Particle Swarm Optimization Algorithm as a Tool for Profiling from Predictive Data Mining Models

This chapter introduces the methodology of particle swarm optimization algorithm usage as a tool for finding customer profiles based on a previously developed predictive model that predicts events like selection of some products or services with some probabilities. Particle swarm optimization algorithm is used as a tool that finds optimal values of input variables within developed predictive models as referent values for maximization value of probability that customers select/buy a product or service. Recognized results are used as a base for finding similar profiles between customers. The presented methodology has practical value for decision support in business, where information about customer profiles are valuable information for campaign planning and customer portfolio management.

Swarm Intelligence-Based Optimization for PHEV Charging Stations

In this chapter, Gravitational Search Algorithm (GSA) and Particle Swarm Optimization (PSO) technique were applied for intelligent allocation of energy to the Plug-in Hybrid Electric Vehicles (PHEVs). Considering constraints such as energy price, remaining battery capacity, and remaining charging time, they optimized the State-of-Charge (SoC), a key performance indicator in hybrid electric vehicle for the betterment of charging infrastructure. Simulation results obtained for maximizing the highly non-linear objective function evaluates the performance of both techniques in terms of global best fitness and computation time.

Image Enhancement Techniques Using Particle Swarm Optimization Technique

The image quality enhancement process is considered as one of the basic requirement for high-level image processing techniques that demand good quality in images. High-level image processing techniques include feature extraction, morphological processing, pattern recognition, automation engineering, and many more. Many classical enhancement methods are available for enhancing the quality of images and they can be carried out either in spatial domain or in frequency domain. But in real time applications, the quality enhancement process carried out by classical approaches may not serve the purpose. It is required to combine the concept of computational intelligence with the classical approaches to meet the requirements of real-time applications. In recent days, Particle Swarm Optimization (PSO) technique is considered one of the new approaches in optimization techniques and it is used extensively in image processing and pattern recognition applications. In this chapter, image enhancement is considered an optimization problem, and different methods to solve it through PSO are discussed in detail.

Studies of Computational Intelligence Based on the Behaviour of Cockroaches

In this chapter, the author expands the notion of computational intelligence using the behavior of cockroaches. An introduction to cockroach as swarm intelligence emerging research area and literature review of its growing concept is explained in the beginning. The chapter also covers the ideas of hybrid cockroach optimization system. Next, the author studies the applicability of cockroach swarm optimization. Thereafter, the author presents the details of theoretical algorithm and an experimental result of integration of robot to some cockroaches to make collective decisions. Then, the author proposes his algorithm for traversing the shortest distance of city warehouses. Then, a few comparative statistical results of the progress of the present work on cockroach intelligence are shown. Finally, conclusive remarks are given. At last, the author hopes that even researchers with little experience in swarm intelligence will be enabled to apply the proposed algorithm in their own application areas.

A Uniformly Distributed Mobile Sensor Nodes Deployment Strategy Using Swarm Intelligence

WSN consists of spatially dispersed and dedicated sensors for monitoring the physical conditions of the universe and organizing the collected data at a central location. WSN incorporates a gateway that provides wireless connectivity back to the wired world and distributed sensor nodes. Various applications have been proposed for WSN like Ecosystem and Seismic monitoring, where deployment of nodes in a suitable manner is of an immense concern. Currently, sensor nodes are mobile in nature and they are deployed at an accelerated pace. This chapter focuses on developing the mobile nodes in an apt technique to meet the needs of WSNs properly. It considers the swarm intelligence-based movement strategies with the assistance of local communications through which the randomly deployed sensors can arrange themselves to reach the optimal placement to meet the issues like lower cost, lower power consumption, simpler computation, and better sensing of the total area.

Particle Swarm Optimization Method to Design a Linear Tubular Switched Reluctance Generator

In this chapter, the Particle Swarm Optimization method is applied to four different structural configurations of a linear switched reluctance generator with tubular topology. The optimization process involves the search of the values for a defined set of geometric parameters that maximize the rate of change of the generator's inductance with the relative displacement of its mover part. The optimization algorithm is applied to each structural configuration in order to find the optimum geometry as well to identify the most suitable configuration for electric generation.

Remote Sensing Image Classification Using Fuzzy-PSO Hybrid Approach

Pixel classification among overlapping land cover regions in remote sensing imagery is a challenging task. Detection of uncertainty and vagueness are always key features for classifying mixed pixels. This chapter proposes an approach for pixel classification using hybrid approach of Fuzzy C-Means and Particle Swarm Optimization methods. This new unsupervised algorithm is able to identify clusters utilizing particle swarm optimization based on fuzzy membership values. This approach addresses overlapping regions in remote sensing images by uncertainties using fuzzy set membership values. PSO is a population-based stochastic optimization technique inspired from the social behavior of bird flocks. The authors demonstrate the algorithm for segmenting a LANDSAT image of Shanghai. The newly developed algorithm is compared with FCM and K-Means algorithms. The new algorithm-generated clustered regions are verified with the available ground truth knowledge. The validity and statistical analysis are performed to demonstrate the superior performance of the new algorithm with K-Means and FCM algorithms.

Swarm-Based Mean-Variance Mapping Optimization (MVMOS) for Solving Non-Convex Economic Dispatch Problems

The practical Economic Dispatch (ED) problems have non-convex objective functions with complex constraints due to the effects of valve point loadings, multiple fuels, and prohibited zones. This leads to difficulty in finding the global optimal solution of the ED problems. This chapter proposes a new swarm-based Mean-Variance Mapping Optimization (MVMOS) for solving the non-convex ED. The proposed algorithm is a new population-based meta-heuristic optimization technique. Its special feature is a mapping function applied for the mutation. The proposed MVMOS is tested on several test systems and the comparisons of numerical obtained results between MVMOS and other optimization techniques are carried out. The comparisons show that the proposed method is more robust and provides better solution quality than most of the other methods. Therefore, the MVMOS is very favorable for solving non-convex ED problems.

Particle Swarm Optimization-Based Session Key Generation for Wireless Communication (PSOSKG)

In this chapter, a Particle Swarm Optimization-Based Session Key Generation for wireless communication (PSOSKG) is proposed. This cryptographic technique is solely based on the behavior of the particle swarm. Here, particle and velocity vector are formed for generation of keystream by setting up the maximum dimension of each particle and velocity vector. Each particle position and probability value is evaluated. Probability value of each particle can be determined by dividing the position of a particular particle by its length. If probability value of a particle is less than minimum probability value then a velocity is applied to move each particle into a new position. After that, the probability value of the particle at the new position is calculated. A threshold value is selected to evaluate against the velocity level of each particle. The particle having the highest velocity more than predefined threshold value is selected as a keystream for encryption.

Ambiguity Reduction through Optimal Set of Region Selection Using GA and BFO for Handwritten Bangla Character Recognition

To recognize different patterns, identification of local regions where the pattern classes differ significantly is an inherent ability of the human cognitive system. This inherent ability of human beings may be imitated in any pattern recognition system by incorporating the ability of locating the regions that contain the maximum discriminating information among the pattern classes. In this chapter, the concept of Genetic Algorithm (GA) and Bacterial Foraging Optimization (BFO) are discussed to identify those regions having maximum discriminating information. The discussion includes the evaluation of the methods on the sample images of handwritten Bangla digit and Basic character, which is a subset of Bangla character set. Different methods of sub-image or local region creation such as random creation or based on the Center of Gravity (CG) of the foreground pixels are also discussed here. Longest run features, extracted from the generated local regions, are used as local feature in the present chapter. Based on these extracted local features, together with global features, the algorithms are applied to search for the optimal set of local regions. The obtained results are higher than that results obtained without optimization on the same data set.