Existing clustering techniques have many drawbacks and this includes being trapped in a local optima. In this paper, we introduce the utilization of a new meta-heuristics algorithm, namely the Firefly algorithm (FA) to increase solution diversity. FA is a nature-inspired algorithm that is used in many optimization problems. The FA is realized in document clustering by executing it on Reuters-21578 database. The algorithm identifies documents that has the highest light intensity in a search space and represents it as a centroid. This is followed by recognizing similar documents using the cosine similarity function. Documents that are similar to the centroid are located into one cluster and dissimilar in the other. Experiments performed on the chosen dataset produce high values of Purity and F-measure. Hence, suggesting that the proposed Firefly algorithm is a possible approach in document clustering.
The aim of robot path planning is to search for a safe path for the mobile robot. Even though there exist various path planning algorithms for mobile robots, yet only a few are optimized. The optimized algorithms include the Particle Swarm Optimization (PSO) that finds the optimal path with respect to avoiding the obstacles while ensuring safety. In PSO, the sub-optimal solution takes place frequently while finding a solution to the optimal path problem. This paper proposes an enhanced PSO algorithm that contains an improved particle velocity. Experimental results show that the proposed Enhanced PSO performs better than the standard PSO in terms of solution’s quality. Hence, a mobile robot implementing the proposed algorithm operates better and is more secure.
Least Squares Support Vector Machine (LSSVM) has been known to be one of the effective forecasting models. However, its operation relies on two important parameters (regularization and kernel). Predetermining the values of parameters will affect the results of the forecasting model; hence, to find the optimal value of these parameters, this study investigates the adaptation of Bat and Cuckoo Search algorithms to optimize LSSVM parameters. Even though Cuckoo Search has been proven to be able to solve global optimization in various areas, the algorithm leads to a slow convergence rate when the step size is large. Hence, to enhance the search ability of Cuckoo Search, it is integrated with Bat algorithm that offers a balanced search between global and local. Evaluation was performed separately to further analyze the strength of Bat and Cuckoo Search to optimize LSSVM parameters. Five evaluation metrics were utilized; mean average percent error (MAPE), accuracy, symmetric mean absolute percent error (SMAPE), root mean square percent error (RMSPE) and fitness value. Experimental results on diabetes forecasting demonstrated that the proposed
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