Hybrid artificial fish and glowworm swarm optimization algorithm for electrical discharge machining of titanium alloy

Electrical discharge machining (EDM) is a non-traditional machining process widely used to machine geometrically complex and hard materials. In EDM, selection of optimal EDM parameters is important to have high quality products and increase productivity. However, one of the major issues is to obtain better machining performance at optimal value of these machining parameters. Modelling and optimization of EDM parameters have been considered to identify optimal EDM parameters that would lead to better EDM performance. Due to the complexity and uncertainty of the machining process, computational approaches have been implemented to solve the EDM problem. Thus, this study conducted a comprehensive investigation concerning the influence of EDM parameters on material removal rate (MRR), surface roughness (Ra) and dimensional accuracy (DA) through an experimental design. The experiment was performed based on full factorial design of experiment (DOE) with added center points of pulse on time (Ton), pulse off time (Toff), peak current (Ip) and servo voltage (Sv). In the EDM optimization, glowworm swarm optimization (GSO) algorithm was implemented. However, previous works indicated that GSO algorithm has always been trapped in the local optima solution and is slow in convergence. Therefore, this study developed a new hybrid artificial fish and glowworm swarm optimization (AF-GSO) algorithm to overcome the weaknesses of GSO algorithm in order to have a better EDM performance. For the modeling process, four types of regression models, namely multiple linear regression (MLR), two factor interaction (2FI), multiple polynomial regression (MPR) and stepwise regression (SR) were developed. These regression models were implemented in the optimization process as an objective function equation. Analysis of the optimization proved that AF-GSO algorithm has successfully outperformed the standard GSO algorithm. 2FI model of AF-GSO optimization for MRR and DA gave optimal solutions of 0.0042g/min and 0.00129%, respectively. On the other hand, the SR model for Ra of AF-GSO optimization gave the optimal solution of 1.8216p,s. Overall, it can be concluded that AF-GSO algorithm has successfully improved the quality and productivity of the EDM problems

Test Case Prioritization Using Swarm Intelligence Algorithm to Improve Fault Detection and Time for Web Application

Prioritizing test cases based on several parameters where important ones are executed first is known as test case prioritization (TCP). Code coverage, functionality, and features are all possible factors of TCP for detecting bugs in software as early as possible. This research was carried out to test and compare the effectiveness Swarm Intelligence algorithms, where Artificial Bee Colony (ABC) and Ant Colony Optimization (ACO) algorithms were implemented to find the fault detected and execution time as these are the curial aspects in software testing to ensure good quality products are produced within the timeline. As web applications are commonly used by a board population, this research was carried out on an Online Shopping application represented as Case Study One and Education Administrative application known as Case Study Two. In recent years, TCP has been implemented widely, but none has implemented on web application which was conducted to fill the gaps and produce a new contribution in this area. The outcome was compared using Average Percentage Fault Detected (APFD) and execution time. For Case Study One, the APFD value was 0.80 and 0.71 while the execution time was 8.64 seconds and 0.69 seconds respectively for ABC and ACO. For Case Study Two, the APFD values were 0.81 and 0.64 while the execution time was 8.83 seconds and 1.22 seconds for ABC and ACO. It was seen that both algorithms performed well in their respective ways. ABC had shown to give a higher value for APFD while ACO had converged faster for execution time

Comparative Study of Search Engine Optimization Algorithms On Retrieval Time and Precise Data Entry for Websites

A search engine is a complex software that a finder visits to numerous websites and their pages to find important data. It is a main source to find content on the World Wide Web. Search Engine Optimization (SEO) methods have been invented to make user searching smoother. Although search engines are smart, sometimes they also provide irrelevant data. As a result, researchers have found a solution to this problem by implementing the SEO techniques. SEO techniques are used to make websites more visible as well as to produce organic search results. Thus, this study proposed an implementation of Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) for SEO problems on online shopping websites and educational websites. These algorithms are evaluated based on retrieval time and precise data entry by using precision and recall. Moreover, PageSpeed Insights is used to check the speed index of the websites. The outcome of the research found that PSO outperformed ANN for both shopping website and educational website. PSO have the minimal retrieval time that is 0.04 seconds for online shopping website and 0.10 seconds for educational website. As for precision and recall, online shopping websites have been proven to have the highest precision score of 0.67 and 0.63 recall score. The simulation analysis results show that in future researchers should concentrate more on determining the significance of each SEO approach and determining the best blend for various sectors

Comparative Analysis of Test Case Prioritization Using Ant Colony Optimization Algorithm and Genetic Algorithm

After it is published, every software system will get an upgrade, requiring it to adapt to meet the ever-changing client needs thus regression testing becomes one of the most important operations in any software system. As it is too expensive to repeat the execution of all the test cases available from a previous version of the software system, numerous ways to optimize the regression test suite have evolved, one of which is test case prioritizing. This study was carried out to test and compare the effectiveness of evolutionary algorithms and swarm intelligence algorithms, represented by the Genetic Algorithm (GA) and Ant Colony Optimization (ACO) algorithms. They will be implemented to find the Average Percentage Fault Detected (APFD), execution time, and Big O notation, as these are critical aspects in software testing to ensure high-quality products are produced on time. This study employs data from two separate investigations on comparable issues, denoted as Case Study One and Case Study Two. TCP has been extensively used in recent years, but not much research has been conducted to analyze and evaluate the performance of genetic algorithms (GA) and ant colony optimization (ACO) in a test case prioritization context. The algorithms were compared using APFD, execution time. The APFD and execution time values of 50, 100, 150, and 200 iterations for ACO and GA for both datasets are conducted. Both algorithms were determined to work on O() notation, which indicates they should scale up their execution process similarly on different input scales. Both algorithms performed well in their respective roles. ACO has shown to be more valuable than GA in terms of APFD and GA has shown to be more valuable than ACO in terms of execution time

The Effects of Photovoltaic Panel’s Temperature Towards Photovoltaic Electrical Power Output

Photovoltaic (PV) system is recognized as one of the most current renewable energy types in producing electrical power. However, one of the problems of PV systems is that the performance of PV panel output is dependent on the climate condition. Hence, it is important to predict the actual generating output power of PV systems. This study investigates the relationship between the temperature of the PV panel with the PV power output. The PV systems installed at the rooftop of Mega label SDN. BHD. with type of poly-crystalline 405.72KWP has been chosen as the reference system in this study. The results have shown that the rise of PV panel's temperature will make the value of the PV electrical power output decreases

Hybrid taguchi glowworm optimization algorithm for optimization of cutting parameters

Quality of machining products is generally associated with the surface roughness (Ra) and is one of the important aspects that could affect machining performance. In traditional and modern machining operations, optimization of reasonable cutting parameters is a requirement for providing better quality products. This research employs and enhanced the Glowworm Swarm Optimization (GSO) algorithm to optimize cutting parameters to obtain minimum Ra values. GSO is a new method of swarm intelligent based algorithm to search for global extremes of multi-modal optimization problems. The algorithm is employed in this study to approximate optimum cutting parameters to obtain improved values of Ra in end milling and abrasive water jet (AWJ) processes. The cutting parameters considered for end milling are cutting speed (v), feed rate (f) and depth of cut (d) whereas traverse speed (V), water jet pressure (P), standoff distance (h), abrasive grit size (D) and abrasive flow rate (m) are considered for AWJ. Following that, to improve further the Ra values, this study proposed hybridization of GSO and Taguchi method known as HTGSO. HTGSO simulation results were compared to experimental and GSO results. In AWJ machining process, HTGSO reduced the Ra value by 13% and 25% compared to those obtain from both experimental and GSO. Whilst, HTGSO is found has outperformed both experimental and GSO in end milling by decreasing the Ra value up to 25% and 40% respectively. Therefore, HTGSO produced the best Ra with the lowest values. This performance indicates HTGSO significantly improved the Ra during the machining process that lead to higher quality of machining produc

Overview of artificial fish swarm algorithm and its applications in industrial problems

Artificial fish swarm algorithm (AFSA) is a class of swarm intelligent optimization algorithm stimulated by the various social behaviors of fish in search of food. AFSA can search for global optimum through local optimum value search of each individual fish effectively based on simulating of fish-swarm behaviors such as searching, swarming, following and bulletin. This paper presents an overview of AFSA algorithm by describing the evolution of the algorithm along with all the improvements and its combinations with various algorithms and methods as well as its applications in solving industrial problems

A study of electrode wear ratio on EDM of Ti-6AL-4V with copper-tungsten electrode

In this work, a study of electrode wear ratio (EWR) on the diesinking electrical discharge machining (EDM) of Ti-6AL-4V titanium alloy with copper-tungsten (Cu-W) electrode has been carried out. Pulse on time (ON), pulse of time (OFF), peak current (V) and servo voltage (SV) were seen as the machining parameters. The experiments were run according to the design of experiments (DOE), which is two levels of full factorial with added centre points. The experimental results reveal that pulse on time and peak current are statistically significant parameters for affecting EWR with the p-value of 0.0013 and 0.0012 respectively. Moreover, based on ANOVA, we recognized peak current as the most significant parameters which contribute 31.75%, followed by pulse on time, servo voltage and pulse on time which contribute 30.99%, 8.68% and 0.72%, respectively

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

A study of dimensional accuracy on die sinking electrical discharge machining of Ti-6AL-4V

Objectives: In this work, an investigation of dimensional accuracy (DA) on the die sinking electrical discharge machining (EDM) of Ti-6AL-4V titanium alloy with positive polarity of copper-tungsten (Cu-W) electrode has been carried out. Methods/Analysis: Pulse on time (ON), pulse off time OFF), peak current (IP) and servo voltage (SV) were selected as the cutting parameters. The experiments have been conducted upon the two levels of full factorial design with added four center points Design of Experiments (DOE). A mathematical model is developed to associate the effects of these four cutting parameters with DA of the work piece. Multiple linear regression modelling technique was performed in the mathematical modelling process. Furthermore, Analysis of Variance (ANOVA) technique was performed to determine the significance of the cutting parameters. Findings: Based on the results, ON, IP and SV were found to be the significant cutting parameters contributes to the DA. This finding also indicates the model developed is observed to be in good simultaneous with the experimental results