A multi-objective artificial immune algorithm for parameter optimization in support vector machine

2011 ◽  
Vol 11 (1) ◽  
pp. 120-129 ◽  
Author(s):  
Ilhan Aydin ◽  
Mehmet Karakose ◽  
Erhan Akin
Keyword(s):  
Support Vector Machine ◽  
Parameter Optimization ◽  
Immune Algorithm ◽  
Support Vector ◽  
Artificial Immune ◽  
Artificial Immune Algorithm ◽  
Multi Objective

scholarly journals Regional Integrated Energy Site Layout Optimization Based on Improved Artificial Immune Algorithm

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4381
Author(s):  
Yan Xu ◽  
Jianhao Zhang
Keyword(s):  
Decision Making ◽  
Load Capacity ◽  
Layout Optimization ◽  
Immune Algorithm ◽  
Initial Population ◽  
Artificial Immune ◽  
Weighting Method ◽  
Artificial Immune Algorithm ◽  
Multi Objective ◽  
Site Layout

Regional integrated energy site layout optimization involves multi-energy coupling, multi-data processing and multi-objective decision making, among other things. It is essentially a kind of non-convex multi-objective nonlinear programming problem, which is very difficult to solve by traditional methods. This paper proposes a decentralized optimization and comprehensive decision-making planning strategy and preprocesses the data information, so as to reduce the difficulty of solving the problem and improve operational efficiency. Three objective functions, namely the number of energy stations to be built, the coverage rate and the transmission load capacity of pipeline network, are constructed, normalized by linear weighting method, and solved by the improved p-median model to obtain the optimal value of comprehensive benefits. The artificial immune algorithm was improved from the three aspects of the initial population screening mechanism, population updating and bidirectional crossover-mutation, and its performance was preliminarily verified by test function. Finally, an improved artificial immune algorithm is used to solve and optimize the regional integrated energy site layout model. The results show that the strategies, models and methods presented in this paper are feasible and can meet the interest needs and planning objectives of different decision-makers.

Multi-objective artificial immune algorithm for fuzzy clustering based on multiple kernels

2019 ◽  
Vol 50 ◽  
pp. 100485 ◽  
Author(s):  
Ronghua Shang ◽  
Weitong Zhang ◽  
Feng Li ◽  
Licheng Jiao ◽  
Rustam Stolkin
Keyword(s):  
Fuzzy Clustering ◽  
Immune Algorithm ◽  
Artificial Immune ◽  
Artificial Immune Algorithm ◽  
Multi Objective

scholarly journals A Dynamic Health Assessment Approach for Shearer Based on Artificial Immune Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-12
Author(s):  
Zhongbin Wang ◽  
Xihua Xu ◽  
Lei Si ◽  
Rui Ji ◽  
Xinhua Liu ◽  
...  
Keyword(s):  
Production Efficiency ◽  
Health Assessment ◽  
Engineering Problem ◽  
Immune Algorithm ◽  
Assessment Model ◽  
Support Vector ◽  
Artificial Immune ◽  
Artificial Immune Algorithm ◽  
Assessment Approach ◽  
Automation Control

In order to accurately identify the dynamic health of shearer, reducing operating trouble and production accident of shearer and improving coal production efficiency further, a dynamic health assessment approach for shearer based on artificial immune algorithm was proposed. The key technologies such as system framework, selecting the indicators for shearer dynamic health assessment, and health assessment model were provided, and the flowchart of the proposed approach was designed. A simulation example, with an accuracy of 96%, based on the collected data from industrial production scene was provided. Furthermore, the comparison demonstrated that the proposed method exhibited higher classification accuracy than the classifiers based on back propagation-neural network (BP-NN) and support vector machine (SVM) methods. Finally, the proposed approach was applied in an engineering problem of shearer dynamic health assessment. The industrial application results showed that the paper research achievements could be used combining with shearer automation control system in fully mechanized coal face. The simulation and the application results indicated that the proposed method was feasible and outperforming others.

Parameter Matching Analysis of Hydraulic Hybrid Bergepanzers Based on RBF-Adaptive Artificial Immune Algorithm

2019 ◽  
Vol 34 (01) ◽  
pp. 2059002 ◽  
Author(s):  
Chuming Ning ◽  
Zhiqiang Chao ◽  
Huaying Li ◽  
Shousong Han
Keyword(s):  
Parameter Optimization ◽  
Optimal Parameter ◽  
Minimum Energy ◽  
High Energy ◽  
Immune Algorithm ◽  
Artificial Immune ◽  
Matching Problem ◽  
Artificial Immune Algorithm ◽  
Hydraulic Hybrid ◽  
Parameter Matching

Due to the multiple types of support tasks and high-energy efficiency equipment, energy saving research in a Bergepanzer has attracted much attention in recent years. A hydraulic hybrid system is an effective way for these problems of Bergepanzers. The initial problem is to optimize the parameter optimization-matching for a new hydraulic hybrid Bergepanzers (NHHB), which is a relatively complex and variable time-varying nonlinear system. In this paper, an RBF-adaptive artificial immune algorithm (RBF&AAIA) is presented to solve the parameter optimization-matching problem, and the performance of algorithm is analyzed. Finally, the RBF&AAIA is successfully used for the NHHB to optimize the parameters of key components for the minimum energy consumption of system, and the optimal parameter matching group is obtained.

A New Artificial Immune Algorithm for Flexible Job-Shop Scheduling

2010 ◽  
Vol 121-122 ◽  
pp. 266-270
Author(s):  
Lu Hong
Keyword(s):  
Job Shop ◽  
Job Shop Scheduling ◽  
Immune Algorithm ◽  
Initial Population ◽  
Clonal Selection Algorithm ◽  
Scheduling Problem ◽  
Artificial Immune ◽  
Artificial Immune Algorithm ◽  
Shop Scheduling ◽  
Multi Objective

Flexible job-sop scheduling problem (FJSP) is based on the classical job-shop scheduling problem (JSP). however, it is even harder than JSP because of the addition of machine selection process in FJSP. An improved artificial immune algorithm, which combines the stretching technique and clonal selection algorithm is proposed to solve the FJSP. The algorithm can keep workload balance among the machines, improve the quality of the initial population and accelerate the speed of the algorithm’s convergence. The details of implementation for the multi-objective FJSP and the corresponding computational experiments are reported. The results indicate that the proposed algorithm is an efficient approach for the multi-objective FJSP.

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