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Recently many researchers invented a wide variety of meta-heuristic optimization algorithms and they have achieved remarkable performance results. Through observing natural phenomena, clues were inspired and programmed into search logics, such as PSO, Cuckoo Search and so on. Although those algorithms have promising performance, there still exist a drawback — it is hard to find a perfect balance between...
Elephant Search Algorithm (ESA) is one of the contemporary meta-heuristic search algorithms recently proposed. The male elephants are responsible for global exploration, roaming to new dimensions of search space. The female elephants focus on doing local search, for finding the optimal solution. The lifespan mechanism design makes the whole group born and die dynamically thus helps inherit good information...
Data clustering is one of the most popular branches in machine learning and data analysis. Partitioning-based type of clustering algorithms, such as K-means, is prone to the problem of producing a set of clusters that is far from perfect due to its probabilistic nature. The clustering process starts with some random partitions at the beginning, and it tries to improve the partitions progressively...
Although K-means clustering algorithm is simple and popular, it has a fundamental drawback of falling into local optima that depend on the randomly generated initial centroid values. Optimization algorithms are well known for their ability to guide iterative computation in searching for global optima. They also speed up the clustering process by achieving early convergence. Contemporary optimization...
In computer science, a computational challenge exists in finding a globally optimized solution from a tremendously large search space. Heuristic optimization methods have therefore been created that can search the very large spaces of candidate solutions. These methods have been extensively studied in the past, and progressively extended in order to suit a wide range of optimization problems. Researchers...
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