Nor Erne Nazira Bazin

Cuckoo Search Algorithm for Optimization Problems: A Literature Review and its Applications

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.

A process prediction model based on Cuckoo algorithm for abrasive waterjet machining

Cuckoo is a new evolutionary algorithm which is inspired by lifestyle of bird family. This study proposes Cuckoo algorithm for prediction of the surface roughness of Abrasive Water Jet (AWJ). Several prediction models with different initial eggs were developed and analyzed to investigate the best predicted surface roughness value. The paired sample t-test was used to demonstrate the validity of the results. Throughout this study, it was evidence that the Cuckoo algorithm could improve machining performances of the AWJ. The result shows that the more initial eggs were considered, a much lower predicted value of surface roughness was obtained. When the initial eggs increase, the value of the best parameters become nearer to the goal point. It was found that Cuckoo algorithm is capable for giving an improved surface roughness as it outperformed the results of two established computational techniques, artificial neural network and support vector machine.

Cuckoo Search Algorithm for Optimization Problems - A Literature Review

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.

An Effective Hybrid of Bees Algorithm and Differential Evolution Algorithm in Data Clustering

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 is -means clustering such that the observation is grouped into cluster. 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.

Exploratory study on the effect of discount pricing strategies for new product introduction

Rapid introduction of new product into the market has resulted in growing competition between retailers. Nowadays, retailers compete with one another in order to increase revenue and to maintain their position in the marketplace. This situation has forced the retailers to enhance their strategic management as well as creating competitive advantages. Generally, this situation can be observed in highly demanded product such as fashion goods and high technology electronic devices (smart phone, notebook). The consequence from the intense competition and new product introduction is difficulties in retailers pricing management. Retailers are now facing with complexity in making decisions on suitable pricing strategies and discount level for new product in association with the product life cycle. Thus, this research aims to investigate the suitable discount pricing strategies that can be integrated in every phase of product life cycle. This paper presents relationships between the discount pricing and the stages in the product life cycle in the form of conceptual diagram and mathematical expression. A system dynamic approach is used for developing the conceptual diagram and formulating the mathematical expression for the discount pricing strategies to visualize the relationship between discount pricing and product life cycle.

Integration of system dynamics with big data using python: An overview

Big Data has rapidly boomed into a hot topic that attracts attention from governments, academia, and industry around the world. Furthermore, the rapid development of Internet, Internet of Things, and Machine To Machine Communication has led to an extensive growth of data whether in industry or business area. This paper provided discussion about the definition, the characteristics of Big Data and important of Big Data to be integrated with System Dynamics model. Next, we present a basic step on how to simulate SD model into Python environment. Finally, we concluded the paper by presenting several suggestions for future references.

Conceptualization of factors influencing new product introduction within shorter product life cycle

Rapid innovation of products by manufacturers reduces the life cycle of the current product due to frequent introduction of new version of product to the market. Manufacturers dealing with these innovative products face problems to forecast the demand from customer, thus increase the cost of carrying the inventory of such products. Generally, this drawback is observed in highly demanded product, such as fashion goods and electronic devices (notebook, mobile phone). It is becoming more important for the manufacturer to adopt tools to manage their strategy in determining the suitable entry point for the new product. This paper presents a system dynamic approach for the conceptualization modeling of the factors influencing the introduction of new product within the shorter product life cycle. The conceptual diagram visualizes the relationships across the factors that contribute to the new product introduction is illustrated in causal loop diagram.