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A hybrid artificial immune optimization for high-dimensional feature selection

KNOWLEDGE-BASED SYSTEMS 2023年 260卷

作者： Zhu, Yongbin Li, Wenshan Li, Tao Sichuan Univ Sch Cyber Sci & Engn Chengdu 610065 Peoples R China Honghe Univ Engn Coll Mengzi 661199 Peoples R China

For high-dimensional data, the traditional feature selection method is slightly inadequate. At present, most of the existing hybrid search methods have problems of high computational cost and unsatisfac-tory feature reduction rate. In this paper, a hybrid feature selection method based on artificial immune algorithm optimization (HFSIA) is proposed to solve the feature reduction problem of high-dimensional data. This method combines the filter method with the metaheuristic-based search strategy more effectively. Inspired by biological research results, the method introduces a lethal mutation mechanism and a Cauchy mutation operator with adaptive adjustment factors to improve the search performance of the algorithm. In addition, this method introduces an adaptive adjustment factor in the population update stage to improve the problem of insufficient diversity of the original algorithm. The effective combination of these mechanisms enables the algorithm to obtain better search capability at a lower computational cost. Experimental comparisons with 23 state-of-the-art feature selection methods are conducted on 22 high-dimensional benchmark datasets. The results show that the computational cost of HFSIA is comparable to 5 classical feature selection methods known for their speed. Moreover, it achieves a higher average classification accuracy than 18 hybrid feature selection methods reported in the latest literature with the best feature reduction rate.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Feature selection artificial immune algorithm Hybrid search High-dimensional data Classification

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Comparative Study of Five Bio-Inspired Evolutionary Optimization Techniques

Comparative Study of Five Bio-Inspired Evolutionary Optimiza...

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World Congress on Nature and Biologically Inspired Computing

作者： Krishnanand, K. R. Nayak, Santanu Kumar Panigrahi, B. K. Rout, P. K. Silicon Inst Technol Elect & Elect Engn Bhubaneswar Orissa India Silicon Inst Technol Bhubaneswar Orissa India Indian Inst Technol Engn Elect Delhi India

ISBN: (纸本)9781424450534

Bio-inspired evolutionary algorithms are probabilistic search methods that simulate the natural biological evolution or the behaviour of biological entities. Such algorithms can be used to obtain near optimal solutions in optimization problems, for which traditional mathematical techniques may fail. This paper does a comparative study of results of five evolutionary algorithms: Genetic algorithm (GA), Particle Swarm Optimization (PSO) algorithm, artificial Bee Colony (ABC) algorithm, Invasive Weed Optimization (IWO) algorithm and artificial immune (AI) algorithm when applied to some standard benchmark multivariable functions.

关键词： Genetic algorithm Invasive Weed Optimization algorithm artificial immune algorithm artificial Bee Colony algorithm Particle Swarm Optimization algorithm

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PSO algorithm for a Single Machine Scheduling Problem with Batching in Chemical Industries

PSO Algorithm for a Single Machine Scheduling Problem with B...

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IEEE International Conference on Automation and Logistics

作者： Yan, Ping Tang, Lixin Northeastern Univ Logist Inst Liaoning Key Lab Mfg Syst & Logist Shenyang Peoples R China

ISBN: (纸本)9781424447947

In this paper, a single machine batch process scheduling problem (SBPSP) integrating hatching decision is investigated. The integration problem of batching and scheduling is to allocate the demands from different customer orders to sets of batches and schedule these batches such that the total weighted tardiness costs and the total set-up costs are minimized. This problem is formulated as a mixed-integer nonlinear programming model and thus provides a challenging area for metaheuristics. We propose a hybrid algorithm of particle swarm optimization (PSO) and artificial immune algorithm (AIA) to solve this problem. In the proposed algorithm, a novel particle solution representation is designed for representing a batching scheme for SBPSP and the AIA mechanism improves the diversity of the swarm. Computational experiments on randomly generated instances with different structures show the validity and effectiveness of the proposed algorithm.

关键词： Scheduling Batching MINLP Particle Swarm Optimization artificial immune algorithm

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Bacterial Foraging algorithm based parameter estimation of solar PV model

Bacterial Foraging Algorithm based parameter estimation of s...

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Annual International Conference on Emerging Research Areas (AICERA) / International Conference on Microelectronics, Communication and Renewable Energy (ICMiCR)

作者： Krishnakumar, Neeraja Venugopalan, Rini Rajasekar, N. VIT Univ Sch Elect Engn Vellore Tamil Nadu India

ISBN: (纸本)9781467351492;9781467351508

The abundance and non-polluting nature of solar energy has aroused the interest of many researchers. This worldwide attention of photovoltaic panels has led to the need of generating accurate model of photovoltaic module before proceeding to the installation part. However, the modeling of solar PV characteristics is difficult since the manufacturer's datasheet provides only four values such as V-mp, I-mp, V-oc, and I-sc. For the accurate modeling of photovoltaic panels, the precise estimation of model parameters at different environmental conditions is very essential. Optimization technique is a very powerful tool to obtain the solution of complex non-linear problems. In this paper, the application of the bacterial foraging algorithm (BFA) for the accurate extraction of model parameters has been discussed. A systematic evaluation and performance comparison of BFA with other optimization techniques such as Genetic algorithm, artificial immune System etc. has been done and the best computational technique is determined based on certain criteria such as accuracy, consistency, speed of convergence etc. The computed data is compared with experimental data and the results are validated using two photovoltaic modules of different nature (multicrystalline and thin film).

关键词： Bacterial Foraging algorithm Genetic algorithm artificial immune algorithm Photovoltaic module

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Stochastic Global Optimization Method for Solving Constrained Engineering Design Optimization Problems

Stochastic Global Optimization Method for Solving Constraine...

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6th International Conference on Genetic and Evolutionary Computing (ICGEC)

作者： Wu, Jui-Yu Lunghwa Univ Sci & Technol Dept Business Adm Tao Yuan Taiwan

ISBN: (纸本)9780769547633;9781467321389

This work presents a stochastic global optimization (SGO) approach, which integrates an artificial immune algorithm and a particle swarm optimization (AIA-PSO) approach. To solve constrained engineering design optimization problems (e.g., tension/compression string design and pressure vessel design problems), the proposed AIA-PSO algorithm uses a penalty function method to transform a constrained engineering design optimization problem to an unconstrained optimization problem. Based on an external AIA approach, the constriction coefficient, cognitive parameter, social parameter, penalty parameter and mutation probability of an internal PSO algorithm are optimized. Constrained engineering design optimization problems are then solved using the internal PSO algorithm. Moreover, numerical results obtained using the proposed AIA-PSO algorithm is compared with those of published individual genetic algorithm (GA) with AIA methods and hybrid algorithms. Experimental results indicate that the optimum parameter settings of the internal PSO algorithm can be obtained using the external AIA approach. Also, the proposed AIA-PSO algorithm performs significantly better than those of some published individual GA with AIA approaches and hybrid algorithms for solving the pressure vessel design problem. Therefore, the proposed AIA-PSO algorithm can be considered as a promising SGO approach for solving constrained engineering design optimization problems.

关键词： stochastic global optimization artificial immune algorithm particle swarm optimization constrained engineering design optimization

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A New immune Genetic algorithm

A New Immune Genetic Algorithm

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2nd International Conference on Computer and Automation Engineering (ICCAE)

作者： Lu, Yan Wu, Xiangting Dai, Ran Xia, Guanglei Shandong Univ Sci & Technol Coll Informat Sci & Engn Qingdao Peoples R China

ISBN: (纸本)9781424455690

The application of genetic algorithm is widely, but it is easy to premature convergence and is inadequate about the local searching optimization ability. In this paper, a new immune genetic algorithm (IGA) is proposed. Experiments are done to compare the proposed algorithm with the standard GA, and the results indicate that the proposed IGA's optimization results and converging speed are superior and the proposed IGA overcomes the premature convergence and solves the problem of falling into local optimum solution easily. This paper also combines TSP's encoding characteristics to propose a new crossover operator (DEGX). Experiments shows the DEGX can enhance the local search ability greatly.

关键词： genetic algorithm immune genetic algorithm artificial immune algorithm vaccine

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Optimal Implementation of Feeder Automation in Medium Voltage Distribution Networks 20

Optimal Implementation of Feeder Automation in Medium Voltag...

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20th Electrical Power Distribution Conference (EPDC)

作者： Koozehkanani, Sanaz Salemi, Saeid Sadr, Shahab Nick Andishan Energy Co Maham Shargh Grp Mashhad Iran

ISBN: (纸本)9781467366120

The distribution automation studies include intelligent local automation arrangements. The profitability of the new technique was evaluated by comparing the costs of the automation investments to the savings obtained in the economic impact of interruptions. Feeder Automation reduces the restoration time and outage time of the system after a fault and increases the reliability of the system. In this study, the objective function is to achieve the lowest installation cost of the feeder terminal units versus the benefit obtained by decreasing the outage time.

关键词： Distribution Automation Feeder Automation Optimization artificial immune algorithm Energy Not Supplied

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A Hybrid Particle Swarm Optimization algorithm Based on immune Selection for Stochastic Loader Problem

A Hybrid Particle Swarm Optimization Algorithm Based on Immu...

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International Conference on Mechatronics and Industrial Informatics (ICMII 2013)

作者： Wang, Hong Zhao, Peiyi Shandong Inst Light Ind Coll Finance Jinan Shandong Peoples R China Shandong Prov Acad Educ Recruitment & Examinat Jinan Shandong Peoples R China

ISBN: (纸本)9783037856949

The Particle Swarm Optimization algorithm is a new computational method for the combinatorial optimization problem, it is simple and effective, but it does suffer from the premature convergence. For overcome this problem and finding the optimal solution of the Stochastic Loader problem, we presented a new hybrid Particle Swarm Optimization algorithm that combines with artificial immune algorithm, such as immune memory, antibody promotion and suppression, immune selection and so on. Numerical example illustrates the higher efficiency and reliability of the new hybrid PSO compared with the basic Particle Swarm Optimization algorithm.

关键词： stochastic loader problem particle swarm optimization artificial immune algorithm

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PID control parameters optimize based on an immune fruit fly optimization algorithm 28

PID control parameters optimize based on an immune fruit fly...

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28th Chinese Control and Decision Conference

作者： Song, Juan Pan, Huan Ningxia Univ Sch Phys & Elect Informat Sci Ningxia Key Lab Intelligent Sensing Desert Inform Yinchuan 750021 Peoples R China

ISBN: (纸本)9781467397148

The traditional Ziegler-Nichols (Z-N) method usually fails to achieve the best control performance for tuning PID parameters. Thus, this paper proposed an immune fly fruit optimization algorithm (IFOA) with the error performance criterion of ITAE as fitness function for the PID parameters optimized. Firstly, the proposed algorithm selected the best fruit flies for immune vaccines in the osphresis search mode. Then, it introduced the immune vaccination and immune selection mechanism in the visual search mode, so as to avoid fly fruit optimization algorithm (FOA) falling into premature, and to overcome the artificial immune algorithm (AIA) shortcomings in the cumbersome and inefficient calculations. Finally, test the performance of the hybrid algorithm with four benchmarks, and apply it in PID parameters tuning. Simulation results show that the IFOA has fast convergence, good stability and higher precision, and also prove the feasibility and effectiveness in PID control parameter optimization.

关键词： Fruit fly optimization algorithm artificial immune algorithm PID controller Parameters optimization

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Sound Insulation Inversion to an Attached Room in Buildings

Sound Insulation Inversion to an Attached Room in Buildings

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International Conference on Civil, Architectural and Hydraulic Engineering (ICCAHE 2012)

作者： Huang Xianfeng Lu Yimin Guangxi Univ Key Lab Disaster Prevent & Struct Safety Minist Educ Nanning 530004 Guangxi Peoples R China Guangxi Univ Coll Elect Engn Nanning Peoples R China

ISBN: (纸本)9783037854853

With respect to the higher sound insulation need of the buildings, the attached room may be considered to adopt. A calculation model is applied to predict the sound insulation of a specified attached room. Realistic sound insulation inversion, furthermore, is consistent with the procedure of engineering practice. By the artificial immune algorithm (AIA), the inverse sound insulation prediction model is developed, which adjust the insulation of each element (wall, door and window etc.) and sound absorption of the attached room. Under agreeing with the reasonable configuration of an attached building and meeting sound insulation requirement simultaneously, it was found that acoustic properties of each member within a whole attached room were obtained. As a consequence, the material and even the configuration of each wall can also be determined. It will be beneficial to building design.

关键词： attached room inverse sound insulation artificial immune algorithm

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