text: The sine-cosine algorithm (SCA) is a new population-based meta-heuristic algorithm. In addition to exploiting sine and cosine functions to perform local and global searches (hence the name sine-cosine), the SCA introduces several random and adaptive parameters to facilitate the search process. Although it shows promising results, the search process of the SCA is vulnerable to local minima maxima due to the adoption of a fixed switch probability and the bounded magnitude of the sine and cosine functions (from -1 to 1). In this paper, we propose a new hybrid Q-learning sine-cosine- based strategy, called the Q-learning sine-cosine algorithm (QLSCA). Within the QLSCA, we eliminate the switching probability. Instead, we rely on the Q-learning algorithm (based on the penalty and reward mechanism) to dynamically identify the best operation during runtime. Additionally, we integrate two new operations (Le´vy flight motion and crossover) into the QLSCA to facilitate jumping out of local minima maxima and enhance the solution diversity. To assess its performance, we adopt the QLSCA for the combinatorial test suite minimization problem. Experimental results reveal that the QLSCA is statistically superior with regard to test suite size reduction compared to recent state-of-the-art strategies, including the original SCA, the particle swarm test generator (PSTG), adaptive particle swarm optimization (APSO) and the cuckoo search strategy (CS) at the 95 confidence level. However, concerning the comparison with discrete particle swarm optimization (DPSO), there is no significant difference in performance at the 95 confidence level. On a positive note, the QLSCA statistically outperforms the DPSO in certain configurations at the 90 confidence level.
  @cite_35: The need for trading off interpretability and accuracy is intrinsic to the use of fuzzy systems. The obtaining of accurate but also human-comprehensible fuzzy systems played a key role in Zadeh and Mamdani's seminal ideas and system identification methodologies. Nevertheless, before the advent of soft computing, accuracy progressively became the main concern of fuzzy model builders, making the resulting fuzzy systems get closer to black-box models such as neural networks. Fortunately, the fuzzy modeling scientific community has come back to its origins by considering design techniques dealing with the interpretability-accuracy tradeoff. In particular, the use of genetic fuzzy systems has been widely extended thanks to their inherent flexibility and their capability to jointly consider different optimization criteria. The current contribution constitutes a review on the most representative genetic fuzzy systems relying on Mamdani-type fuzzy rule-based systems to obtain interpretable linguistic fuzzy models with a good accuracy.
  @cite_4: The optimization of nonlinear functions using particle swarm methodology is described. Implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm. Benchmark testing of both paradigms is described, and applications, including neural network training and robot task learning, are proposed. Relationships between particle swarm optimization and both artificial life and evolutionary computation are reviewed.
  @cite_3: This paper highlights a novel strategy for generating variable-strength (VS) interaction test suites, called VS Particle Swarm Test Generator (VS-PSTG). As the name suggests, VS-PSTG adopts Particle Swarm Optimization to ensure optimal test size reduction. To determine its efficiency in terms of the size of the generated test suite, VS-PSTG was subjected to well-known benchmark configurations. Comparative results indicate that VS-PSTG gives competitive results as compared to existing strategies. An empirical case study was conducted on a non-trivial software system to show the applicability of the strategy and to determine the effectiveness of the generated test suites to detect faults.
  @cite_5: Software behavior depends on many factors. Combinatorial testing (CT) aims to generate small sets of test cases to uncover defects caused by those factors and their interactions. Covering array generation, a discrete optimization problem, is the most popular research area in the field of CT. Particle swarm optimization (PSO), an evolutionary search-based heuristic technique, has succeeded in generating covering arrays that are competitive in size. However, current PSO methods for covering array generation simply round the particle’s position to an integer to handle the discrete search space. Moreover, no guidelines are available to effectively set PSOs parameters for this problem. In this paper, we extend the set-based PSO, an existing discrete PSO (DPSO) method, to covering array generation. Two auxiliary strategies (particle reinitialization and additional evaluation of gbest ) are proposed to improve performance, and thus a novel DPSO for covering array generation is developed. Guidelines for parameter settings both for conventional PSO (CPSO) and for DPSO are developed systematically here. Discrete extensions of four existing PSO variants are developed, in order to further investigate the effectiveness of DPSO for covering array generation. Experiments show that CPSO can produce better results using the guidelines for parameter settings, and that DPSO can generate smaller covering arrays than CPSO and other existing evolutionary algorithms. DPSO is a promising improvement on PSO for covering array generation.
  @cite_31: A fuzzy system for environmental risk assessment of genetically modified plants is described.The fuzzy system is based on Mamdani inference.The Fuzzy System Risk Assessments have been validated on real world trial case studies.The system decisions have been considered coherent and consistent by human experts. Environmental risk assessment (ERA) of the deliberate release of genetically modified plants (GMPs) is currently performed by human experts on the basis of own personal experience and knowledge. In this paper we describe a fuzzy decision system (FDS) for the ERA of GMPs, based on Mamdani fuzzy inference. The risk assessment in the FDS is obtained by using a fuzzy inference system (FIS), performed using jFuzzyLogic library. The FDS permits obtaining an evaluation process for the identification of potential impacts that can achieve one or more receptors through a set of migration paths. The decisions derived by FDS have been validated on real world cases by the human experts that are in charge of ERA. They have confirmed the reliability and correctness of the fuzzy system decisions.
  @cite_16: Recently, researchers have started to explore the use of Artificial Intelligence (AI)-based algorithms as t-way (where t indicates the interaction strength) testing strategies. Many AI-based strategies have been developed, such as Ant Colony, Simulated Annealing, Genetic Algorithm, and Tabu Search. Although useful, most existing AI-based t-way testing strategies adopt complex search processes and require heavy computations. For this reason, existing AI-based t-way testing strategies have been confined to small interaction strengths (i.e., t@?3) and small test configurations. Recent studies demonstrate the need to go up to t=6 in order to capture most faults. In this paper, we demonstrate the effectiveness of our proposed Particle Swarm-based t-way Test Generator (PSTG) for generating uniform and variable strength covering arrays. Unlike other existing AI-based t-way testing strategies, the lightweight computation of the particle swarm search process enables PSTG to support high interaction strengths of up to t=6. The performance of our proposed PSTG is evaluated using several sets of benchmark experiments. Comparatively, PSTG consistently outperforms its AI counterparts and other existing testing strategies as far as the size of the array is concerned. Furthermore, our case study demonstrates the usefulness of PSTG for facilitating fault detection owing to interactions of the input components.
  @cite_10: New approach is used to generate combinatorial test suitesFuzzy-based strategy developed to generate the test cases using adaptive techniqueIt is a new approach in developing Artificial Intelligent and Expert systemsThe strategy proves its efficiency, performance compared to its counterpartsThe strategy proves its effectiveness also through the case study Recent research activities have demonstrated the effective application of combinatorial optimization in different areas, especially in software testing. Covering array (CA) has been introduced as a representation of the combinations in one complete set. CAλ(N; t, k, v) is an N?×?k array in which each t-tuple for an N × t sub array occurs at least λ times, where t is the combination strength, k is the number of components (factors), and v is the number of symbols for each component (levels). Generating an optimized covering array for a specific number of k and v is difficult because the problem is a non-deterministic polynomial-time hard computational one. To address this issue, many relevant strategies have been developed, including stochastic population-based algorithms. This paper presents a new and effective approach for constructing efficient covering arrays through fuzzy-based, adaptive particle swarm optimization (PSO). With this approach, efficient covering arrays have been constructed and the performance of PSO has been improved for this type of application. To measure the effectiveness of the technique, an empirical study is conducted on a software system. The technique proves its effectiveness through the conducted case study.
summary: Particle swarm optimization (PSO) @cite_4 is perhaps the most popular implementation of @math test suite generation. The PSO-based @math strategy searches by mimicking the swarm behaviour of flocking birds. In PSO, global and local searches are guided by the inertia weight and social cognitive parameters. Initially, a random swarm is created. Then, the PSO algorithm iteratively selects a candidate solution within the swarm to be added to the final test suite until all interaction tuples are covered (based on velocity and displacement transformation). developed early PSO-based strategies called the PSTG @cite_16 @cite_3 and APSO @cite_10 . APSO is an improvement on the PSTG integrated with adaptive tuning based on the Mamdani fuzzy inference system @cite_31 @cite_35 . implemented discrete PSO @cite_16 by substantially modifying the displacement and velocity transformation used in PSO. The benchmark results of DPSO @cite_5 demonstrate its superior performance when compared with both the PSTG and APSO.