Comparison Between Genetic Algorithm and Electromagnetism-Like Algorithm for Solving Inverse Kinematics

A comparison study between Electromagnetism-Like Algorithm (EM) and Genetic Algorithm (GA)has been presented in this work to solve the Inverse Kinematics (IK) of a four-link planar robot manipulator. The comparison is focused on some points for both algorithms like the accuracy of the results and the speed of convergence. Different target points have been taken to check the performance of each algorithm to solve the IK problem. The results showed that EM algorithm needs less population size and number of generations to get the true solution. There are multiple robot configurations at the goal points and both algorithms are able to find these solutions at each point. Self developed software simulator is used to display some of these solutions at each goal position

A Comparative Analysis of Various Chaotic Genetic Algorithms for Multimodal Function Optimization

This study proposes a novel method of introducing chaotic induced genes into Genetic Algorithms (GA) in order to solve unimodal and multimodal mathematical test functions. The integration of chaotic elements based on logistic map into GA has significantly improved the accuracy in the aspect of the best fitness value. Simulation results show that the influence of Chaos theory does improve the optimization accuracy of the mathematical functions used

An Adaptive Immune Algorithm based Gravimetric Fluid Dispensing Machine

A dispensing system is used in a materials-mixing plant to provide accurate blend ratios in producing the desired end-use product. The AIS-based (Artificial Immune Systems) fine tuning of dispensing parameters is proposed by optimizing the components of dispensing time and stopping time delay to obtain constant and accurate reading from the precision balance scale. Based on the new dispensing sequence, experimental tests had been carried out using different materials with varying viscosities. The results indicate that the combination of both PWM and AIS techniques would minimize overshoot while exhibiting lower steady-state error and faster response time. These are important in order to overcome the limitations of the conventional volumetric dispensing and manual parameter tuning presently applied in the dispensing system used in the coatings industry

Error Detection of Personalized English Isolated-Word Using Support Vector Machine

A better understanding on word classification could lead to a better detection and correction technique. In this study, a new features representation technique is used to represent the machine-printed English word. Subsequently, a well-known classification type of artificial intelligent algorithm namely Support Vector Machine (SVM) is used to evaluate those features under two class types of words with proper segregation of correct and erroneous words in two data sets. Our proposed model shows good performance in error detection and is superior when compared with neural networks, Hamming distance or minimum edit distance technique; with further improvement in sight

Artificial Immune System based on Hybrid and External Memory for Mathematical Function Optimization

Artificial immune system (AIS) is one of the natureinspired algorithm for optimization problem. In AIS, clonal selection algorithm (CSA) is able to improve global searching ability. However, the CSA convergence and accuracy can be further improved because the hypermutation in CSA itself cannot always guarantee a better solution. Alternatively, Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been used efficiently in solving complex optimization problems, but they have a tendency to converge prematurely. Thus, a hybrid PSO-AIS and a new external memory CSA based scheme called EMCSA are proposed. In hybrid PSO-AIS, the good features of PSO and AIS are combined in order to reduce any limitation. Alternatively, EMCSA captures all the best antibodies into the memory in order to enhance global searching capability. In this preliminary study, the results show that the performance of hybrid PSO-AIS compares favourably with other algorithms while EMCSA produced moderate results in most of the simulations

Artificial Immune System Based Remainder Method for Multimodal Mathematical Function Optimization

Artificial immune system (AIS) is one of the nature-inspired algorithm for solving optimization problems. In AIS, clonal selection algorithm (CSA) is able to improve global searching ability compare to other meta-heuristic methods. However, the CSA rate of convergence and accuracy can be further improved as the hypermutation in CSA itself cannot always guarantee a better solution. Conversely, Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been used efficiently in solving complex optimization problems, but they have an inclination to converge prematurely. In this work, the CSA is modified using the best solutions for each exposure (iteration) namely Single Best Remainder (SBR) - CSA. Simulation results show that the proposed algorithm is able to enhance the performance of the conventional CSA in terms of accuracy and stability for single objective functions

Antibody Remainder Method Based Artificial Immune System for Mathematical Function Optimization

Artificial immune system (AIS) is one of the natureinspired algorithm for solving optimization problem. In AIS, clonal selection algorithm (CSA) is able to improve global searching ability. However, the CSA convergence and accuracy can be improved further because the hypermutation in CSA itself cannot always guarantee a better solution. Alternatively,Genetic Algorithms (GAs) and Particle Swarm Optimization(PSO) have been used efficiently in solving complex optimization problems, but they have a tendency to converge prematurely. In this study, the CSA is modified using the best solution for each exposure (iteration) namely Single Best Remainder (SBR) CSA. In this study, the results show that the performance of the proposed algorithm (SBR-CSA) compares favourably with other algorithms while Half Best Insertion (HBI) CSA produced moderate results in most of the simulations

Bandwidth Widening Strategies for Piezoelectric Based Energy Harvesting from Ambient Vibration Sources

Due to the fact that the ambient vibration sources are random and unpredictable, therefore a vibration based energy harvesting device is desirable to be able to operate at wider bandwidth in an envelop of frequency range to generate maximum electrical output. In this paper, various ambient vibration from household appliances, machineries, vehicle and moving vehicle were measured and investigated. The second part of the paper will discuss the strategies to harvest these ambient vibration sources. An array of piezoelectric multi-cantilever is proposed to address the issue of single piezoelectric cantilever with high Q-factor. Two configurations of multi-cantilever were fabricated in a form that elevated from the substrate as freestanding structures. One with six cantilevers of constant width but different lengths and another with five cantilevers of constant length but different widths. The measurement and experimental results show a frequency band of 200 Hz to 300 Hz as a common bandwith between the vibration sources and the capability of miniature piezoelectric energy harvester in harvesting maximum electrical energy

Mathematical function optimization using AIS antibody remainder method

Artificial immune system (AIS) is one of the metaheuristics used for solving combinatorial optimization problems. In AIS, clonal selection algorithm (CSA) has good global searching capability. However, the CSA convergence and accuracy can be improved further because the hypermutation in CSA itself cannot always guarantee a better solution. Alternatively, Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been used efficiently in solving complex optimization problems, but they have a tendency to converge prematurely. In this study, the CSA is modified using the best solutions for each exposure (iteration) namely Single Best Remainder (SBR) - CSA. The results show that the proposed algorithm is able to improve the conventional CSA in terms of accuracy and stability for single and multi objective functions

Link Performance Enhancement For Image Transmission With FEC In Wireless Sensor Networks

Wireless Sensor Networks (WSN) is formerly created to support text-based data communication. However, by improving link level mechanism of WSN with Error Control Coding (ECC), reliable multimedia transmission could be realized. This paper addresses the performance issue of transferring multimedia data, particularly still image data, using real sensor motes platform. An X-ray image is transferred from one mote to other mote in one hop scenario. Forward Error Correction (FEC) and interleaving technique are used to design the code that capable of handling both erasure and noise in the received packet. The results show that erasure code can effectively combat the effect of random noise in one packet despite its number as well as recover small quantity of lost packet. Furthermore, the scheme can increase the image PSNR (Peak Signal to Noise Ratio) up to 18.41 dB as compared to the uncoded counterpart