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The primary goal of traveling salesman problem (TSP) is for a salesman to visit many cities and return to the starting city via a sequence of potential shortest paths. Subsequently, conventional algorithms are inadequate for large-scale problems; thus, metaheuristic algorithms have been proposed. A recent metaheuristic algorithm that has been implemented to solve TSP is the plant propagation algorithm (PPA), which belongs to the rose family. In this research, this existing PPA is modified to solve TSP. Although PPA is claimed to be successful, it suffers from the slow convergence problem, which significantly impedes its applicability for getting good solution. Therefore, the proposed partial-partitioned greedy algorithm (PPGA) offers crossover and three mutation operations (flip, swap, and slide), which allow local and global search and seem to be wise methods to help PPA in solving the TSP. The PPGA performance is evaluated on 10 separate datasets available in the literature and compared with the original PPA. In terms of distance, the computational results demonstrate that the PPGA outperforms the original PPA in nine datasets which assures that it is 90% better than PPA. PPGA produces good solutions when compared with other algorithms in the literature, where the average execution time reduces by 10.73%.

The advanced of Information Technology has resulting in the generation of numerous datasets with different dimensions. However, dealing with multi-dimensional datasets which typically contain large number of attributes, p has cause problems to classification process. Classifying different dimensional numerical data is a difficult problem as dealing with various feature spaces, could cause the performance of supervised learning method to suffer from the curse of dimensionality.  This condition eventually degrades both classification accuracy and efficiency. In a nutshell, not all attributes in the dataset can be used in the classification process since some features may lead to low performance of classifier. Feature selection (FS) is a good mechanism that minimises the dimensions of high-dimensional datasets and solve classification problems. This paper proposed Bat Algorithm (BA) for FS that were trained using a Support Vector Machine (SVM) classifier. The proposed algorithm was tested on six public datasets with different sizes and compared with other benchmark algorithms, such as Particle Swarm Optimisation (PSO) and Genetic Algorithm (GA). The experimental results indicated that the BA has outperformed the other two algorithms. In addition, the comparison details showed that binary BA is more competitive in terms of accuracy and the number of features when assessed on datasets with different sizes.