Cuckoo Search (CS) is an optimization algorithm developed by Yang and Deb in 2009. This article describes an overview of CS, which is inspired by the life of a bird family, as well as an overview of CS applications in various categories for solving optimization problems. Optimization is a process of determining the best solution to make something as functional and effective as possible by minimizing or maximizing the parameters involved in the problems. The categories reviewed are Engineering, Pattern Recognition, Job Scheduling, Networking, Object-Oriented Software (Software Testing), and Data Fusion in Wireless Sensor Networks. From the reviewed literature, CS is mostly applied in the engineering area for solving optimization problems. The objective of this study is to provide an overview and to summarize the review of applications of the CS

COVID-19 epidemic in Malaysia started as a small wave of 22 cases in January 2020 through imported cases. It was followed by a bigger wave mainly from local transmissions resulting in 651 cases. The following wave saw unexpectedly three digit number of daily cases following a mass gathering urged the government to choose a more stringent measure. A limited lock-down approach called Movement Control Order (MCO) was immediately initiated to the whole country as a way to suppress the epidemic trajectory. The lock-down causes a major socio-economic disruption thus the ability to forecast the infection dynamic is urgently required to assist the government on timely decisions. Limited testing capacity and limited epidemiological data complicate the understanding of the future infection dynamic of the COVID-19 epidemic. Three different epidemic forecasting models was used to generate forecasts of COVID-19 cases in Malaysia using daily reported cumulative case data up until 1st April 2020 from the Malaysia Ministry of Health. The forecasts were generated using a Curve Fitting Model with Probability Density Function and Skewness Effect, the SIR Model, and a System Dynamic Model. Method one based on curve fitting with probability density function estimated that the peak will be on 19th April 2020 with an estimation of 5,637 infected persons. Method two based on SIR Model estimated that the peak will be on 20th - 31st May 2020 if Movement Contro (MCO) is in place with an estimation of 630,000 to 800,000 infected persons. Method three based on System Dynamic Model estimated that the peak will be on 17th May 2020 with an estimation of 22,421 infected persons. Forecasts from each of model suggested the epidemic may peak between middle of April to end of May 2020.

Cuckoo Search (CS) is an optimization algorithm developed by Yang and Deb in 2009. This paper describes an overview of CS which is inspired by the life of a bird family, called Cuckoo as well as overview of CS applications in various categories for solving optimization problems. Special lifestyle of Cuckoo and their characteristics in egg laying and breeding has been the basic motivation for this optimization algorithm. The categories that reviewed are Engineering, Pattern Recognition, Software Testing & Data Generation, Networking, Job Scheduling and Data Fusion and Wireless Sensor Networks. From the reviewed CS mostly applied in engineering area for solving optimization problems. The objective of this paper is to provide overview and summarize the review of application of the CS.

Clustering is one of the most commonly used approaches in data mining and data analysis. One clustering technique in clustering that gains big attention in clustering related research isk-means clustering such that the observation is grouped intokcluster. However, some obstacles such as the adherence of results to the initial cluster centers or the risk of getting trapped into local optimality hinder the overall clustering performance. The purpose of this research is to minimize the dissimilarity of all points of a cluster from gravity center of the cluster with respect to capacity constraints in each cluster, such that each element is allocated to only one cluster. This paper proposes an effective combination algorithm to find optimal cluster center for the analysis of data in data mining and a new combination algorithm is proposed to untangle the clustering problem. This paper presents a new hybrid algorithm, which is, based on cluster center initialization algorithm (CCIA), bees algorithm (BA), and differential evolution (DE), known as CCIA-BADE-K, aiming at finding the best cluster center. The proposed algorithm performance is evaluated with standard data set. The evaluation results of the proposed algorithm and its comparison with other alternative algorithms in the literature confirm its superior performance and higher efficiency.