Ant Colony Optimization (ACO)

The problem of structure extraction from the image which contains many clustered objects is a challenging one for high level image analysis. When an image contains many clustered objects overlapping of objects can cause for hiding the structure. The existing segmentation techniques for better understanding, not able to the address the constituent parts of the image implicitly. The approaches like multistage segmentation address to some extent, but for each stage a separate structure is extracted, and thus causes for the ambiguity about the structure. The proposed approach called Ant Colony Optimization and Fuzzy logic based technique resolves this problem, and gives the implicit structure, that meets with original structure. The segmentation approach uses the swarm intelligence technique based on the behavior of the ant colonies. The segmentation is the process of separating the non-overlapping regions that constitute an image. The segmentation is important for structured and non-structured image analysis and classification for better understanding

Travelling salesman problem (TSP) is a most popular combinatorial routing problem, belongs to the class of NP-hard problems. Many approacheshave been proposed for TSP.Among them, swarm intelligence (SI) algorithms can effectively achieve optimal tours with the minimum lengths and attempt to avoid trapping in local minima points. The transcendence of each SI is depended on the nature of the problem. In our studies, there has been yet no any article, which had compared the performance of SI algorithms for TSP perfectly. In this paper,four common SI algorithms are used to solve TSP, in order to compare the performance of SI algorithms for the TSP problem. These algorithms include genetic algorithm, particle swarm optimization, ant colony optimization, and artificial bee colony. For each SI, the various parameters and operators were tested, and the best values were selected for it. Experiments oversome benchmarks fromTSPLIBshow that artificial bee colony algorithm is the best one among the fourSI-basedmethods to solverouting problems like TSP.

In the age of digital and network, every high efficiency and high profit activity has to harmonize with internet. The business behaviors and activities always are the precursor for getting high efficiency and high profit. Consequently, each business behavior and activities have to adjust for integrating with internet. Underlay on the internet, business extension and promotion behaviors and activities general are called the Electronic Commerce (E-commerce). The quality of web-based customer service is the capability of a firm's website to provide individual heed and attention. Today scenario personalization has become a vital business problem in various e-commerce applications, ranging from various dynamic web content presentations. In our paper Iterative technique partitions the customer in terms of frankly combining transactional data of various consumers that forms dissimilar customer behavior for each group, and best customers are acquired, by applying approach such as, IE (Iterative Evolution), ID (Iterative Diminution) and II (Iterative Intermingle) algorithm. The excellence of clustering is improved via Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). In this paper these two algorithms are compared and it is found that Iterative technique chorus Particle Swarm Optimization (PSO) is better than the other Ant Colony Optimization (ACO) algorithms. Additionally the results show that the Particle Swarm Optimization (PSO) algorithm outperforms other Ant Colony Optimization (ACO) algorithms methods. Finally quality is superior along with this response time higher and cost wise performance is increased and both accuracy and efficiency.

This work proposes an optimization algorithm to control speed of a permanent magnet synchronous motor (PMSM) during starting and speed reversal of motor, as well as during load disturbance conditions. The objective is to minimize the integral absolute control error of the PMSM shaft speed to achieve fast and accurate speed response under load disturbance and speed reversal conditions. The maximum overshoot, peak time, settling time and rise time of the motor is also minimized to obtain efficient transient speed response. Optimum speed control of PMSM is obtained with the aid of a PID speed controller. Modified Particle Swarm Optimization (MPSO) and Ant Colony Optimization (ACO) techniques has been employed for tuning of the PID speed controller, to determine its gain coefficients (proportional, integral and derivative). Simulation results demonstrate that with use of MPSO and ACO techniques improved control performance of PMSM can be achieved in comparison to the classical Ziegler-Nichols (Z-N) method of PID tuning.

The problem of structure extraction from the image which contains many clustered objects is a challenging one for high level image analysis. When an image contains many clustered objects overlapping of objects can cause for hiding the structure. The existing segmentation techniques for better understanding, not able to the address the constituent parts of the image implicitly. The approaches like multistage segmentation address to some extent, but for each stage a separate structure is extracted, and thus causes for the ambiguity about the structure. The proposed approach called Ant Colony Optimization and Fuzzy logic based technique resolves this problem, and gives the implicit structure, that meets with original structure. The segmentation approach uses the swarm intelligence technique based on the behavior of the ant colonies. The segmentation is the process of separating the non-overlapping regions that constitute an image. The segmentation is important for structured and non-structured image analysis and classification for better understanding.

This paper presents, an optimal basic speed controller for switched reluctance motor (SRM) based on ant colony optimization (ACO) with the presence of good accuracies and performances. The control mechanism consists of proportional-integral (PI) speed controller in the outer loop and hysteresis current controller in the inner loop for the three phases, 6/4 switched reluctance motor. Because of nonlinear characteristics of a SRM, ACO algorithm is employed to tune coefficients of PI speed controller by minimizing the time domain objective function. Simulations of ACO based control of SRM are carried out using MATLAB /SIMULINK software. The behavior of the proposed ACO has been estimated with the classical Ziegler-Nichols (ZN) method in order to prove the proposed approach is able to improve the parameters of PI chosen by ZN method. Simulations results confirm the better behavior of the optimized PI controller based on ACO compared with optimized PI controller based on classical Ziegler-Nichols method. 1. INTRODUCTION The inherent simplicity, ruggedness and low cost of a SRM make it a viable machine for various general purpose adjustable speed drive applications [1], [2]. It has no permanent magnet (PM) or winding on the rotor. This structure not only reduces the cost of the SRM but also offers high speed operation capability for this motor. The performance of SRMs has been enhanced greatly due to advances in power electronics and computer science. It requires only simple converter circuit with reduced number of switches due to unidirectional current requirements. In addition, the inverter of the SRM drive has a reliable topology. The stator windings are connected in series with the upper and lower switches of the inverter. This topology can prevent the shoot through fault that exists in the induction and permanent motor drive inverter [3]. These advantages make this type of motors economically alternative to PMBLDC motor, squirrel cage induction motor and DC series motor [4], [5]. The SRM can be operated in the four quadrants and it is very much suitable for hazardous areas requiring high performances such a in electric vehicle propulsion, automotive starter-generators, aerospace applications. Nevertheless, it suffers some drawbacks such as; high torque ripple and significant acoustic noise as well as speed oscillations. Currently, much research is being done on SRM Control and torque constraint in order to make it compete with fully controlled DC and DC drives. In order to resolve these problems, it used essentially two primary approaches: one method is to improve the magnetic design of the

The radix (R) used in Multiple-Valued Logic (MVL) circuits goes beyond the binary case (R = 2). MVLRead Only Memory (ROM), Random Access Memory (RAM), and Digital Signal Processing (DSP) systems have been successfully implemented using Complementary Metal Oxide Semiconductor (CMOS) Technology. The complexity of exact synthesis of MVL circuits is prohibitively large. This is because of the enormous solution search space. The Direct Cover (DC) algorithm is a well-known heuristic for synthesis of MVL functions. The algorithm selects the next minterm to be covered and the appropriate implicant to cover it in an iterative manner using a set of synthesis criteria. Evolutionary algorithms, such as Genetic Algorithms (GAs) and Ant Colony (ACO) have also been successfully used in synthesis of MVL functions. In this paper, we introduce and compare synthesis of MVL functions using the GAs and ACO with the corresponding synthesis using the conventional DC algorithm. Our comparison is based on synthesizing a benchmark consisting of 50000 randomly generated 2-variable 4-valued randomly generated functions. The obtained results showed that the average number of product terms (PTs) needed to synthesize a given MVL function using evolutionary techniques outperforms those obtained using conventional DC heuristics. Among the two techniques it is shown that the technique based on the ACO achieves the best results.