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Differential evolution combined with clonal selection for dynamic economic dispatch

JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2015年第3期27卷 325-350页

作者： Niu, Q. Li, K. Irwin, G. W. Shanghai Univ Sch Mechatron Engn & Automat Shanghai Key Lab Power Stn Automat Technol Shanghai 200072 Peoples R China Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Intelligent Syst & Control Belfast BT9 5AH Antrim North Ireland

Dynamic economic load dispatch (DELD) is one of the most important steps in power system operation. Various optimisation algorithms for solving the problem have been developed;however, due to the non-convex characteristics and large dimensionality of the problem, it is necessary to explore new methods to further improve the dispatch results and minimise the costs. This article proposes a hybrid differential evolution (DE) algorithm, namely clonal selection-based differential evolution (CSDE), to solve the problem. CSDE is an artificial intelligence technique that can be applied to complex optimisation problems which are for example nonlinear, large scale, non-convex and discontinuous. This hybrid algorithm combines the clonal selection algorithm (CSA) as the local search technique to update the best individual in the population, which enhances the diversity of the solutions and prevents premature convergence in DE. Furthermore, we investigate four mutation operations which are used in CSA as the hyper-mutation operations. Finally, an efficient solution repair method is designed for DELD to satisfy the complicated equality and inequality constraints of the power system to guarantee the feasibility of the solutions. Two benchmark power systems are used to evaluate the performance of the proposed method. The experimental results show that the proposed CSDE/best/1 approach significantly outperforms nine other variants of CSDE and DE, as well as most other published methods, in terms of the quality of the solution and the convergence characteristics.

关键词： clonal selection algorithm differential evolution dynamic economic load dispatch valve-point effects

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A novel artificial immune clonal selection classification and rule mining with swarm learning model

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CONNECTION SCIENCE 2013年第2-3期25卷 75-127页

作者： Al-Sheshtawi, Khaled A. Abdul-Kader, Hatem M. Elsisi, Ashraf B. King Abdulaziz Univ Dept Informat Technol Jeddah 21589 Saudi Arabia Menoufia Univ Fac Comp & Informat Dept Informat Syst Menoufia Egypt Menoufia Univ Fac Comp & Informat Dept Comp Sci Menoufia Egypt

Metaheuristic optimisation algorithms have become popular choice for solving complex problems. By integrating Artificial Immune clonal selection algorithm (CSA) and particle swarm optimisation (PSO) algorithm, a novel hybrid clonal selection Classification and Rule Mining with Swarm Learning algorithm (CS2) is proposed. The main goal of the approach is to exploit and explore the parallel computation merit of clonal selection and the speed and self-organisation merits of Particle Swarm by sharing information between clonal selection population and particle swarm. Hence, we employed the advantages of PSO to improve the mutation mechanism of the artificial immune CSA and to mine classification rules within datasets. Consequently, our proposed algorithm required less training time and memory cells in comparison to other AIS algorithms. In this paper, classification rule mining has been modelled as a miltiobjective optimisation problem with predictive accuracy. The multiobjective approach is intended to allow the PSO algorithm to return an approximation to the accuracy and comprehensibility border, containing solutions that are spread across the border. We compared our proposed algorithm classification accuracy CS2 with five commonly used CSAs, namely: AIRS1, AIRS2, AIRS-Parallel, clonalG, and CSCA using eight benchmark datasets. We also compared our proposed algorithm classification accuracy CS2 with other five methods, namely: Naive Bayes, SVM, MLP, CART, and RFB. The results show that the proposed algorithm is comparable to the 10 studied algorithms. As a result, the hybridisation, built of CSA and PSO, can develop respective merit, compensate opponent defect, and make search-optimal effect and speed better.

关键词： artificial immune system clonal selection algorithm particle swarm optimisation hybrid mutation

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Unsupervised feature selection based on maximum information and minimum redundancy for hyperspectral images

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PATTERN RECOGNITION 2016年 51卷 295-309页

作者： Feng, Jie Jiao, Licheng Liu, Fang Sun, Tao Zhang, Xiangrong Xidian Univ Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China

Unsupervised feature selection plays an important role in hyperspectral image processing. It is a very challenge issue to select an effective feature subset with the unavailability of class labels. To select the features maximally preserving the information of original features, a maximum joint mutual information (MJMI) criterion is defined. Since the high-order distribution involved in MJMI is hard to calculate, a maximum information and minimum redundancy (MIMR) criterion is derived as the low-order approximation of MJMI. From information theory, many classical unsupervised feature selection criteria can also be considered as the low-order approximations of MJMI. Compared with them, MIMR requires more relaxed approximation condition. Moreover, a new clonal selection algorithm (CSA) in artificial immune system is devised to optimize the selected features with the guidance of MIMR. Experimental results on several hyperspectral datasets demonstrate that the proposed method obtains better feature subsets compared with classical unsupervised feature selection methods. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Unsupervised feature selection Hyperspectral images Maximum information and minimum redundancy Information theory clonal selection algorithm

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Solving Multi-objective Portfolio Optimization Problem for Saudi Arabia Stock Market Using Hybrid clonal selection and Particle Swarm Optimization

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2015年第8期40卷 2407-2421页

作者： Bin Shalan, Sara A. Ykhlef, Mourad King Saud Univ Dept Nat & Engn Sci Coll Appl Studies & Community Serv Riyadh Saudi Arabia King Saud Univ Coll Comp & Informat Sci Dept Informat Syst Riyadh Saudi Arabia

The portfolio is a group of assets held by an institution or a private individual. Each asset has an investment share of the total investment. The investor tries to distribute the investment to these different assets. The main issue in portfolio optimization is the allocation of different assets for maximum return and minimum risk within a suitable time. These two objectives lead to the multi-objective portfolio optimization problem, which must be solved. Several previous studies have addressed this issue. In this article, we propose a new intelligence hybrid evolutionary algorithm that combines clonal selection with particle swarm optimization to optimize the portfolio's return and risk. We then show the results of the proposed solution through experiments that are conducted using stocks in Kingdom of Saudi Arabia stock exchange market (Tadawul). Moreover, we compare our hybrid algorithm, clonal selection and particle swarm optimization-based solution.

关键词： clonal selection algorithm Hybrid evolutionary algorithm Portfolio optimization Particle swarm optimization algorithm

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A novel ensemble decision tree based on under-sampling and clonal selection for web spam detection

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PATTERN ANALYSIS AND APPLICATIONS 2018年第3期21卷 741-754页

作者： Lu, Xiao-Yong Chen, Mu-Sheng Wu, Jheng-Long Chang, Pei-Chan Chen, Meng-Hui Nanchang Univ Sch Software Engn Nanchang 330047 Jiangxi Peoples R China Nanchang Univ Sch Informat Engn Nanchang 330031 Jiangxi Peoples R China Nanchang Univ Software Sch Nanchang 330031 Jiangxi Peoples R China Acad Sinica Inst Informat Sci Taipei 115 Taiwan Yuan Ze Univ Informat Management & Innovat Ctr Big Data & Digi Taoyuan 32003 Taiwan

Currently, web spamming is a serious problem for search engines. It not only degrades the quality of search results by intentionally boosting undesirable web pages to users, but also causes the search engine to waste a significant amount of computational and storage resources in manipulating useless information. In this paper, we present a novel ensemble classifier for web spam detection which combines the clonal selection algorithm for feature selection and under-sampling for data balancing. This web spam detection system is called USCS. The USCS ensemble classifiers can automatically sample and select sub-classifiers. First, the system will convert the imbalanced training dataset into several balanced datasets using the under-sampling method. Second, the system will automatically select several optimal feature subsets for each sub-classifier using a customized clonal selection algorithm. Third, the system will build several C4.5 decision tree sub-classifiers from these balanced datasets based on its specified features. Finally, these sub-classifiers will be used to construct an ensemble decision tree classifier which will be applied to classify the examples in the testing data. Experiments on WEBSPAM-UK2006 dataset on the web spam problem show that our proposed approach, the USCS ensemble web spam classifier, contributes significant classification performance compared to several baseline systems and state-of-the-art approaches.

关键词： Web spam detection Ensemble learning clonal selection algorithm Feature selection Decision trees

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A parallel hybrid approach integrating clonal selection with artificial bee colony for logistic regression in spam email detection

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Neural Computing and Applications 2024年 1-19页

作者： Dedeturk, Bilge Kagan Akay, Bahriye Department of Software Engineering Erciyes University Kayseri Melikgazi38039 Turkey Department of Computer Engineering Erciyes University Kayseri Melikgazi38039 Turkey

Spam emails are sent to recipients for advertisement and phishing purposes. In either case, it disturbs recipients and reduces communication quality. Addressing this issue requires classifying emails on servers as either spam or ham. Numerous methods have been proposed for this classification task. Among them, logistic regression (LR) stands out for its simplicity, speed, and ease of implementation. However, LR suffers from low detection rates caused by the gradient descent algorithm used in its training phase. To overcome this limitation, we propose a novel method based on the clonal selection algorithm (CSA), renowned for its success in optimization problems due to its local and global search capabilities. Despite CSA’s effective optimization performance, it suffers from robustness and slow training time. Therefore, the CSA and artificial bee colony (ABC) algorithms are hybridized to improve CSA’s robustness and are parallelized to reduce the training time significantly. This hybrid method is employed to optimize the weights of LR by minimizing the cost at the output of LR. The empirical results denote that the proposed method, named CSA–ABC–LR, yields better classification performance compared to state-of-the-art models reported by previous studies, demonstrating an accuracy rate of 99.13% on the Enron-1 dataset, 99.22% on the CSDMC2010 dataset, and 94.49% on the Spambase dataset. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Artificial bee colony clonal selection algorithm Email spam detection Logistic regression Spam filtering

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Orthogonal Immune algorithm with Diversity-based selection for Numerical Optimization 09

Orthogonal Immune Algorithm with Diversity-based Selection f...

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World Summit on Genetic and Evolutionary Computation (GEC 09)

作者： Gong, Maoguo Jiao, Licheng Ma, Wenping Xidian Univ Inst Intelligent Informat Proc Minist Educ China Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China

ISBN: (纸本)9781605583266

In this study, we design an Orthogonal Immune algorithm (OIA) for numerical optimization by incorporating orthogonal initialization, a novel neighborhood orthogonal cloning operator, a static hypermutation operator, and a novel diversity-based selection operator. The OIA is unique in three respects: Firstly, a new selection method based on orthogonal arrays is provided in order to maintain diversity in the population. Secondly, the orthogonal design with quantization technique is introduced to generate initial population. Thirdly, the orthogonal design with the modified quantization technique is introduced into the cloning operator. In order to identify any improvement due to orthogonal initialization, diversity-based selection and neighborhood orthogonal cloning, we modify the OIA via replacing its orthogonal initialization by random initialization;replacing its diversity-based selection by a standard evolutionary operator (mu+lambda)-selection operator;and replacing its neighborhood orthogonal cloning by proportional cloning, and compare the four version algorithms in solving eight benchmark functions and six composition functions.

关键词： Orthogonal experimental design clonal selection algorithm evolutionary algorithm numerical optimization

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Elastic and Inelastic Content Distribution based on clonal selection in VANETs

Elastic and Inelastic Content Distribution based on Clonal S...

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IEEE Global Communications Conference (IEEE GLOBECOM)

作者： Hu, Jinna Chen, Chen Qiu, Tie Atiquzzaman, Mohammed Pei, Qingqi Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Tianjin Univ Sch Comp Sci & Technol Tianjin 300350 Peoples R China Univ Oklahoma Sch Comp Sci Norman OK 73019 USA Shaanxi Key Lab Blockchain & Secure Comp Xian 710071 Peoples R China

ISBN: (纸本)9781728109626

In Vehicular Ad Hoc Networks (VANETs), the unreliable wireless environment and highly dynamic topology make multi-content distribution inefficient due to the large amount of content requests from vehicles. This gives rise to the need for new content distribution schemes in VANETs. Current researches on content distribution for VANETs generally focus on the single type of content. In this paper, we consider both elastic (with no hard delay requirement) and inelastic (with a hard deadline) contents and propose a joint content distribution scheme in VANETs. We model the content distribution as an optimization problem which jointly minimizes the waiting delay of elastic requests and the failure ratio of inelastic requests. Then, we propose an efficient clonal selection-based approximate algorithm to solve the optimization problem. The performance of our scheme is evaluated by simulation using realistic vehicular traces. Simulation results show that our proposed scheme has better performance than previous solutions.

关键词： VANETs content distribution inelastic contents clonal selection algorithm

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Extracting Information in Agricultural Data Using Fuzzy-Rough Sets Hybridization and clonal selection Theory Inspired algorithms

Extracting Information in Agricultural Data Using Fuzzy-Roug...

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11th International Conference on Natural Computation (ICNC) / 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Lasisi, Ayodele Ghazali, Rozaida Deris, Mustafa Mat Herawan, Tutut Lasisi, Fola Univ Tun Hussein Onn Malaysia Fac Comp Sci & Informat Technol Batu Pahat 86400 Johor Malaysia Univ Malaya Fac Comp Sci & Informat Technol Kuala Lumpur 50603 Malaysia Obafemi Awolowo Univ Dept Agr Engn Ife Nigeria

Mining agricultural data with artificial immune system (AIS) algorithms, particularly the clonal selection algorithm (clonalG) and artificial immune recognition system (AIRS), form the bedrock of this paper. The fuzzy-rough feature selection (FRFS) and vaguely quantified rough set (VQRS) feature selection are coupled with clonalG and AIRS for improved detection and computational efficiencies. Comparative simulations with sequential minimal optimization and multi-layer perceptron reveal that the clonalG and AIRS produced significant results. Their respective FRFS and VQRS upgrades namely, FRFS-clonalG, FRFS-AIRS, VQRS-clonalG, and VQRS-AIRS, are able to generate the highest detection rates and lowest false alarm rates. Thus, gathering useful information with the AIS models can help to enhance productivity related to agriculture.

关键词： clonal selection algorithm artificial immune recognition system fuzzy-rough set vaguely quantified rough set

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A Novel Clone selection algorithm for High-dimensional Global Optimization Problems

A Novel Clone Selection Algorithm for High-dimensional Globa...

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Asia-Pacific Conference on Information Processing (APCIP 2009)

作者： Liu, Xingbao Shi, Liangwu Chen, Rongyuan Chen, Haijun Hunan Coll Business Educ Ctr Modern Tech Changsha Hunan Peoples R China

ISBN: (纸本)9780769536996

The clone selection algorithm (CSA) is a stochastic, population-based evolutionary method that can be applied to the global optimization problems. The paper proposes a variation on the traditional CSA: clone selection algorithm with simplex crossover, or CSA_SPX. The novel algorithm employs the randomized distribution scheme for clone individuals, bit hyper-mutation and simplex crossover to significantly improve the performance of the original algorithm. Application of the CSA_SPX on 23 benchmark optimization problems shows a marked improvement in performance over the traditional CSA.

关键词： clonal selection algorithm simplex crossover global optimization differential evolution

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