Implementation and Comparison of Four Algorithms on Transportation Problem

The transportation problem is a very applicable and relevant logistic problem. In this paper, to test meta-heuristics on the transportation problem and also improve initial feasible solutions in few number of iterations, four recent and effective meta-heuristic algorithms are used to solve transportation problems. Laying Chicken Algorithm (LCA), Volcano Eruption Algorithm (VEA), COVID-19 Optimizer Algorithm (CVA), and Multiverse Algorithm (MVA) are implemented to solve different sizes of the transportation problem. Computational results show that CVA is the most efficient optimizer for large size cases and LCA is the best algorithm for the others. Finally, convergence of algorithms will be discussed and rate of convergence will be compared. The advantage of these heuristics are that they can be easily adapted to more challenging versions of the transportation problem which are not solveable by the Simplex method. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

An Improved K-means Clustering Algorithm Towards an Efficient Data-Driven Modeling

K-means algorithm is one of the well-known unsupervised machine learning algorithms. The algorithm typically finds out distinct non-overlapping clusters in which each point is assigned to a group. The minimum squared distance technique distributes each point to the nearest clusters or subgroups. One of the K-means algorithm’s main concerns is to find out the initial optimal centroids of clusters. It is the most challenging task to determine the optimum position of the initial clusters’ centroids at the very first iteration. This paper proposes an approach to find the optimal initial centroids efficiently to reduce the number of iterations and execution time. To analyze the effectiveness of our proposed method, we have utilized different real-world datasets to conduct experiments. We have first analyzed COVID-19 and patient datasets to show our proposed method’s efficiency. A synthetic dataset of 10M instances with 8 dimensions is also used to estimate the performance of the proposed algorithm. Experimental results show that our proposed method outperforms traditional kmeans++ and random centroids initialization methods regarding the computation time and the number of iterations. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.