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A binary version of the hybrid grey wolf optimization (GWO) and particle swarm optimization (PSO) is proposed to solve feature selection problems in this paper. The original PSOGWO is a new hybrid optimization algorithm that benefits from the strengths of both GWO and PSO. Despite the superior performance, the original hybrid approach is appropriate for problems with a continuous search space. Feature selection, however, is a binary problem. Therefore, a binary version of hybrid PSOGWO called BGWOPSO is proposed to find the best feature subset. To find the best solutions, the wrapper-based method K-nearest neighbors classifier with Euclidean separation matric is utilized. For performance evaluation of the proposed binary algorithm, 18 standard benchmark datasets from UCI repository are employed. The results show that BGWOPSO significantly outperformed the binary GWO (BGWO), the binary PSO, the binary genetic algorithm, and the whale optimization algorithm with simulated annealing when using several performance measures including accuracy, selecting the best optimal features, and the computational time.

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Feature selection or dimensionally reduction can be considered as a multi-objective minimization problem with two objectives: minimizing the number of features and minimizing the error rate simultaneously. Despite being a multiobjective problem, most existing approaches treat feature selection as a single-objective optimization problem. Recently, Multiobjective Grey Wolf optimizer (MOGWO) was proposed to solve multi-objective optimization problem. However, MOGWO was originally designed for continuous optimization problems and hence, it cannot be utilized directly to solve multi-objective feature selection problems which are inherently discrete in nature. Therefore, in this research, a binary version of MOGWO based on sigmoid transfer function called BMOGW-S is developed to optimize feature selection problems. A wrapper based Artificial Neural Network (ANN) is used to assess the classification performance of a subset of selected features. To validate the performance of the proposed method, 15 standard benchmark datasets from the UCI repository are employed. The proposed BMOGWO-S was compared with MOGWO with a tanh transfer function and Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-objective Particle Swarm Optimization (MOPSO). The results showed that the proposed BMOGWO-S can effectively determine a set of non-dominated solutions. The proposed method outperforms the existing multi-objective approaches in most cases in terms of features reduction as well as classification error rate while benefiting from a lower computational cost.

Feature selection has gained much consideration from scholars working in the domain of machine learning and data mining in recent years. Feature selection is a popular problem in Machine learning with the goal of finding optimal features with increase accuracy. As a result, several studies have been conducted on multi-objective feature selection through numerous multi-objective techniques and algorithms. The objective of this paper is to present a systematic literature review of the challenges and issues of the multi-objective feature selection problem and critically analyses the proposed techniques used to tackle this problem. The conducted review covered all related studies published since 2012 up to 2019. The outcomes of the reviewed of these studies clearly showed that no perfect solution to the multi-objective feature selection problem yet. The authors believed that the conducted review would serve as the main source of the techniques and methods used to resolve the problem of multi-objective feature selection. Furthermore, current challenges and issues are deliberated to find promising research domains for further study.

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To achieve acceptable quality and performance of any software product, it is crucial to assess various software components in the application. There exist various softwaretesting techniques such as combinatorial testing and covering array. However, problems such as t-way combinatorial explosion is still challenging in any combinatorial testing strategy, as it takes into consideration the entire combinations of input variables. Therefore, to overcome this problem, several optimizations and metaheuristic strategies have been suggested. One of the most effective optimization algorithms based techniques is the Artificial Bee Colony (ABC) algorithm. This paper presents t-way generation strategy for both a uniform and variable strength test suite by applying the ABC strategy (ABCVS) to reduce the size of the test suite and to subsequently enhance the test suite generation interaction. To assess both the effectiveness and performance of the presented ABCVS, several experiments were conducted applying various sets of benchmarks. The results revealed that the proposed ABCVS outweigh the existing based strategies and demonstrated wider interaction between components as opposed to AI-search based and computational based strategies. The results also revealed higher prospect of ABCVS in the aspect of its effectiveness and performance as observed in the majority of case studies. 260 | P a g e www.ijacsa.thesai.org acceptable strategy, known as the Artificial Bee Colony Strategy (ABCVS) continuous to our previous research [40,41] for uniform and variable strength interaction t-way minimal test suite production. It is anticipated that ABCVS will address this problem. In fact, experimental results have revealed that the proposed ABCVS is able to support higher interaction strengths up to t = 6 compared to other Artificial Intelligence (AI)-based strategies. Furthermore, ABCVS it can compete against these other strategies in the majority of the case studies examined in the literature regarding efficiency (test suite generation) and performance (speed) and against other AI-based strategies having higher interaction strengths.The remainder of this paper is structured accordingly. Section II presents the background to this study, (i.e. CA and MCA concepts) which is followed by Section III which surveys "state of the art" testing strategies in this area. Section IV provides an overview of an ABC which is then followed by Section V describing the proposed strategy, consisting of two parts: (1) construction of the covering matrix, and (2) the proposed ABCVS. Section VI illustrates the tuning of the ABCVS parameters. Section VII evaluates the ABCVS by conducting several benchmark experiments in terms of effciency and performance alongside with statistical analysis evaluation by using Wilcoxon signed-rank test in Section VIII. Section IX discusses the advantages, limitations and threats to the validity of the approach, which followed by Section X providing overall conclusions to the study and presenting recommendations for future work. 12

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