Adaptive Clone Selection Algorithm for Function Optimization

The performance of underwater acoustic target classification decreases and is unstable when the training set contains noisy, redundant or irrelevant samples. In this paper, a new adaptive immune clonal sample selection algorithm (AICISA) is proposed to address this problem. AICISA is aimed at directing generation evolution. An experiment about the application of AICISA using the multi-field features extracted from 4 kinds of underwater acoustic targets was conducted. Experimental results show that AICISA can select effective subsets of samples. Reducing the sample size by 90%, the classification accuracy of SVM is improved by 10%. AICISA also shows good convergence and stability. The optimal subset of samples obtained by AICISA has good generalization ability and can remarkably reduce the classification time.

Aiming at the lack of relevant research on relationship constraints between facilities in the corridor allocation problem (CAP). In this paper, fixed position constraints and ordering constraints are considered in CAP, and the logistics cost is minimized. Considering that the existing search technology is complicated and time-consuming in dealing with such constrained CAP (cCAP), and immune clone selection algorithm with variable neighborhood operation (ICSAVNS) is provided for solving this problem. Two approaches to initial solution generation are designed to improve the quality of the initial population. A variable neighborhood search operator is embedded to improve the accuracy of the local search. A threshold is set in the mutation operation of the ICSAVNS to achieve population expansion better. A double index of sequences consisting of affinity values and constrained facility index values is used to select and reselect, achieving population compression in the clonal selection part. Finally, by exactly solving the model, the rationality of the model is verified. The hybrid clone selection algorithm is used to solve the cCAP and cbCAP benchmark instances of different sizes, and compared with the state-of-the-art optimization algorithms. The results show that the proposed algorithm exhibits better performance.

ABSTRACTThe optimal design and installation of hybrid photovoltaic (PV), wind turbine (WT) distributed generation (DG), and battery energy storage system (BESS) in radial distribution network (RDN) using dynamic arithmetic optimization algorithm (DAOA) is the main purpose of this work. The DAOA algorithm is the improved version of arithmetic optimization algorithm (AOA) which is widely used in the field of Electrical Engineering to solve the different optimization tasks. The simultaneous minimization of active power loss and voltage deviation is taken as the objectives to improve the efficiency of the distribution network using hybrid PV/WT/BESS system. To establish the efficacy of the suggested DAOA method, it is examined on two very well‐known test systems (33‐bus and 69‐bus). The uncertainty of power generation from PV‐ and WT‐based DG due to solar irradiance and wind speed variation also is taken into consideration. The cost analysis of the hybrid PV/WT/BESS system as well as active power loss cost are also assessed in this study. The result comparison shows that the DAOA algorithm gives supreme result in case of active power losses, voltage deviation, and benefits in active power lost cost. This algorithm has good speed of response and solution quality when hybrid PV/WT/BESS system allocation problem of the distribution network with simultaneous power loss reduction and voltage deviation minimization is executed in RDN. The placement of hybrid PV/WT/BESS system in 33‐bus reduces the active power loss by 44.27%, 41.47%, 46.23% using DAOA and 44.26%, 39.87%, 45.18% using AOA for normal loading, 15% increased loading, and 10% decreased loading, respectively. Similar results are also observed for 69‐bus test system. Moreover, if the performance of the DAOA, AOA, immune clone selection algorithm (ICSA), clone selection algorithm (CSA), manta ray foraging optimization (MRFO), and particle swarm optimization (PSO) for PV, WT, BESS‐ based 33‐bus system are observed, it can be noticed that DAOA gives annual savings of 10$, 496$, 505$, 669$, and 554$ as compared to AOA, ICSA, CSA, MRFO, and PSO, respectively. Similarly for PV, WT, BESS‐based 69‐bus system, it is observed that DAOA gives annual savings of 29$, 3147$, 3154$, 3342$, and 3158$ as compared to AOA, ICSA, CSA, MRFO, and PSO, respectively. In case of solution quality, the DAOA method grants its superiority over several optimization methods found in the literature.

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

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 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.

Cognitive radar is a new framework of radar system proposed by Simon Haykin recently. Adaptive waveform selection is an important problem of intelligent transmitter in cognit i ve radar. In this paper, the problem of adaptive waveform selection is modeled as stochastic dynamic programming model. Then backward dynamic programming, temporal difference learning and Q-learning are used to solve this problem. Optimal waveform selection algorithm and approximate solutions are proposed respectively. The simulation results demonstrate that the two approximate methods approach the optimal waveform selection scheme and have lower uncertainty of state estimation compared to fixed waveform. The performance of temporal difference learning is better than Q-learning, but Q-learning is more suitable to use in radar scene. Finally, the whole paper is summarized.

The feasibility of a parameter identification method based on symbolic time series analysis (STSA) and the adaptive immune clonal selection algorithm (AICSA) is studied. Data symbolization by using STSA alleviates the effects of harmful noise in raw acceleration data. The effect of the parameters in STSA is theoretically evaluated and numerically verified. AICSA is employed to minimize the error between the state sequence histogram (SSH) that is transformed from raw acceleration data by STSA. The proposed methodology is evaluated by comparing it with AICSA using raw acceleration data. AICSA combining STSA is proved to be a powerful tool for identifying unknown parameters of structural systems even when the data is contaminated with relatively large amounts of noise.

The immune algorithm based on clonal selection can be used for identification and optimization of the parameters of Sugeno fuzzy model extracted from numerical data. In [5] the rules of fuzzy model were extracted using clonal selection for clustering task, implemented in the MATLAB code. In this paper, the application of the clonal selection has been proposed to solve optimization problems. The algorithm of clonal selection can be used before the clustering version of identification of parameters of the fuzzy model Sugeno-type. Then the same algorithm can be adapted to solve optimization tasks. The results were applied to approximation of a test function.

The job shop scheduling problem (JSSP) is a notoriously difficult problem in combinatorial optimization. Extensive investigation has been devoted to developing efficient algorithms to find optimal or near-optimal solutions. This paper proposes an improved immune clonal selection algorithm, called improved clonal selection algorithm for the JSSP. The new algorithm has the advantage of preventing from prematurity and fast convergence speed. Numerous well-studied benchmark examples in job-shop scheduling problems were utilized to evaluate the proposed approach. The computational results show that the proposed algorithm could obtain the high-quality solutions within reasonable computing times, and the results indicate the effectiveness and flexibility of the immune memory clonal selection algorithm.

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.

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.

Multi-objective adaptive clonal selection algorithm for solving optimal power flow considering multi-type FACTS devices and load uncertainty

The least-cost multicast routing with delay constrained is an NP-Complete problems, to deal with which, a Multi-Agent Immune Clonal Selection Algorithm based multicast routing (MAICSA) is proposed in this paper. MAICSA combines the characteristic of Multi-Agent with the search strategy of Immune Clonal Selection Algorithm. To compare with the conventional Genetic Algorithm (GA), MAICSA overcomes the most serious drawbacks, such as slow convergence rate and “prematurity”. The experimental results show that MAICSA has faster astringency and higher precision than traditional GA, MAGA (Multi-Agent multicast routing based on Genetic Algorithm) and MAIA (Multi-Agent multicast routing based on Immune Algorithm).

Feature selection (FS) constitutes a critical stage within the realms of machine learning and data mining, with the objective of eliminating irrelevant features while guaranteeing model accuracy. Nevertheless, in datasets featuring a multitude of features, choosing the optimal feature poses a significant challenge. This study presents an enhanced Sand Cat Swarm Optimization algorithm (MSCSO) to improve the feature selection process, augmenting the algorithm's global search capacity and convergence rate via multiple innovative strategies. Specifically, this study devised logistic chaotic mapping and lens imaging reverse learning approaches for population initialization to enhance population diversity; balanced global exploration and local development capabilities through nonlinear parameter processing; and introduced a Weibull flight strategy and triangular parade strategy to optimize individual position updates. Additionally, the Gaussian-Cauchy mutation strategy was employed to improve the algorithm's ability to overcome local optima. The experimental results demonstrate that MSCSO performs well on 65.2% of the test functions in the CEC2005 benchmark test; on the 15 datasets of UCI, MSCSO achieved the best average fitness in 93.3% of the datasets and achieved the fewest feature selections in 86.7% of the datasets while attaining the best average accuracy across 100% of the datasets, significantly outperforming other comparative algorithms.