Clustering and retrieval method of immunological memory cell in clonal selection algorithm

The Coronary Heart Disease Database (CHD_DB) is based on actual measurements of the Framingham Heart Study - one of the most famous prospective studies of cardiovascular dis- ease. The CHD_DB includes more than 6,500 records indicating the development of coronary heart disease (CHD). The data set is statistically proved to be an effective as a benchmark test. This paper describes the classification result of CHD_DB by using some learning methods. Especially, a com- putational implementation of the clonal selection principle that explicitly takes into account the af- finity maturation of the immune response was well discussed. Antibodies generated by the clonal selection algorithm are clustered in some categories according to the affinity maturation, so that immunological memory cells which respond to the specified pathogen are created to record those antibodies. Such observations supply the inspiration for the Clonal Selection Algorithm with Im- munological Memory Cells. For the data set, our proposed method shows about 99.6% classifica- tion capability of training data.

Optimization of multiple input–output fuzzy membership functions using clonal selection algorithm

Bio-inspired computing has demonstrated to be useful in several application areas. Over the last decade, new bio-inspired algorithms have emerged with applications for detection, optimisation and classification for use in computer vision tasks. On the other hand, automatic circle detection in digital images is considered an important and complex task for the computer vision community. Consequently, a tremendous amount of research has been devoted to find an optimal circle detector. This article presents an algorithm for the automatic detection of circular shapes from complicated and noisy images with no consideration of the conventional Hough transform principles. The proposed algorithm is based on newly developed Artificial Immune Optimisation (AIO) technique, known as the Clonal Selection Algorithm (CSA). The CSA is an effective method for searching and optimising following the Clonal Selection Principle (CSP) in the human immune system which generates a response according to the relationship between antigens (Ags), i.e. patterns to be recognised and antibodies (Abs), i.e. possible solutions. The algorithm uses the encoding of three points as candidate circles (x,y,r) over the edge image. An objective function evaluates if such candidate circles (Ab) are actually present in the edge image (Ag). Guided by the values of this objective function, the set of encoded candidate circles are evolved using the CSA so that they can fit to the actual circles on the edge map of the image. Experimental results over several synthetic as well as natural images with varying range of complexity validate the efficiency of the proposed technique with regard to accuracy, speed and robustness.

This paper first discusses the background knowledge of the clonal selection algorithm and particle swarm method. The clonal selection algorithm is imitated by the basic principle of the adaptive immune response to virus stimulus. The particle swarm optimization is motivated by the social behaviors of swarms. Inspired by these two optimization methods, we propose a hybrid optimization algorithm in this paper. The steps of this hybrid optimization algorithm are described in details, and its performance is evaluated hybrid unidimensional function optimization and three multidimensional functions optimization problems. It is also compared with both the clonal selection algorithm and particle swarm method based on numerical simulations

Increasing energy efficiency of rule-based fuzzy clustering algorithms using CLONALG-M for wireless sensor networks

This paper presents the calibration results of the C-Town network obtained using an artificial immune algorithm called Clonal Selection Algorithm (CLONALG).The calibration problem was formulated as determining the network model parameters such that the best match between measured and predicted data is obtained. The model parameters to be determined include pipe roughness values, valve closures, nodal demands, pump controls and valve settings. The artificial immune algorithm used in this work evolves a population of candidate solutions using the clonal selection principle. The main components of CLONALG include fitness based cloning and maturation of cloned population. The proposed model was applied to calibrate the C-Town network. The network calibration parameters obtained, when input into the C-Town hydraulic model, produced reasonably good match between the predicted and the observed tank water levels and pump station flows. Various performance measures were calculated and presented in the paper.

To solve optimal generation scheduling problem of cascaded hydropower stations, a hybrid cultural algorithm based on clonal selection principle (HCA-CSA) is presented. HCA-CSA uses cultural algorithm (CA) as its framework and clonal selection algorithm (CSA) in population space. Considering the characteristics of CSA, three knowledge structures are redefined in belief space to improve the search purposefulness and directivity of CSA, so as to improve the searching convergence rate and precision. In addition, a recombination and a chaos search operation are adopted in belief space to accelerate convergence rate and precision of the proposed algorithm. HCA-CSA is first tested by several benchmark problems and then it is applied to a case study of optimal generation scheduling of the Three Gorges Cascaded Hydropower Stations. The results obtained show its efficiency on solving complex optimization problems, and it can be an alternative for optimal generation scheduling of cascaded hydropower stations.

Clonal selection algorithms (CSAs) is a special class of immune algorithms (IA), inspired by the clonal selection principle of the human immune system. To improve the algorithm′s ability to perform better, this CSA has been modified by implementing two new concepts called fixed mutation factor and ladder mutation factor. Fixed mutation factor maintains a constant factor throughout the process, where as ladder mutation factor changes adaptively based on the affinity of antibodies. This paper compared the conventional CLONALG, with the two proposed approaches and tested on several standard benchmark functions. Experimental results empirically show that the proposed methods ladder mutation‐based clonal selection algorithm (LMCSA) and fixed mutation clonal selection algorithm (FMCSA) significantly outperform the existing CLONALG method in terms of quality of the solution, convergence speed, and solution stability.

In this dissertation, three different evolutionary algorithms are proposed by hybridizing the Taguchi method, chaos disturbance method, multilevel immune algorithm (MIA), clonal selection algorithm (CLONALG), and artificial bee colony algorithm (ABC). Including multi-level immune algorithm and clonal selection algorithm is derived by the immune algorithm. These algorithms are thus called HTCMIABC, HTCABC, and HCABC. The HTCMIABC comprises two main different phases. We use the MIA as the recognition phase to balance local and global search and accelerate the search speed to enhance the evolutionary phase. Second, the evolutionary phase is built on the ABC and chaos disturbance operation to have the capabilities of exploration and exploitation. Moreover, the Taguchi method and crossover operation are inserted between the recognition phase and evolutionary phase for the recombination and diversification of several antibodies to improve the searching ability. In the HTCABC, the chaos search algorithm and adaptive bound method are adopted to improve the ABC performance. Then, the Taguchi method and crossover operation are incorporated into the chaos artificial bee colony (CABC) to accelerate the search capacity. Moreover, the natural phenomenon of the elite strategy is adopted and the recruitment of new scout bees is used for HTCABC, which can maintain the diversity of the population, and escape from local optima. Additionally, there is no complex parameter setting in the algorithm design. In the HCABC, the mutation mechanism of the CLONALG by using the advantages of ABC can improve the capabilities of exploration and exploitation. Finally, the HTCMIABC algorithm is examined by parameter identification of nonlinear chaotic system. Simulation results show that the HTCMIABC is more efficient than some typical existing algorithms. The HTCABC algorithm is examined by using a set of benchmarks and the proposed approach is also applied to solve the parameter identification of a chaotic system. Simulation results show that the HTCABC is more efficient than some existing algorithms reported in the literature. In addition, the simulation results also demonstrate that HCABC can effectively achieve the best PID-like fuzzy controller structure and parameters.

Clonal Selection Algorithm (CSA), inspired by the clonal selection theory, has gained much attention and wide applications. In most common forms, the CSAs use a binary representation of variables, and the emulated immune operators, mutation, proliferation, selection, for example, are made to act on it. However, the binary representation often suffers from the so-called Hamming Cliff problem. In order to overcome this problem, a Gray-coded CSA is presented and used to solve optimization problems. The algorithm is applied to numerous bench-mark problems of numerical optimization problems and the computational results show effectiveness of the proposed algorithm.

High dimensional pose state space is the main challenge in articulated human pose tracking which makes pose analysis computationally expensive or even infeasible. In this paper, we propose a novel generative approach in the framework of evolutionary computation, by which we try to widen the bottleneck with effective search strategy embedded in the extracted state subspace. Firstly, we use ISOMAP to learn the low-dimensional latent space of pose state in the aim of both reducing dimensionality and extracting the prior knowledge of human motion simultaneously. Then, we propose a manifold reconstruction method to establish smooth mappings between the latent space and original space, which enables us to perform pose analysis in the latent space. In the search strategy, we adopt a new evolutionary approach, clonal selection algorithm (CSA), for pose optimization. We design a CSA based method to estimate human pose from static image, which can be used for initialization of motion tracking. In order to make CSA suitable for motion tracking, we propose a sequential CSA (S-CSA) algorithm by incorporating the temporal continuity information into the traditional CSA. Actually, in a Bayesian inference view, the sequential CSA algorithm is in essence a multilayer importance sampling based particle filter. Our methods are demonstrated in different motion types and different image sequences. Experimental results show that our CSA based pose estimation method can achieve viewpoint invariant 3D pose reconstruction and the S-CSA based motion tracking method can achieve accurate and stable tracking of 3D human motion.

ABSTRACTOne of the complex and time‐consuming stages of land consolidation, where both parcel geometry in rural lands and the ownership structure are changed, is land reallocation. Land reallocation consists of land redistribution and land partitioning stages. Land redistribution aims at careful location of the parcels whose ownership statuses have been changed into land blocks constructed through taking many parameters into account. The focus of this study is the optimization of the land redistribution stage. The performance of many optimization algorithms such as genetic algorithms (GA), Artificial Bee Colony (ABC) Algorithm, and Migrating Birds Optimization (MBO) Algorithm has been investigated for the solution of the land redistribution problem. In this study, we apply the Clonal Selection Algorithm (CSA) to the land redistribution problem for the first time, offering a novel approach to improving allocation efficiency. CSA has been applied to the land redistribution problem, which considers various redistribution parameters. The study area covers 1551.1 ha; there are 351 cadastral parcels and 143 blocks where the parcel areas will be redistributed. So as to test the validity of the application results, the outcomes of CSA were compared with those results of Genetic Algorithms (GA), a biological‐based optimization method previously used to solve the problem. According to the results obtained for GA, the best fitness value was achieved (523.4672), and the placement rate based on the most successful preferences was (67.41294). According to the results obtained for CSA, the best fitness value was achieved (296.4551), and the placement rate based on the most successful preferences was (66.54229). The primary goal of this process was to find the lowest fitness values. In addition, the best suitability solutions found with GA and CSA were compared according to the block fullness rates. The deviations in block occupancy rates obtained by GA are larger than the deviations obtained by CSA.

In this paper we propose a novel change detection method based on clonal selection and optimal entropy threshold. We apply the log-ratio operator to construct the difference image of two temporal images firstly. Then the optimal threshold of the difference image is generated automatically by clonal selection and optimal entropy method. In order to reduce the noise impact of remote sensing images, the results of initial segmentation are morphological processed. Experimental results show that the proposed method is effective and stable.

Chaos theory describes complex motion and the dynamics of nonlinear systems. As a complex nonlinear system, the immune system is chaotic. This paper introduces chaos into the clonal selection algorithm by a novel mutation method, self-adaptive chaotic mutation (SACM). In detail, based on the logistic chaotic sequence, SACM extracts antibody's affinity and distribution to adjust the mutation scale. Compared with the clonal selection algorithm using random mutation and standard genetic algorithm, the improved clonal selection algorithm can enhance the precision and stability, and overcome prematurity effectively with a high convergence speed.

Artificial immune systems are classified as computational systems inspired by theoretical immunology and are mechanisms that can solve complex problems. Clonal selection algorithm is a selection algorithm, which is derived as a special selection algorithm to this artificial intelligence concept. In this work, Clonal selection algorithm is applied to design a simple microwave matching network for a load with arbirtary impedance. The results obtained were tested in a microwave simulator and indicated that clonal selection algorithm can be very effectively applied for microwave design problems. © 2011 Wiley Periodicals, Inc. Microwave Opt Technol Lett 53:991–993, 2011; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.25935