Amirmudin Udin

Levy Flight Algorithm for Optimization Problems - A Literature Review

This paper presents a literature review on applications of Levy flight. Nowadays, Levy flight laws has been used for a broad class of processes such as in physical, chemical, biological, statistical and also in financial. From the review, Levy flight technique has been applied mostly in physics area where the researchers use Levy flight technique to solve and optimize the problem regarding diffusive, scaling and transmission. This paper also reviews the latest researches using modified Levy flight technique such as truncated, smoothly truncated and gradually truncated Levy Flight for optimization. Finally, future trends of Levy flight are discussed.

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.

Firefly Algorithm for Optimization Problem

This paper reviews the applications of Firefly Algorithm (FA) in various domain of optimization problem. 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. Several categories of optimization problem such as discrete, chaotic, multi-objective and many more are addressed by inspiring the behavior of fireflies as mentioned in the literatures. Literatures found that FA was mostly applied by researchers to solve the optimization problems in Computer Science and Engineering domain. Some of them are enhanced or hybridized with other techniques to discover better performance. In addition, literatures found that most of the cases that used FA technique have outperformed compare to other metaheuristic algorithms.

Glowworm Swarm Optimization (GSO) Algorithm for Optimization Problems: A State-of-the-Art Review

Glowworm Swarm Optimization (GSO) algorithm is a derivative-free, meta-heuristic algorithm and mimicking the glow behavior of glowworms which can efficiently capture all the maximum multimodal function. Nevertheless, there are several weaknesses to locate the global optimum solution for instance low calculation accuracy, simply falling into the local optimum, convergence rate of success and slow speed to converge. This paper reviews the exposition of a new method of swarm intelligence in solving optimization problems using GSO. Recently the GSO algorithm was used simultaneously to find solutions of multimodal function optimization problem in various fields in today industry such as science, engineering, network and robotic. From the paper review, we could conclude that the basic GSO algorithm, GSO with modification or improvement and GSO with hybridization are considered by previous researchers in order to solve the optimization problem. However, based on the literature review, many researchers applied basic GSO algorithm in their research rather than others.

Optimization of Surface Roughness in Turning Operation Using Firefly Algorithm

Recently, Firefly Algorithm (FA) has become an important technique to solve optimization problems. Various FA variants have been developed to suit various applications. In this paper, FA is used to optimize machining parameters such as % Volume fraction of SiC (V), cutting speed (S), feed rate (F), depth of cut (D) and machining time (T). The optimal machining cutting parameters estimated by FA that lead to a minimum surface roughness are validated using ANOVA test.

DSM-IV-defined anxiety disorder symptoms in a middle-childhood-aged group of Malaysian children using the Spence Children's Anxiety Scale

INTRODUCTION Pediatric anxiety disorders are the most common mental health disorders in the middle-childhood age group. The purpose of this study is to assess anxiety disorder symptoms, as defined by the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV), in a large community sample of low socioeconomic level rural children and to investigate some of the psychometric properties (internal consistency, construct and convergent validity and items rated as often or always experienced) of the Malay version of the Spence Childrens Anxiety Scale - Child version (SCAS-C). METHOD Six hundred children aged 9-11 and 424 of their parents completely answered the child or parent versions of the SCAS. RESULTS Results indicated that the internal reliability of subscales were moderate to adequate. Significant correlations between child and parent reports supported the measures concurrent validity. Additionally, anxiety levels in this Malaysian sample were lower than among South-African children and higher than among their Western peers. There were both similarities and differences between symptom items reported as often or always experienced by Malaysian students and by children from other cultures. Confirmatory factor analysis provided evidence of the existence of five inter-correlated factors for anxiety disorders based on SCAS-C. CONCLUSION Although some of the instruments psychometric properties deviated from those observed in some other countries, it nevertheless appears to be useful for assessing childhood anxiety symptoms in this country.

Machining Parameters Optimization using Hybrid Firefly Algorithm and Particle Swarm Optimization

Firefly Algorithm (FA) is a metaheuristic algorithm that is inspired by the flashing behavior of fireflies and the phenomenon of bioluminescent communication and the algorithm is used to optimize the machining parameters (feed rate, depth of cut, and spindle speed) in this research. The algorithm is hybridized with Particle Swarm Optimization (PSO) to discover better solution in exploring the search space. Objective function of previous research is used to optimize the machining parameters in turning operation. The optimal machining cutting parameters estimated by FA that lead to a minimum surface roughness are validated using ANOVA test.