks. Furthermore, in order to efficiently realize the path planning in complex environments, the algorithm first found a better location for decomposing the original problem into two subproblems and then solved them using a parallel programming method-single program multiple data (SPMD)-in MATLAB. In different grid map environments, simulation experiments were carried out. The experimental results showed that on grid maps with scales of 20 $ \times $ 20, 30 $ \times $ 30, and 40 $ \times $ 40 compared to nonparallel ACO algorithms, the proposed PACO algorithm had less loss of solution accuracy but reduced the average total time by 50.71, 46.83 and 46.03%, respectively, demonstrating good solution performance.

Component-based software engineering has been continuously improved and successfully applied over the past years. Future systems, like ultra-large scale systems, are a vast array of decentralized, distributed, autonomic, heterogeneous, organically grown and continually evolving subsystems respectively components. Components may join or leave these systems during the life cycle of these systems, even at runtime. Despite this enormous complexity, we depend more and more on these dynamic adaptive systems. Hence we have to care about dependability although the systems are evolving at runtime. To achieve dependable dynamic adaptive systems which support hot plug and binding of components during runtime we claim to integrate specific concepts, like runtime testing, into the component infrastructure. In this paper we will show that existing approaches cannot guarantee the correctness of component bindings in dynamic adaptive systems. Therefore we introduce a technique for runtime testing and show how it can be integrated into a component-based approach.

An adaptive dynamic focusing system used for the ultrasonic nondestructive testing of pipeline girth welds is developed. This system relies on ultrasonic linear phased array transducers connected to a multichannel acquisition system, which provides delay and amplitude laws, allowing one to control the ultrasonic beams dynamically. We introduce a testing method for welds and the system’s structure. Optimization of array transducers has also been pursued based on a mathematical model of the acoustic field for a linear phased array. The experimental results have proved that the phased array transducers system has the same capability to detect as a conventional ultrasonic transducers system, but the components of the system can be simplified greatly, and the testing flexibility and the testing speed can be improved greatly. It provides an excellent basis for further research and development of defect inspection of pipeline girth welds.

Simulated Evolution algorithm is versatile, efficient but very much time consuming. This paper shows that the reduction of trials in the allocation phase leads to the improvement of the performances of Simulated Evolution algorithm. In its application to the cell placement problem of VLSI chip, 90% reduction of the solution space in the allocation phase accelerates the total processing time, 4.6 ~ 7.7 times and improves the solution quality, 1.6 ~ 6.4% when compared with an exhaustive search of the solution space. This result is also useful for implementing Simulated Evolution algorithm on an FPGA.

Ant colony optimization (ACO) algorithm is a metaheuristic inspired by the behavior of real ants in their search for the shortest path to food sources. The ACO algorithm takes on these characteristics of robust, positive feedback distributed computing, easy fusing with other algorithms. But the basic ACO algorithm has some deficiencies of premature and stagnation phenomenon in the evolution process, and is easily trapped into local optimal solution. And it is difficult to explore other solutions in the neighbor space. So a improved ACO(DPSEMACO) algorithm based on dual population strategy, bi-directional dynamic adjust evaporation factor strategy of the pheromone and parallel strategy is proposed to solve the traveling salesman problem(TSP). In the DPSEMACO algorithm, the ants are divided into the two subpopulations by borrowing the mutual cooperation mechanism of biological community, which evolve separately and exchange information timely. The bi-directional dynamic adjusting evaporation factor strategy of the pheromone is used to change the corresponding path pheromone of different subpopulations in order to avoid to trap into a local optimum. The parallel strategy can avoid falling into a local optimum. And the DPSEMACO algorithm can expand the search space and improve the overall searching performance by repeated changing the pheromone of the each subpopulation and adaptive adjusting evaporation factor. Finally, in order to prove the optimization performance of the proposed DPSEMACO algorithm, some classic TSP instances are selected from the TSPLIB in this paper. And some existing methods are selected to compare the optimization performance with the proposed DPSEMACO algorithm. The experimental results demonstrate that the proposed DPSEMACO algorithm is feasible and effective in solving TSP, and takes on a good global searching ability and high convergence speed.

Runtime testing of context-aware variability in adaptive systems

A best-path-updating information-guided ant colony optimization algorithm

Abstract. With the advancement of urbanization, urban-rural public transport issues have become one of the most critical issues in urban development. The paper makes a detailed study on the optimization of urban-rural bus routes in Erqi District of Zhengzhou, China. The factors of bus stop selection are analyzed, and the three categories, including traffic road condition factors, economic benefit factors and waiting number factors, are mainly considered. The analytic hierarchy process is used to determine 35 specific objectives of urban-rural bus stop optimization, 20 of which are selected for simulation experiment with large weight. Then the ant colony optimization (ACO) algorithm in path optimization is analyzed, which has the following two advantages. First, the global pheromone update is combined with the local pheromone update to enhance the algorithm's optimization ability and convergence speed. Second, through the method of spatial contraction transformation, the ant constructs a solution to reduce the number of construction steps and speed up the operation. Based on the actual analysis of urban-rural public transportation in the Erqi District of Zhengzhou, a simulation experiment is executed to show that the ACO algorithm is able to find out the optimal path, which is 15.1% shorter than the ant colony system (ACS) algorithm. The ACO algorithm improved path planning has good time effectiveness and path practicability.

Components within service-oriented systems require a mechanism to get in touch with offered services. A traditional approach from the field of Web services is a discovery agency provided by the infrastructure, where service providers register and service requestors submit their queries. In the field of dynamic adaptive systems we deal with systems which change their behavior according to the needs of their users during runtime based on context information. Therefore, the wiring of service providers and service requestors - known as the system configuration - may change often. As a consequence of the dynamics it is reasonable not to let the service requestor take care about the service discovery. Instead we propose a component model enabling the proactive configuration of service-oriented systems which will be described in this paper.