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 popular modified graph clustering ant colony optimization (ACO) algorithm (MGCACO) performs feature selection (FS) by grouping highly correlated features. However, the MGCACO has problems in local search, thus limiting the search for optimal feature subset. Hence, an enhanced feature clustering with ant colony optimization (ECACO) algorithm is proposed. The improvement constructs an ACO feature clustering method to obtain clusters of highly correlated features. The ACO feature clustering method utilizes the ability of various mechanisms, such as local and global search to provide highly correlated features. The performance of ECACO was evaluated on six benchmark datasets from the University California Irvine (UCI) repository and two deoxyribonucleic acid microarray datasets, and its performance was compared against that of five benchmark metaheuristic algorithms. The classifiers used are random forest, k-nearest neighbors, decision tree, and support vector machine. Experimental results on the UCI dataset show the superior performance of ECACO compared with other algorithms in all classifiers in terms of classification accuracy. Experiments on the microarray datasets, in general, showed that the ECACO algorithm outperforms other algorithms in terms of average classification accuracy. ECACO can be utilized for FS in classification tasks for high-dimensionality datasets in various application domains such as medical diagnosis, biological classification, and health care systems.
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