METHODS: In this paper, the Modified Cuckoo Search Algorithm (MCSA) is proposed to enhance the performance of CSA for unconstrained optimization problems. MCSA is focused on the default selection scheme of CSA (i.e. random selection) which is replaced with tournament selection. So, MCSA will increase the probability of better results and avoid the premature convergence. A set of benchmark functions is used to evaluate the performance of MCSA.
RESULTS: The experimental results showed that the performance of MCSA outperformed standard CSA and the existing literature methods.
CONCLUSION: The MCSA provides the diversity by using the tournament selection scheme because it gives the opportunity to all solutions to participate in the selection process.
AIMS: This paper presents a novel local clustering technique, namely, β-hill climbing, to solve the problem of the text document clustering through modeling the β-hill climbing technique for partitioning the similar documents into the same cluster.
METHODS: The β parameter is the primary innovation in β-hill climbing technique. It has been introduced in order to perform a balance between local and global search. Local search methods are successfully applied to solve the problem of the text document clustering such as; k-medoid and kmean techniques.
RESULTS: Experiments were conducted on eight benchmark standard text datasets with different characteristics taken from the Laboratory of Computational Intelligence (LABIC). The results proved that the proposed β-hill climbing achieved better results in comparison with the original hill climbing technique in solving the text clustering problem.
CONCLUSION: The performance of the text clustering is useful by adding the β operator to the hill climbing.