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Solving the task assignment problem with ant colony optimisation incorporating ideas from the clonal selection algorithm

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2015年第2期7卷 129-143页

作者： Martinovic, Goran Bajer, Drazen Josip Juraj Strossmayer Univ Osijek Fac Elect Engn Osijek 31000 Croatia

The task assignment problem commonly appears in distributed computing environments. It asks an assignment of tasks to processors is found such that it satisfies the imposed constraints and that the total execution and communication cost of the tasks is minimal. This paper presents an algorithm based on ant colony optimisation that incorporates ideas from the clonal selection algorithm. Namely, the ant colony optimisation algorithm includes the cloning of the iteration-best ant and mutation of its clones' solutions;the goal being a better exploitation of promising parts of the search space. Besides that, the solution construction procedure is modified to take the memory constraints into account and the pheromone update mechanism is modified to enable the best clone to deposit pheromone. The experimental analysis, conducted on a large number of problem instances, showed that the proposed algorithm performs better compared to the MAX-MIN ant system, a differential evolution and a particle swarm optimisation algorithm.

关键词： ant colony optimisation ACO clonal selection algorithm CSA clones' solutions cloning differential evolution DE iteration-best ant MAX-MIN ant system MMAS memory constraints mutation particle swarm optimisation PSO pheromone update mechanism solution construction procedure task assignment problem TAP

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Hybrid learning clonal selection algorithm

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INFORMATION SCIENCES 2015年第1期296卷 128-146页

作者： Peng, Yong Lu, Bao-Liang Shanghai Jiao Tong Univ Dept Comp Sci & Engn Ctr Brain Like Comp & Machine Intelligence Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Shanghai Educ Commiss Intelligent Interact & Cogn Key Lab Shanghai 200240 Peoples R China

Artificial immune system is a class of computational intelligence methods drawing inspiration from human immune system. As one type of popular artificial immune computing model, clonal selection algorithm (CSA) has been widely used for many optimization problems. CSA mainly generates new schemes by hyper-mutation operators which simulate the immune response process. However, these hyper-mutation operators, which usually perturb the antibodies in population, are semi-blind and not effective enough for complex optimization problems.. In this paper, we propose a hybrid learning clonal selection algorithm (HLCSA) by incorporating two learning mechanisms, Baldwinian learning and orthogonal learning, into CSA to guide the immune response process. Specifically, (1) Baldwinian learning is used to direct the genotypic changes based on the Baldwin effect, and this operator can enhance the antibody information by employing other antibodies' information to alter the search space;(2) Orthogonal learning operator is used to search the space defined by one antibody and its best Baldwinian learning vector. In HLCSA, the Baldwinian learning works for exploration (global search) while the orthogonal learning for exploitation (local refinement). Therefore, orthogonal learning can be viewed as the compensation for the search ability of Baldwinian learning. In order to validate the effectiveness of the proposed algorithm, a suite of sixteen benchmark test problems are fed into HLCSA. Experimental results show that HLCSA performs very well in solving most of the optimization problems. Therefore, HLCSA is an effective and robust algorithm for optimization. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Artificial immune system clonal selection algorithm Baldwin effect Baldwinian learning Orthogonal learning Optimization

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A comparative study of clonal selection algorithm for effluent removal forecasting in septic sludge treatment plant

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WATER SCIENCE AND TECHNOLOGY 2015年第4期71卷 524-528页

作者： Chun, Ting Sie Malek, M. A. Ismail, Amelia Ritahani Univ Tenaga Nas Dept Civil Engn Kajang 43000 Selangor Malaysia Univ Tenaga Nas Inst Energy Policy & Res IEPRE Kajang 43000 Selangor Malaysia Int Islamic Univ Malaysia Dept Comp Sci Kulliyyah Informat & Commun Technol Kuala Lumpur 50728 Malaysia

The development of effluent removal prediction is crucial in providing a planning tool necessary for the future development and the construction of a septic sludge treatment plant (SSTP), especially in the developing countries. In order to investigate the expected functionality of the required standard, the prediction of the effluent quality, namely biological oxygen demand, chemical oxygen demand and total suspended solid of an SSTP was modelled using an artificial intelligence approach. In this paper, we adopt the clonal selection algorithm (CSA) to set up a prediction model, with a well-established method - namely the least-square support vector machine (LS-SVM) as a baseline model. The test results of the case study showed that the prediction of the CSA-based SSTP model worked well and provided model performance as satisfactory as the LS-SVM model. The CSA approach shows that fewer control and training parameters are required for model simulation as compared with the LS-SVM approach. The ability of a CSA approach in resolving limited data samples, nonlinear sample function and multidimensional pattern recognition makes it a powerful tool in modelling the prediction of effluent removals in an SSTP.

关键词： biological oxygen demand chemical oxygen demand clonal selection algorithm least-square support vector machine septic sludge treatment plant total suspended solids

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clonal selection algorithm Approach for Multi-objective Optimization of Production-distribution System

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Procedia - Social and Behavioral Sciences 2015年 189卷 48-56页

作者： K.Y. Sreeram Vinay V. Panicker Post graduate student Department of Mechanical Engineering National Institute of Technology Calicut Kozhikode 673601 India Assistant Professor Department of Mechanical Engineering National Institute of Technology Calicut Kozhikode 673601 India

This paper addresses a multi-objective production distribution problem to minimize both total weighted tardiness and total distribution cost. It is an NP hard problem with two objectives. The first objective is to sequence the orders on a production line such that it will decrease the tardiness of orders and the second objective is to ship those orders by considering routing and heterogeneous fleet of vehicles, if possible and thereby decrease the distribution cost. To solve the above problem, this work proposes a clonal selection algorithm. This algorithm uses several strategies for generating the initial population and selecting the individuals for reproduction. Different mutation operators are also utilized for reproducing new individuals. The same algorithm is extended for considering routing between customers and heterogeneous fleet of vehicles. Finally, a Pareto optimal analysis is done to have a diverse set of solutions which are suitable for the environment prevailing at customers end.

关键词： multi-objective production distribution problem meta-heuristics clonal selection algorithm pareto optimal analysis.

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clonal selection algorithms for optimal product line design: A comparative study

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2022年第2期298卷 585-595页

作者： Pantourakis, Michail Tsafarakis, Stelios Zervoudakis, Konstantinos Altsitsiadis, Efthymios Andronikidis, Andreas Ntamadaki, Vasiliki Copenhagen Business Sch Dept Management Soc & Commun Copenhagen Denmark Univ Macedonia Dept Business Adm Thessaloniki Greece Tech Univ Crete Sch Prod Engn & Management Khania Greece

Product design constitutes a critical process for a firm to stay competitive. Whilst the biologically in-spired clonal selection algorithms (CSA) have been applied to efficiently solve several combinatorial op-timization problems, they have not yet been tested for optimal product lines. By adopting a previous comparative analysis with real and simulated conjoint data, we adapt and compare in this context 23 CSA variants. Our comparison demonstrates the efficiency of specific cloning, selection and somatic hy -permutation operators against other optimization algorithms, such as Simulated Annealing and Genetic algorithm. To further investigate the robustness of each method to combinatorial size, we extend the previous paradigm to larger product lines and different optimization objectives. The consequent perfor-mance variation elucidates how each operator shifts the search focus of CSAs. Collectively, our study demonstrates the importance of a fine balance between global and local search in such combinatorial problems, and the ability of CSAs to achieve it. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Or in marketing clonal selection algorithm Combinatorial optimization Product line design

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A Generation Method of Immunological Memory in clonal selection algorithm by using Restricted Boltzmann Machines

A Generation Method of Immunological Memory in Clonal Select...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Kamada, Shin Ichimura, Takumi Hiroshima City Univ Grad Sch Informat Sci Dept Intelligent Syst Asa Minami Ku 3-4-1 Ozuka Higashi Hiroshima 7313194 Japan Prefectural Univ Hiroshima Fac Management & Informat Syst Minami Ku Hiroshima 7348559 Japan

ISBN: (纸本)9781479986965

Recently, a high technique of image processing is required to extract the image features in real time. In our research, the tourist subject data are collected from the Mobile Phone based Participatory Sensing (MPPS) system. Each record consists of image files with GPS, geographic location name, user's numerical evaluation, and comments written in natural language at sightseeing spots where a user really visits. In our previous research, the famous landmarks in sightseeing spot can be detected by clonal selection algorithm with Immunological Memory Cell (CSAIM). However, some landmarks was not detected correctly by the previous method because they didn't have enough amount of information for the feature extraction. In order to improve the weakness, we propose the generation method of immunological memory by Restricted Boltzmann Machines. To verify the effectiveness of the method, some experiments for classification of the subjective data are executed by using machine learning tools for Deep Learning.

关键词： Image Analysis clonal selection algorithm Immunological Memory Cells Restricted Boltzmann Machines Deep Learning Smartphone based Participatory Sensing System Knowledge Discovery

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An integrated approach to detect online financial transaction fraud using negative and clonal selection algorithms with AIS 3

An integrated approach to detect online financial transactio...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Gupta, Rohan Ghanghas, Peshal Uie Chandigarh University Dept. of Ece Mohali India

ISBN: (纸本)9798331515911

The relevancy of fraud detection in account takeover fraud in online financial transactions is at it's peak. The traditional fraud detection approaches have seen a downwards trend. This paper adds more onto the utilization of Biological Immune System to level an AI-based Immune system for detecting frauds and the emerging patterns within it's domain. By implementing various machine learning algorithms such as negative and clonal selection the proposed system can accurately identify previously unknown fraud patterns and various attack strategies used by the unauthorized users and hackers. The integration of this approach seems promising in improving the security and enhancing the dependability of the transactions, hence, safeguarding people from account takeover fraud. © 2025 IEEE.

关键词： Artificial Immune system(AIS) Biological Immune System principles(BISP) clonal selection algorithm Fraud Detection (FD) negative selection algorithm online financial transaction fraud

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clonal selection algorithm in Identification of Boundary Condition in the Inverse Stefan Problem 3

Clonal Selection Algorithm in Identification of Boundary Con...

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3rd International Conference on Man-Machine Interactions (ICMMI)

作者： Hetmaniok, Edyta Slota, Damian Silesian Tech Univ Inst Math PL-44100 Gliwice Poland

ISBN: (纸本)9783319023090

In the paper a procedure for identifying the heat transfer coefficient in boundary condition of the inverse Stefan problem is presented. Stefan problem is a boundary value problem describing thermal processes with the change of phase and the proposed procedure is based on the clonal selection algorithm belonging to the group of immune algorithms.

关键词： artificial intelligence immune algorithm clonal selection algorithm inverse stefan problem

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Generative tracking of 3D human motion in latent space by sequential clonal selection algorithm

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MULTIMEDIA TOOLS AND APPLICATIONS 2014年第1期69卷 79-109页

作者： Li, Yi Sun, Zhengxing Nanjing Univ State Key Lab Novel Software Technol Nanjing 210008 Jiangsu Peoples R China

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.

关键词： Human motion analysis Pose estimation Human motion tracking Manifold learning clonal selection algorithm

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A clonal selection algorithm model for daily rainfall data prediction

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WATER SCIENCE AND TECHNOLOGY 2014年第10期70卷 1641-1647页

作者： Rodi, N. S. Noor Malek, M. A. Ismail, Amelia Ritahani Ting, Sie Chun Tang, Chao-Wei Univ Tenaga Nas Dept Civil Engn Kajang 43000 Selangor Malaysia Int Islamic Univ Malaysia Kulliyyah Informat & Commun Technol Dept Comp Sci Kuala Lumpur 50728 Malaysia Cheng Shiu Univ Dept Civil Engn & Geomat Kaohsiung Taiwan

This study applies the clonal selection algorithm (CSA) in an artificial immune system (AIS) as an alternative method to predicting future rainfall data. The stochastic and the artificial neural network techniques are commonly used in hydrology. However, in this study a novel technique for forecasting rainfall was established. Results from this study have proven that the theory of biological immune systems could be technically applied to time series data. Biological immune systems are nonlinear and chaotic in nature similar to the daily rainfall data. This study discovered that the proposed CSA was able to predict the daily rainfall data with an accuracy of 90% during the model training stage. In the testing stage, the results showed that an accuracy between the actual and the generated data was within the range of 75 to 92%. Thus, the CSA approach shows a new method in rainfall data prediction.

关键词： artificial immune system clonal selection algorithm daily rainfall prediction

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