Hybrid Non-Dominated Sorting Genetic Algorithm

Water pollution due to industrial and domestic reasons is highly affecting the water quality. In undeveloped and developed countries, it has become a major reason behind a number of water borne diseases. Poor public health is putting an extra economic liability in order to deploy precautionary measures against these diseases. Recent research works have been directed toward more sustainable solutions to this problem. It has been revealed that good quality of water supply can not only improve the public health, it also accelerates economic growth of a geographical location as well. Water quality prediction using machine learning methods is still at its primitive stage. Besides, most of the studies did not follow any national or international standard for water quality prediction. In the current work, both the problems have been addressed. First, advanced machine learning methods, namely Artificial Neural Networks (ANNs) supported by a well-known multi-objective optimization algorithm called the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) has been used to classify the water samples into two different classes. Secondly, Indian national standard for water quality (IS 10500:2012) has been utilized for this classification task. The hybrid NN-NSGA-II model is compared with another two well-known meta-heuristic supported ANN classifiers, namely ANN trained by Genetic Algorithm (NN-GA) and by Particle Swarm Optimization (NN-PSO). Apart from that, the support vector machine (SVM) has also been included in the comparative study. Besides analysing the performance based on several performance measuring methods, the statistical significance of the results obtained by NN-NSGA-II has been judged by performing Wilcoxon rank sum test with 5% confidence level. Results have indicated the ingenuity of the proposed NN-NSGA-II model over the other classifiers under current study.

Optimized Crossover JumpX in Genetic Algorithm for General Routing Problems

Inspired by nature, genetic algorithms (GA) are among the greatest meta-heuristics optimization methods that have proved their effectiveness to conventional NP-hard problems, especially the traveling salesman problem (TSP) which is one of the most studied Supply chain management problems. This paper proposes a new crossover operator called Jump Crossover (JMPX) for solving the travelling salesmen problem using a genetic algorithm (GA) for near-optimal solutions, to conclude on its efficiency compared to solutions quality given by other conventional operators to the same problem, namely, Partially matched crossover (PMX), Edge recombination Crossover (ERX) and r-opt heuristic with consideration of computational overload. We adopt the path representation technique for our chromosome which is the most direct representation and a low mutation rate to isolate the search space exploration ability of each crossover. The experimental results show that in most cases JMPX can remarkably improve the solution quality of the GA compared to the two existing classic crossover approaches and the r-opt heuristic.

Meta-Heuristic Algorithms in Medical Image Segmentation

Artificial intelligence is the outlet of computer science apprehensive with creating computers that perform as humans. It compromises expert systems, playing games, natural language, and robotics. However, soft computing (SC) varies from the hard (conventional) computing in its tolerant of partial truth, uncertainty, imprecision, and approximation, thus, it models the human mind. The most common SC techniques include neural networks, fuzzy systems, machine learning, and the meta-heuristic stochastic algorithms (e.g., Cellular automata, ant colony optimization, Memetic algorithms, particle swarms, Tabu search, evolutionary computation and simulated annealing. Due to the required accurate diseases analysis, magnetic resonance imaging, computed tomography images and images of other modalities segmentation remains a challenging problem. Over the past years, soft computing approaches attract attention of several researchers for problems solving in medical data applications. Image segmentation is the process that partitioned an image into some groups based on similarity measures. This process is employed for abnormalities volumetric analysis in medical images to identify the disease nature. Recently, meta-heuristic algorithms are conducted to support the segmentation techniques. In the current chapter, different segmentation procedures are addressed. Several meta-heuristic approaches are reported with highlights on their procedures. Finally, several medical applications using meta-heuristic based-approaches for segmentation are discussed.

Chaotic Differential-Evolution-Based Fuzzy Contrast Stretching Method

This study concentrates to develop one novel parameterized Bi-Histogram Fuzzy Contrast Stretching (BHFCS) method for enhancing the contrast of the grey level as well as color images properly. The parameters of this method have been optimized by employing one modified Chaotic Differential Evolution (CDE) with the combined assistance of Fractal Dimension (FD) and Quality Index based on Local Variance (QILV) as objective function. Experimental results prove that the modified DE gives better result than particle swarm optimization (PSO), genetic algorithm (GA) and traditional DE in this enhancement domain and the used objective function is also very useful to preserve the image's original brightness which is the one of the main criterion of the consumer electronics field.

On Developing and Performance Evaluation of Adaptive Second Order Neural Network With GA-Based Training (ASONN-GA) for Financial Time Series Prediction

Financial time series forecasting has been regarded as a challenging issue because of successful prediction could yield significant profit, hence require an efficient prediction system. Conventional ANN based models are not competent systems. Higher order neural networks have several advantages over traditional neural networks such as stronger approximation, higher fault tolerance capacity and faster convergence. With the aim of achieving improved forecasting accuracy, this article develops and evaluates the performance of an adaptive single layer second order neural network with GA based training (ASONN-GA). The global search ability of GA has been incorporated with the better generalization ability of a second order neural network and the model is found quite capable in handling the uncertainties and nonlinearities associated with the financial time series. The model takes minimal input data and considered the partially optimized weight set from previous training, hence a significant reduction in training time. The efficiency of the model has been evaluated by forecasting one-step-ahead closing prices and exchange rates of five real stock markets and it is revealed that the ASONN-GA model achieves better forecasting accuracy over other state of the art models.

Protein Motif Comparator Using Bio-Inspired Two-Way K-Means

In this scientific world, the evolution of the disease is predominantly higher than the medicines. The diagnosis and prognosis of such diseases will differ from patient to patient. In this scenario, the protein motifs are very useful for understanding the functionality and lethality of the disease. Most of the existing techniques are supervised approaches which require prior knowledge of the data. As the protein sequences are unsupervised data, the unsupervised data mining techniques like Clustering and 2-way Clustering are chosen to mine the homologous protein motifs. The quality of the results is refined further using the bio-inspired computing models like Particle Swarm Optimization, Genetic Algorithm and Venus Flytrap Optimization in this research work. The existing approaches can mine homologous patterns with structure similarity of 75 percent which is increased in this proposed approach. The results from these three different approaches show that the bio-inspired based 2-way Clustering approaches can mine more homologous motifs than the clustering approaches.

Effect of SMES Unit in AGC of an Interconnected Multi-Area Thermal Power System With ACO-Tuned PID Controller

Load changes in any one of interconnected power system that influence the system response from their nominal values. The Proportional–Integral- Derivative (PID) controller is employed to mitigate this issue as a secondary controller in addition to the Superconducting Magnetic Energy Storage (SMES) unit. In Automatic Generation Control (AGC), the current work proposed an Ant Colony Optimization (ACO) technique to tune PID controller gain values of multi-area interconnected thermal power system. The gain value of PID controller is tuned by using the ACO techniques. The system response is compared with and without considering SMES unit in the system. The comparative results clearly established that the system response with SMES unit improve the performance of system during sudden load disturbance.

Intelligent Computing in Medical Imaging

Biomedical imaging is considered main procedure to acquire valuable physical information about the human body and some other biological species. It produces specialized images of different parts of the biological species for clinical analysis. It assimilates various specialized domains including nuclear medicine, radiological imaging, Positron emission tomography (PET), and microscopy. From the early discovery of X-rays, progress in biomedical imaging continued resulting in highly sophisticated medical imaging modalities, such as magnetic resonance imaging (MRI), ultrasound, Computed Tomography (CT), and lungs monitoring. These biomedical imaging techniques assist physicians for faster and accurate analysis and treatment. The present chapter discussed the impact of intelligent computing methods for biomedical image analysis and healthcare. Different Artificial Intelligence (AI) based automated biomedical image analysis are considered. Different approaches are discussed including the AI ability to resolve various medical imaging problems. It also introduced the popular AI procedures that employed to solve some special problems in medicine. Artificial Neural Network (ANN) and support vector machine (SVM) are active to classify different types of images from various imaging modalities. Different diagnostic analysis, such as mammogram analysis, MRI brain image analysis, CT images, PET images, and bone/retinal analysis using ANN, feed-forward back propagation ANN, probabilistic ANN, and extreme learning machine continuously. Various optimization techniques of ant colony optimization (ACO), genetic algorithm (GA), particle swarm optimization (PSO) and other bio-inspired procedures are also frequently conducted for feature extraction/selection and classification. The advantages and disadvantages of some AI approaches are discussed in the present chapter along with some suggested future research perspectives.

Metaheuristics in Manufacturing

The state of a cutting tool is an important factor in any metal cutting process as additional costs in terms of scrapped components, machine tool breakage and unscheduled downtime result from worn tool usage. Therefore, tool wear prediction plays an important role in industry automation for higher productivity and acceptable product quality. Therefore, in order to increase the productivity of turning process, various researches have been made recently for tool wear estimation and classification in turning process. Chip form is one of the most important factors commonly considered in evaluating the performance of machining process. On account of the effect of the progressive tool wear on the shape and geometrical features of produced chip, it is possible to predict some measurable machining outputs such as crater wear. According to experimentally performed researches, cutting speed and cutting time are two extremely effective parameters which contribute to the development of the crater wear on the tool rake face. As a result, these parameters will change the chip radius and geometry. This chapter presents the development of the genetic equation for the tool wear using occurred changes in chip radius in turning process. The development of the equation combines different methods and technologies like evolutionary methods, manufacturing technology, measuring and control technology with the adequate hardware and software support. The results obtained from genetic equation and experiments showed that obtained genetic equations are correlated well with the experimental data. Furthermore, it can be used for tool wear estimation during cutting process and because of its parametric form, genetic equation enables us to analyze the effect of input parameters on the crater wear parameters.

Multi-Objective Optimal Power Flow Using Metaheuristic Optimization Algorithms With Unified Power Flow Controller to Enhance the Power System Performance

In this chapter a multi objective optimal power flow (OPF) is obtained by using latest Metaheuristic optimization techniques BAT search algorithm (BAT), cuckoo search algorithm (CSA) and firefly algorithm (FA) with Unified power flow controller (UPFC). UPFC is a voltage source converter type Flexible Alternating Current Transmission System (FACTS) device. It is able to control the voltage magnitudes, voltage angles and line impedances individually or simultaneously. To enhance the power system performance, the optimal power flow has been incorporated UPFC along with BAT algorithm, cuckoo search algorithm and firefly algorithm based multi objective function comprising of two objectives those are total real power loss and the fuel cost of total real power generation. The BAT algorithm, cuckoo search algorithm and firefly algorithm based OPF has been examined and tested on a 5 bus test system and modified IEEE 30 bus system without and with UPFC. The results obtained with BAT algorithm, cuckoo search algorithm and firefly algorithms are compared with Differential Evaluation (DE).