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Improved clonal selection algorithm based on the directional update strategy

JOURNAL OF SUPERCOMPUTING 2023年第17期79卷 19312-19331页

作者： Yang, Chao Huang, Zijing Jiang, Bibo Zhu, Menglian Luo, Aoran He, Jianfeng Hubei Univ Coll Comp & Informat Engn Youyi Ave Wuhan 430062 Hubei Province Peoples R China Hubei Prov Educ Informat Engn Technol Res Ctr Wuhan 430062 Hubei Province Peoples R China Social Sci Univ Hubei Prov Project Key Res Inst Humanities Res Ctr Informat Management Performance Evaluat Wuhan 430062 Hubei Province Peoples R China

At present, many cloning selection algorithms have been studied, and improvements have been made to the cloning, mutation and selection steps. However, there is a lack of research on the optimization of the updating operation steps. The clonal selection algorithm is traditionally updated through a random complement of antibodies, which is a blind and uncertain process. The added antibodies may gather near a local optimal solution, resulting in the need for more iterations to obtain the global optimal solution. To solve this problem, our improved algorithm introduces a crowding degree factor in the antibody updating stage to determine whether there is crowding between antibodies. By eliminating antibodies with high crowding potential and poor affinity, the improved algorithm guides the antibodies to update in the direction of the global optimal solution and ensures stable convergence with fewer iterations. Experimental results show that the overall performance of the improved algorithm is 1% higher than that of the clonal selection algorithm and 2.2% higher than that of the genetic algorithm, indicating that the improved algorithm is effective. The improved algorithm is also transplanted to other improved clonal selection algorithms, and the overall performance is improved by 0.97%, indicating that the improved algorithm can be a beneficial supplement to other improved clonal selection algorithms.

关键词： clonal selection algorithm Antibody update Targeted update strategy Antibody crowding Antibody affinity

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An adaptive clonal selection algorithm with multiple differential evolution strategies

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INFORMATION SCIENCES 2022年 604卷 142-169页

作者： Wang, Yi Li, Tao Liu, Xiaojie Yao, Jian Hunan Univ Sci & Technol Sch Comp Sci & Engn Xiangtan 411201 Peoples R China Sichuan Univ Coll Cybersecur Chengdu 610065 Peoples R China

clonal selection algorithms have provided significant insights into numerical optimization problems. However, most mutation operators in conventional clonal selection algorithms have semi-blindness and lack an effective guidance mechanism, which has thus become one of the important factors restricting the performance of algorithms. To address these problems, this study develops an improved clonal selection algorithm called an adaptive clonal selection algorithm with multiple differential evolution strategies (ADECSA) with three features: (1) an adaptive mutation strategy pool based on its historical records of success is introduced to guide the immune response process effectively;(2) an adaptive population resizing method is adopted to speed up convergence;and (3) a premature convergence detection method and a stagnation detection method are proposed to alleviate premature convergence and stagnation problems in the evolution by enhancing the diversity of the population. Experimental results on a wide variety of benchmark functions demonstrate that our proposed method achieves better performance than both state-ofthe-art clonal selection algorithms and differential evolution algorithms. Especially in the comparisons with other clonal selection algorithms, our proposed method outperforms at least 23 out of 30 benchmark functions from the CEC2014 test suite. (c) 2022 Elsevier Inc. All rights reserved.

关键词： clonal selection algorithm Artificial immune system Mutation operator Differential evolution operator Optimization

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clonal selection algorithm based control for two-wheeled self-balancing mobile robot

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SIMULATION MODELLING PRACTICE AND THEORY 2022年 118卷

作者： Olmez, Yagmur Koca, Gonca Ozmen Akpolat, Zuhtu Hakan Firat Univ Fac Technol Dept Mechatron Engn TR-23119 Elazig Turkey Fatih Sultan Mehmet Vakif Univ Fac Engn Dept Elect & Elect Engn TR-34445 Istanbul Turkey

In this study, we propose adaptive control approaches based on optimization for a two-wheeled mobile robot. The mathematical model of the self-balancing vehicle based on the inverted pendulum principle, is derived from the Lagrange method and the vehicle model is simulated within MATLAB/Simulink environment. Five different optimization-based controller models (Model 1-5) are developed based on common control methods, which are the Pole Placement Design Method, PID, and LQR. Besides the stability control of the robot, speed control and trajectory tracking control are also provided with these models. While improving the optimizationbased controller models, the CSA, an artificial immune optimization methods, is adapted for use with the system. The benefits of the study are given as follows: 1) The balancing control and trajectory tracking control performance of the two-wheeled vehicle is improved using the proposed optimization-based control approach;2) A comprehensive study is introduced with different control models based on Pole Placement Design Method, PID, and LQR Method;3) Since parameter tuning of multiple PID controllers is challenging for a two-wheeled vehicle constructed by using the inverted pendulum principle, one of the proposed control models is developed using multiple PIDs. To alleviate the difficulties of parameter tuning for multiple PIDs, the CSA method is adapted for a self-balancing two-wheeled vehicle for the first time;4) To exhibit the optimization effect of the CSA, the simulation results are compared with five other optimization methods, namely PSO, ABC, DE, GoldSa-II, and CS;5) The effectiveness of the proposed optimization-based control approaches and optimization method are demonstrated through analyses and simulation studies. In this concept, it has been also analyzed effects of the external disturbances and uncertain parameters on the proposed control method.

关键词： Two-wheeled robot PID LQR Pole placement design clonal selection algorithm Optimization

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A two-stage clonal selection algorithm for local feature selection on high-dimensional data

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INFORMATION SCIENCES 2024年 677卷

作者： Wang, Yi Tian, Hao Li, Tao Liu, Xiaojie Hubei Univ Econ Sch Informat Engn Wuhan 430205 Peoples R China Sichuan Univ Coll Cybersecur Chengdu 610065 Peoples R China

Various evolutionary computation algorithms have shown their excellent performance for highdimensional feature selection (FS). However, most of current FS methods choose a global feature subset and ignore the correlations between feature subsets and sample subspaces, which limits the performance of methods in the sample space with different probability distributions. To solve the issue, we propose a two -stage clonal selection algorithm for filter -based local feature selection (TSCSA-LFS) that integrates symmetric uncertainty and a discrete clonal selection algorithm with three contributions. First, unlike conventional feature selection methods, TSCSA-LFS introduces local sample behaviors and assigns subsets of features for different sample regions. Second, an improved discrete clonal selection algorithm is developed for searching relevant features, which contains mutual information -based individual initialization, a differential evolution -based mutation strategy pool and a local search technique. Third, a two-part antibody representation is employed for automatical adjustment of the weight -related parameter. Our method shows the promising experimental results compared with well-known global FS and clonal selection -based local FS methods on fourteen high -dimensional datasets. For instance, our method can obviously outperform local FS, filter -based and hybrid methods on at least nine datasets

关键词： Local feature selection clonal selection algorithm Immune computation Evolutionary computation High-dimensional feature selection

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A discrete clonal selection algorithm for filter-based local feature selection

A discrete clonal selection algorithm for filter-based local...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Wang, Yi Li, Tao Liu, Xiaojie Yao, Jian Hunan Univ Sci & Technol Sch Comp Sci & Engn Xiangtan 411201 Peoples R China Sichuan Univ Coll Cybers Chengdu 610065 Peoples R China

ISBN: (数字)9781665467087

ISBN: (纸本)9781665467087

Feature selection algorithms aim to improve the performance of machine learning algorithms by removing irrelevant and redundant features. Various feature selection algorithms have been proposed, but most of them select a global feature subset for characterizing the entire sample space. In contrast, this study proposes an efficient discrete clonal selection algorithm for local feature selection called DCSA-LFS with three features: (1) local sample behaviors are considered, and a local clustering-based evaluation criterion is used to select a distinct optimized feature subset for each different sample region;(2) an improved discrete clonal selection algorithm is proposed, which uses a differential evolution-based mutation operator to enhance the search capability of clonal selection algorithms;and (3) a two-part antibody representation is adopted to automatically adjust the weight-related parameter. Experimental results on twelve UCI datasets show that DCSA-LFS is competitive with traditional filter-based feature selection algorithms and a clonal selection algorithm-based local feature selection algorithm.

关键词： local feature selection clonal selection algorithm artificial immune system dimensionality reduction

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Enhancing rainwater harvesting and groundwater recharge efficiency with multi-dimensional LSTM and clonal selection algorithm

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GROUNDWATER FOR SUSTAINABLE DEVELOPMENT 2024年 25卷

作者： Rao, N. Raghava Kiran, Sree Pokkuluri Amena, I. Tamboli Senthilkumar, A. Sivakumar, R. Kumar, M. Ashok Velusamy, Sampathkumar Inst Aeronaut Engn Dept Informat Technol Hyderabad 500043 India Shri Vishnu Engn Coll Women Dept Comp Sci & Engn Bhimavaram Andhra Pradesh India Deogiri Coll Engn & Management DIETMS Dept Civil Engn Aurangabad Maharashtra India Skyline Univ Nigeria Sch Sci & Informat Technol Kano Kano State Nigeria CHRIST Dept Stat & Data Sci Bangalore India Skyline Univ Nigeria Dept Comp Sci & Software Engn Kano Nigeria Kongu Engn Coll Dept Civil Engn Perundurai 638060 India

Rainwater harvesting stands out as a promising solution to alleviate water scarcity and alleviate pressure on conventional water reservoirs. This work introduces a pioneering strategy to elevate the efficiency of rainwater harvesting systems through the fusion of Multi-Dimensional Long Short-Term Memory (LSTM) networks and the clonal selection algorithm (CSA). The Multi-Dimensional LSTM networks serve to model intricate temporal and spatial rainfall patterns, enabling precise predictions regarding the optimal times and locations for rainwater abundance. This insight is pivotal in refining the design and operation of rainwater harvesting setups. Drawing inspiration from the immune system, the clonal selection algorithm is employed to optimize site selection and resource allocation, ensuring the maximal utilization of harvested rainwater. The adaptability and robustness of CSA prove invaluable in tackling the dynamic nature of rainfall patterns. This research endeavor is dedicated to enhancing groundwater levels and optimizing its sources through the implementation of efficient harvesting techniques. By delving into innovative methodologies, it aims to contribute significantly to sustainable water management practices and ensure a reliable supply of groundwater for various societal needs. The experiments are conducted to study the effectiveness of rainwater harvesting systems, where the proposed method achieves increased efficiency, thereby reducing dependence on conventional water sources and contributing to sustainable water management practices. The proposed CSA-LSTM model demonstrates superior performance compared to ACO-ANN and PSO-BPNN, achieving higher training, testing, and validation accuracies while exhibiting lower training, testing, and validation losses. Additionally, CSA-LSTM showcases excellent site suitability, high resource utilization, and robustness to changes, with a fast response time, emphasizing its potential for efficient and effective applicat

关键词： Rainwater harvesting clonal selection algorithm Sustainability Water management Resource optimization Water scarcity mitigation

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Dual carbon oriented optimization method for manufacturing industry chain based on BP neural network and clonal selection algorithm

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APPLIED SOFT COMPUTING 2023年 148卷

作者： Dou, Runliang Hou, Yanchao Wei, Yixin Liu, Jing Tianjin Univ Coll Management & Econ Tianjin 300072 Peoples R China Hebei Univ Technol Sch Comp Sci & Engn Tianjin 300401 Peoples R China Tianjin Univ Coll Management & Econ 92 Weijin Rd Tianjin 300072 Peoples R China

Global economic growth and increasing carbon emissions pose significant challenges to managing and opti-mizing industry chains. Against the backdrop of carbon peak and carbon neutrality goals, manufacturing en-terprises forgo economic efficiency to achieve low-carbon goals. Therefore, balancing the economic efficiency and carbon emissions of the entire industrial chain has become a vital issue for manufacturing enterprises. Based on the existing research, "a dual carbon industrial chain" is proposed in this study to reveal the characteristics of the industrial chain under the dual carbon goals. A collaborative optimization model under the "dual carbon industrial chain" was constructed by combining complex networks, back-propagation (BP) neural networks, and clonal selection algorithm to optimize the economic efficiency and carbon emissions of the entire industrial chain. Using a chip industry chain as an example, an optimization solution from the perspective of the overall industry chain is proposed. The results show that the proposed method can optimize the economic benefits and carbon emissions of the entire industrial chain, which has specific theoretical and practical significance for developing modern industrial chains with a dual carbon background.

关键词： Dual carbon Industrial chains Complex networks BP neural networks clonal selection algorithm

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CC-CSA: A culture&chaos-inspired clonal selection algorithm for abnormal detection

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第1期39卷 1289-1301页

作者： Xi, Liang Wang, Ruidong Zhang, Fengbin Sun, Yuezhongyi Harbin Univ Sci & Technol Sch Comp Sci & Technol Harbin Peoples R China

The clonal selection algorithm(CSA) is a core method in artificial immune system, which is famous for its intelligent evolution in artificial intelligence application. However, There are some shortcomings in the algorithm, such as local optima and low convergence speed, which make its practical effects not ideal. Culture algorithm(CA) is driven by knowledge, which can significantly improve the evolutionary efficiency. Chaos mechanism can make the algorithm have better problem space coverage ability. Therefore, a culture&chaos-inspired CSA(CC-CSA) is proposed in this paper to deal with the problems mentioned before. CC-CSA adopts the double-layer evolutionary framework of CA to extract knowledge and guide the crossover and chaotic mutation operation to complete the evolution process. The implicit knowledge is used to adaptively control the chaotic mutation scale, guide the individuals to jump out of the local optima, and realize the accurate search in the latter evolution cycle to gradually approach the optimal solution. It can be seen from the mathematical model analysis that CC-CSA can converge to the global optimal solution. Compared with the experimental results of the original CSA and its representative, up-to-date improved methods, CC-CSA has the fastest convergence speed and the best detection performances. It is also proved that CC-CSA can solve the problems of local optima and slow convergence speed by using the knowledge guidance of CA's double-layer framework and good coverage ability of chaos mechanism to the problem space.

关键词： Artificial immune system clonal selection algorithm culture algorithm chaos mechanism abnormal detection

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Adaptation of the clonal selection algorithm to the real-time coordinated Volt/VAr control through a software-in-the-loop strategy*

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ELECTRIC POWER SYSTEMS RESEARCH 2021年 194卷 107092-107092页

作者： Guerrero, Carlos A., V Silveira, Paulo M. Filho, Jose M. C. Univ Fed Itajuba Inst Elect Syst & Energy Av BPS 1303 Itajuba MG Brazil

With the advent of smart grid technologies, modern Distribution Management Systems (DMSs) enable the realtime operation of advanced automation functions such as Coordinated Volt/VAr Control (CVVC). Typically, control rule-based techniques have been proposed to efficiently carry out this important function in Distribution Networks (DNs). In this paper, a novel adaptation of the clonal selection theory of immune systems (clonalG clonal selection algorithm) is proposed for real-time optimal solution of CVVC problem in radial DNs composed of multiple control equipment. To do this, a Software-in-the-Loop (SIL) strategy is developed, through the use of the Real-Time Digital Simulator (RTDS) and Matlab? program. Implementation details of the referred strategy and the corresponding adaptation of the clonalG-based CVVC are discussed in this paper. Simulations results demonstrated that the proposed adaptation of the clonalG technique is suitable to solve the CVVC problem in a maximum time of less than 10 min, in accordance with the Brazilian electric utilities? need. On the other hand, the developed SIL strategy proved to be effective to analyzing the time domain behavior of CVVC algorithms, designed to operate in real time in a modern DMS.

关键词： Distribution management system Coordinated Volt VAr control Distribution networks clonal selection algorithm Real-Time digital simulator

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Complete receptor editing operation based on quantum clonal selection algorithm for optimization problems

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2019年第3期14卷 411-421页

作者： Yang, Yu Dai, Hongwei Gao, Shangce Wang, Yirui Jia, Dongbao Tang, Zheng Univ Toyama Fac Engn Toyama 9308555 Japan Huaihai Inst Technol Sch Comp Engn Lianyungang 222005 Jiangsu Peoples R China

clonal selection mechanism, which is the theoretical foundation of clonal selection algorithm (CSA) and its variants, was proposed for explaining the essential features of adaptive immune responses: adequate diversity, discrimination of self and nonself, and sustaining immunologic memory. On the basis of the clonal selection theory, only the high-affinity immune cells are chosen to proliferate. Those cells with low affinity must be efficiently eliminated. However, the ability of receptor editing to salvage low-affinity immune cells from deletion by changing their receptor specificity realized the clonal selection process anew. By combining clonal selection theory and receptor editing, a quantum clonal selection algorithm based on complete receptor editing operation is proposed for the traveling salesman problem (TSP) and the holes-machining-path-planning (HMPP) problem. Two receptor editing operators (inversion and deletion) work together to improve the performance of CSA. Furthermore, in order to overcome the drawback of asexual proliferation during the immune maturation process, a quantum interference crossover based on complete receptor editing operation is used. The effectiveness of the improved algorithm is evaluated on optimization problems including TSP and HMPP problems. The experimental results are also compared with those of other methods based on clonal selection theory. (c) 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： clonal selection algorithm complete receptor editing quantum interference crossover traveling salesman problem holes-machining-path-planning problem

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