Search-Based test case prioritization for simulation-Based testing of cyber-Physical system product lines

In the software product line research, product variants typically differ by their functionality and quality attributes are not purposefully varied. The goal is to study purposeful performance variability in software product lines, in particular, the motivation to vary performance, and the strategy for realizing performance variability in the product line architecture. The research method was a theory-building case study that was augmented with a systematic literature review. The case was a mobile network base station product line with capacity variability. The data collection, analysis and theorizing were conducted in several stages: the initial case study results were augmented with accounts from the literature. We constructed three theoretical models to explain and characterize performance variability in software product lines: the models aim to be generalizable beyond the single case. The results describe capacity variability in a base station product line. Thereafter, theoretical models of performance variability in software product lines in general are proposed. Performance variability is motivated by customer needs and characteristics, by trade-offs and by varying operating environment constraints. Performance variability can be realized by hardware or software means; moreover, the software can either realize performance differences in an emergent way through impacts from other variability or by utilizing purposeful varying design tactics. The results point out two differences compared with the prevailing literature. Firstly, when the customer needs and characteristics enable price differentiation, performance may be varied even with no trade-offs or production cost differences involved. Secondly, due to the dominance of feature modeling, the literature focuses on the impact management realization. However, performance variability can be realized through purposeful design tactics to downgrade the available software resources and by having more efficient hardware.

Product line test development is more complicated than the conventional test development for a single application. There were numerous research works in the past that address various issues and aspects that arise in product line testing and test development. However, we still lack a coherent framework that can guide product line test development and link various product line development concepts to relevant product line testing concepts. In this paper we provide a basis for a formal framework for product line test development by linking product line development concepts such as feature, variability, product line architecture, component and use case scenario to product line test concepts such as test architecture, variability for test and test scenario and by providing a systematic way for deriving product line tests, adapting product line tests to a specific product and deriving product specific tests.

Training a neural network is a difficult optimization problem because of the nonconvex objective function. Therefore, as an alternative to local search algorithms, many global search algorithms have been used to train neural networks. However, local search algorithms are more efficient with computational resources, and therefore numerous random restarts with a local algorithm may be more effective than a global algorithm at obtaining a low value of the objective function. This study examines, through Monte-Carlo simulations, the relative efficiency of a local search algorithm to 8 stochastic global algorithms: 2 simulated annealing algorithms, 1 simple random stochastic algorithm, 1 genetic algorithm and 4 evolutionary strategy algorithms. The results show that even ignoring the computational requirements of the global algorithms, there is little evidence to support the use of the global algorithms examined for training neural networks.

Non-convex optimization problems are prevalent in scientific and engineering applications, often characterized by multiple local minima that challenge standard optimization techniques. Global optimization methods, such as Genetic Algorithms (GA) (1992) [4] and Particle Swarm Optimization (PSO) (1995) [6], provide robust global exploration but tend to converge slowly. Conversely, local search methods, such as Newton’s method (2004) [1], (2006) [7], and the Conjugate Gradient (CG) (1964) [3] method, offer rapid convergence but are susceptible to local minima. In this paper, we present four hybrid optimization algorithms that integrate the exploratory strength of global algorithms with the refinement ability of local methods. The proposed hybrids, Genetic Algorithms (GA) + Newton, GA + CG, PSO + Newton, and PSO + CG, are evaluated against their standalone counterparts on standard benchmark functions (2013) [5], including Rosenbrock, Rastrigin, Ackley, and Himmelblau. This work builds upon our previous research on the hybridization of two global search algorithms, the Nelder–Mead (simplex) algorithm and the Bat algorithm, which resulted in the Hybrid Simplex Bat Algorithm (HSBA) [2], recently Published. Results demonstrate that the hybrid approaches consistently outperform traditional methods in terms of accuracy, convergence rate, and computational efficiency.

Optimal design of an autonomous underwater helicopter's shape based on combinatorial optimization strategy

As the key technique to build domain-specific search engines, focused crawling has drawn a lot of attention from researchers in the past decade. However, as Web structure analysis techniques advance, several problems in traditional focused crawler design were revealed and they could result in domain-specific collections with low quality. In this work, we studied the problems of focused crawling that are caused by using local search algorithms. We also proposed to use a global search algorithm, the Genetic Algorithm, in focused crawling to address the problems. We conducted evaluation experiments to examine the effectiveness of our approach. The results showed that our approach could build domain-specific collections with higher quality than traditional focused crawling techniques. Furthermore, we used the concept of Web communities to evaluate how comprehensively the focused crawlers could traverse the Web search space, which could be a good complement to the traditional focused crawler evaluation methods.

Optimization Solution Task is a typical and important task in many applications. Many optimization problems have been proved to be NP-hard problems, which cannot be solved by some predefined mathematic formulae. In this case, computer aided method is very helpful. While some local search algorithms are easily to fall into a local optimum solution. On contrast, the population based methods, such as Genetic Algorithms, Artificial Immune System, Autonomy Oriented Computing, are global search algorithms. However, they are not good at the local search. In this paper, an improved method is proposed by combining the local and global search ability, so as to improve the performance in terms of the convergence speed and the convergence reliability. We construct a generic form to deal with the common objective function space or the objective function with the partial derivative. In addition, we present an n-hold method in population based evolution method. The experiments indicate that our approach can effectively improve the convergence reliability, which is much concerned in some applications with the expensive executing expense.

Harmony Search (HS) algorithm is a new meta-heuristic global search algorithm. To solve the algorithm easily fall into the local optimal problem, this paper proposes an adaptive novel global harmony search (ANGHS) algorithm based on NGHS algorithm. First, a new location update strategy is given using the adjustment parameter F. Then, the mutation operation is introduced in the case of excluding the small probability Pm. In order to avoid blind adjustment of the step size in the late search and effectively adjust the structure of the solution, the definition of the norm is introduced to adjust parameter bw dynamically. In this paper, 7 standard Benchmark functions are used to simulate the experiment. The results show that the algorithm avoids the premature problem of the algorithm and enhances the global search ability of the algorithm, with obvious improvement in convergence speed and stability, and has good optimize performance.

Software product line (SPL) testing is a challenging task, due to the huge number of variants sharing common functionalities to be taken into account for efficient testing. By adopting the concept of regression testing, incremental SPL testing strategies cope with this challenge by exploiting the reuse potential of test artifacts between subsequent variants under test. In previous work, we proposed delta-oriented test case prioritization for incremental SPL integration testing, where differences between architecture test model variants allow for reasoning about the order of reusable test cases to be executed. However, the prioritization left two issues open, namely (1) changes to component behavior are ignored, which may also influence component interactions and, (2) the weighting and ordering of similar test cases result in an unintended clustering of test cases. In this paper, we extend the test case prioritization technique by (1) incorporating changes to component behavior allowing for a more fine-grained analysis and (2) defining a dissimilarity measure to avoid clustered test case orders. We prototyped our test case prioritization technique and evaluated its applicability and effectiveness by means of a case study from the automotive domain showing positive results.

Cloud manufacturing as an emerging service-oriented manufacturing paradigm integrates and manages geographically distributed manufacturing resources such that complex and highly customized manufacturing tasks can be performed cooperatively. The service composition and optimal selection (SCOS) problem, in which manufacturing cloud services are optimally selected for performing subtasks, is one of the key issues for implementing a cloud manufacturing system. In this paper, we propose a new mixed-integer programming model for solving the SCOS problem with sequential composition structure. Unlike the majority of previous research on the problem, in the proposed model, the transportation between distributed resources and its effects on quality of services are considered. Although a wide variety of metaheuristics have been tailored for solving the SCOS problem, no consistent and comprehensive conclusion has been reached so far on the superiority of a specific algorithm. Therefore, for the first time, solution space landscape of the problem was analyzed through several statistical criteria which demonstrated that the landscape is rugged and local optima are clustered in a small region of the search space. Therefore, to find good solutions, a metaheuristic algorithm needs to perform both proper exploitation and exploration of the search space. According to the landscape analysis, the basic imperialist competitive algorithm (ICA) was hybridized with a local search (LS) algorithm resulting in the hybrid ICA (HICA). To examine the performance of the proposed HICA, an example of online motorcycle production in the USA as well as four randomly generated large-scale instances, were solved through the LS, ICA, and HICA. Computational results showed that transportation consideration is indispensable for obtaining more realistic solutions in cloud manufacturing. The results also revealed that the HICA outperformed the LS and basic ICA in terms of the value of cost objective function, the stability of solutions and convergence speed. Hence, not only statistically but also analytically, it was proved that algorithms incorporating both exploitation and exploration are able to solve the SCOS problem more efficiently.

A hybrid Particle Swarm Optimization – Variable Neighborhood Search algorithm for Constrained Shortest Path problems

An investigation is presented that considers various aspects of an aerodynamic shape optimization framework. A two-dimensional aerofoil transonic zero-lift drag minimization test case is used to investigate the effect of dimensionality, shape deformation parameters, and optimizer on the results from the shape optimization process. A flexible control point-based parameterization is implemented which decouples the design variables from the surface, such that control point deformations determine the surface and volume mesh deformations in a unified manner. A gradient-based optimizer (feasible sequential quadratic programming) and global search algorithm (gravitational search algorithm) are tested on the constrained optimization case. The results show, as expected, that an increase in the number of dimensions produces a greater design space coverage and better optimization results, and the gradient-based method is prone to terminating in local optima or at constraint boundaries, so the global search algorithm is more reliable at locating optima. Efficient, reduced, and orthogonal shape deformation parameters are defined here by singular value decomposition extraction, and are shown to be particularly effective, demonstrating a 99.7% drag reduction for the case considered.

Behavioral UML models like sequence diagrams (SD) lack sufficient formal semantics, making it difficult to build automated tools for their analysis, simulation and validation. A common approach to circumvent the problem is to map these models to more formal representations. In this context, many works propose a rule-based approach to automatically translate behavioral models like SD into colored Petri nets (CPN). However, finding the rules for such SD-to-CPN transformations manually may be difficult, as the transformation rules are usually not obviously defined. We propose a solution that starts from the hypothesis that examples of good transformation of SD-to-CPN can be useful to automatically generate transformation rules. To this end, we describe an automated approach to find the rules that best match the meta-model elements of SD to corresponding elements in the CPN meta-model. Thus, our approach starts by randomly generating a set of rules, executing them to generate some target models. Then, it evaluates the quality of the proposed solution (rules) by comparing the generated target models to the expected ones in the base of examples. In this case, the search space is large and heuristic-search is needed. To achieve our goal, we combine two algorithms for global and local search, namely Particle Swarm Optimization (PSO) and Simulated Annealing (SA). Our empirical results show that the generated rules derive CPNs similar to the expected ones.

Two key issues in local search algorithms are the definition of a neighborhood and the way to examine it. In this chapter we consider techniques for examining very large neighborhoods, in particular, ways for exactly searching them. We first illustrate such techniques using three paradigmatic examples. In the largest part of the chapter, we focus on the development and experimental study of very largescale neighborhood search algorithms for two coloring problems. The first example concerns the well-known (vertex) graph coloring problem. Despite initial promising results on the use of very large-scale neighborhoods, our final conclusion was negative: the usage of the proposed very large-scale neighborhoods did not help to improve the performance of effective stochastic local search algorithms. The second example, the graph set T-coloring problem, yielded more positive results. In this case, a very large-scale neighborhood that was specially tailored for this problem and that can be efficiently searched, resulted to be an essential component of a new state-of-the-art algorithm for various instance classes.

This paper describes an improved global harmony search (IGHS) algorithm for identifying the nonlinear discrete-time systems based on second-order Volterra model. The IGHS is an improved version of the novel global harmony search (NGHS) algorithm, and it makes two significant improvements on the NGHS. First, the genetic mutation operation is modified by combining normal distribution and Cauchy distribution, which enables the IGHS to fully explore and exploit the solution space. Second, an opposition-based learning (OBL) is introduced and modified to improve the quality of harmony vectors. The IGHS algorithm is implemented on two numerical examples, and they are nonlinear discrete-time rational system and the real heat exchanger, respectively. The results of the IGHS are compared with those of the other three methods, and it has been verified to be more effective than the other three methods on solving the above two problems with different input signals and system memory sizes.