LOG FILE TEMPLATE DETECTION AS A MULTI-OBJECTIVE OPTIMIZATION PROBLEM

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Optimization Problem ◽  
Optimal Solution ◽  
Search Space ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Detection Tool ◽  
Detection Approach ◽  
Optimal Solution Set ◽  
Log File ◽  
Chicken Swarm Optimization

There is a need for automatic log file template detection tool to find out all the log messages through search space. On the other hand, the template detection tool should cope with two constraints: (i) it could not be too general and (ii) it could not be too specific These constraints are, contradict to one another and can be considered as a multi-objective optimization problem. Thus, a novel multi-objective optimization based log-file template detection approach named LTD-MO is proposed in this paper. It uses a new multi-objective based swarm intelligence algorithm called chicken swarm optimization for solving the hard optimization issue. Moreover, it analyzes all templates in the search space and selects a Pareto front optimal solution set for multi-objective compensation. The proposed approach is implemented and evaluated on eight publicly available benchmark log datasets. The empirical analysis shows LTD-MO detects large number of appropriate templates by significantly outperforming the existing techniques on all datasets.

LOG FILE TEMPLATE DETECTION AS A MULTI-OBJECTIVE OPTIMIZATION PROBLEM

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Optimization Problem ◽  
Optimal Solution ◽  
Search Space ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Detection Tool ◽  
Detection Approach ◽  
Optimal Solution Set ◽  
Log File ◽  
Chicken Swarm Optimization

There is a need for automatic log file template detection tool to find out all the log messages through search space. On the other hand, the template detection tool should cope with two constraints: (i) it could not be too general and (ii) it could not be too specific These constraints are, contradict to one another and can be considered as a multi-objective optimization problem. Thus, a novel multi-objective optimization based log-file template detection approach named LTD-MO is proposed in this paper. It uses a new multi-objective based swarm intelligence algorithm called chicken swarm optimization for solving the hard optimization issue. Moreover, it analyzes all templates in the search space and selects a Pareto front optimal solution set for multi-objective compensation. The proposed approach is implemented and evaluated on eight publicly available benchmark log datasets. The empirical analysis shows LTD-MO detects large number of appropriate templates by significantly outperforming the existing techniques on all datasets.

scholarly journals Multi-objective optimization of multi-microgrid power dispatch under uncertainties using interval optimization

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Shungen Luo ◽  
Xiuping Guo
Keyword(s):  
Optimization Problem ◽  
Operating Cost ◽  
Optimal Solution ◽  
Renewable Energies ◽  
Energy Utilization ◽  
Pollutant Emission ◽  
Interval Optimization ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Optimal Solution Set

<p style='text-indent:20px;'>The microgrid technology, which can dispatch power independently, is an effective way to increase the efficiency of energy utilization meanwhile develop and utilize the clean and renewable energy. However, the power generation of a single microgrid is unstable, because it is greatly affected by the external environment. Therefore, the development and application of the multi-microgrid system have gradually drawn various countries' attention. In order to minimize the operating cost and gaseous pollutant emission of the multi-microgrid system, which is composed of renewable energies and electric vehicles and so on, this paper builds a 24 hours day-ahead multi-objective complex constrained optimization model, using interval optimization to handle uncertainties of renewable energies. In view of the model characteristics, the metaheuristic strategies about initialization and repair of solution are designed. Furthermore, the fuzzy membership degree and Chebyshev function are used in parallel to decompose the multi-objective optimization problem, thus a multi-objective evolutionary algorithm based on hybrid decomposition (MOEA/HD) is constructed. Finally, the effectiveness of the metaheuristic strategies can be verified by analyzing the simulation results in this paper. Moreover, the results prove that the MOEA/HD is more efficient, which can get a higher-quality Pareto optimal solution set when compared to other algorithms.</p>

Multi-Objective Optimization Calibration of Spark Advanced Angle Based on NSGA-II Algorithm

2014 ◽  
Vol 543-547 ◽  
pp. 1959-1962
Author(s):  
Hao Ba ◽  
Bao Mei Qiu ◽  
Pei Pei Chen
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Gasoline Engine ◽  
Optimal Solution ◽  
Local Optimum ◽  
Multi Objective Optimization ◽  
Improved Genetic Algorithm ◽  
Nsga Ii ◽  
Multi Objective ◽  
Optimal Solution Set

Modern gasoline engine spark advanced angle calibration is a multi-objective optimization problem, commonly used genetic algorithm to solve this problem. However, the traditional genetic algorithm tends to local optimum probability of a larger, easy to fall into premature, this defect is likely to cause the solution is not the optimal solution set. To address this issue, the non-dominated sorting genetic algorithm II for the spark advanced angle optimization, through crowding distance maintain the diversity, overcome super individuals overgrowth, improved genetic algorithm post search results. Experimental results show the effectiveness of this method.

A multi-objective evolutionary algorithm based on niche selection in solving irregular Pareto fronts

2021 ◽  
pp. 1-21
Author(s):  
Xin Li ◽  
Xiaoli Li ◽  
Kang Wang
Keyword(s):  
Evolutionary Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Benchmark Problems ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Environmental Selection ◽  
Optimal Solution Set ◽  
Pareto Fronts

The key characteristic of multi-objective evolutionary algorithm is that it can find a good approximate multi-objective optimal solution set when solving multi-objective optimization problems(MOPs). However, most multi-objective evolutionary algorithms perform well on regular multi-objective optimization problems, but their performance on irregular fronts deteriorates. In order to remedy this issue, this paper studies the existing algorithms and proposes a multi-objective evolutionary based on niche selection to deal with irregular Pareto fronts. In this paper, the crowding degree is calculated by the niche method in the process of selecting parents when the non-dominated solutions converge to the first front, which improves the the quality of offspring solutions and which is beneficial to local search. In addition, niche selection is adopted into the process of environmental selection through considering the number and the location of the individuals in its niche radius, which improve the diversity of population. Finally, experimental results on 23 benchmark problems including MaF and IMOP show that the proposed algorithm exhibits better performance than the compared MOEAs.

An Interactive Neural Network for Constrained Multi-Objective Optimization with Application to the Design of Digital Filters

2012 ◽  
Vol 433-440 ◽  
pp. 2808-2816
Author(s):  
Jian Jin Zheng ◽  
You Shen Xia
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computational Complexity ◽  
Digital Filters ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Single Objective

This paper presents a new interactive neural network for solving constrained multi-objective optimization problems. The constrained multi-objective optimization problem is reformulated into two constrained single objective optimization problems and two neural networks are designed to obtain the optimal weight and the optimal solution of the two optimization problems respectively. The proposed algorithm has a low computational complexity and is easy to be implemented. Moreover, the proposed algorithm is well applied to the design of digital filters. Computed results illustrate the good performance of the proposed algorithm.

An Improved Adaptive Evolutionary Algorithm for Multi-Objective Optimization

2013 ◽  
Vol 303-306 ◽  
pp. 1494-1500
Author(s):  
Jian Wei Wang ◽  
Jian Ming Zhang
Keyword(s):  
Evolutionary Algorithm ◽  
Optimal Solution ◽  
Optimization Methods ◽  
Relative Fitness ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Fitness Value ◽  
Crossover Probability ◽  
Optimal Solution Set ◽  
Adaptive Evolutionary Algorithms

Aiming at effectively overcoming the disadvantages of traditional evolutionary algorithm which converge slowly and easily run into local extremism, an improved adaptive evolutionary algorithms is proposed. Firstly, in order to choose the optimal objective fitness value from the population in every generation, the absolute and relative fitness are defined. Secondly, fuzzy technique is adopted to adjust the weights of objective functions, crossover probability, mutation probability, crossover positions and mutation positions during the iterative process. Finally, three classical test functions are given to illustrate the validity of improved adaptive evolutionary algorithm, simulation results show that the diversity and practicability of the optimal solution set are better by using the proposed method than other multi-objective optimization methods.

Application of Genetic Algorithm in Hydraulic Calculation of Capillary Air-Conditioning System

2013 ◽  
Vol 291-294 ◽  
pp. 1884-1886
Author(s):  
Yong An Li ◽  
Xin Yang Cui ◽  
Xue Lai Liu ◽  
Chuan Yang Zhao ◽  
Zhi Xin Liu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Air Conditioning ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Frictional Resistance ◽  
Specific Model ◽  
Hydraulic Calculation ◽  
Multi Objective Optimization ◽  
Air Conditioning System ◽  
Multi Objective

This paper briefly introduced the design and operation features of capillary air conditioning system, put forward multi-objective optimization problem in hydraulic calculation process, using genetic algorithm to solve multi-objective optimization problem and established a capillary air-conditioning system hydraulic calculation model. The problems of hydraulic calculation were transformed into the problems of multi-objective optimization Pareto optimal solution using genetic algorithm (GA). And GA was applied to illustrate corresponding to specific model. The optimal specific frictional resistance under the different flow conditions was described here.

scholarly journals Apple Image Recognition Multi-Objective Method Based on the Adaptive Harmony Search Algorithm with Simulation and Creation

Information ◽  
2018 ◽  
Vol 9 (7) ◽  
pp. 180 ◽  
Author(s):  
Liqun Liu ◽  
Jiuyuan Huo
Keyword(s):  
Image Recognition ◽  
Optimization Problem ◽  
Search Algorithm ◽  
Harmony Search ◽  
Search Space ◽  
Harmony Search Algorithm ◽  
Natural Scene ◽  
Multi Objective Optimization ◽  
Recognition Method ◽  
Multi Objective

Aiming at the low recognition effect of apple images captured in a natural scene, and the problem that the OTSU algorithm has a single threshold, lack of adaptability, easily caused noise interference, and over-segmentation, an apple image recognition multi-objective method based on the adaptive harmony search algorithm with simulation and creation is proposed in this paper. The new adaptive harmony search algorithm with simulation and creation expands the search space to maintain the diversity of the solution and accelerates the convergence of the algorithm. In the search process, the harmony tone simulation operator is used to make each harmony tone evolve towards the optimal harmony individual direction to ensure the global search ability of the algorithm. Despite no improvement in the evolution, the harmony tone creation operator is used to make each harmony tone to stay away from the current optimal harmony individual for extending the search space to maintain the diversity of solutions. The adaptive factor of the harmony tone was used to restrain random searching of the two operators to accelerate the convergence ability of the algorithm. The multi-objective optimization recognition method transforms the apple image recognition problem collected in the natural scene into a multi-objective optimization problem, and uses the new adaptive harmony search algorithm with simulation and creation as the image threshold search strategy. The maximum class variance and maximum entropy are chosen as the objective functions of the multi-objective optimization problem. Compared with HS, HIS, GHS, and SGHS algorithms, the experimental results showed that the improved algorithm has higher a convergence speed and accuracy, and maintains optimal performance in high-dimensional, large-scale harmony memory. The proposed multi-objective optimization recognition method obtains a set of non-dominated threshold solution sets, which is more flexible than the OTSU algorithm in the opportunity of threshold selection. The selected threshold has better adaptive characteristics and has good image segmentation results.

A Special Multiobjective Evolutionary Algorithm for Solving Complicated Nonlinear Equations Systems

2014 ◽  
Vol 701-702 ◽  
pp. 18-23
Author(s):  
Chun An Liu
Keyword(s):  
Evolutionary Algorithm ◽  
Nonlinear Equations ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Optimization Methods ◽  
Multi Objective Optimization ◽  
Information Industry ◽  
Multi Objective ◽  
Application Fields ◽  
Industry Network

It is well known that nonlinear equations systems (NESS) is a subclass of nonlinear optimization problem, it exists in many application fields, such as information industry, network design, mechanics and robotics, etc.. How to design feasible and effective optimization methods to obtain the optimal solution or satisfied precision requirement’s optimal solution for complicated NESS is very important in computation fields. In this paper, each nonlinear sub-equation of NESS is approximately regarded as a sub-objective function of multi-objective optimization problem, then the original nonlinear equations systems is transformed into a multi-objective optimization problem, and the equivalence relation of the solution between the original NESS and the transformed multi-objective optimization problem is given. In order to effectively solve the nonlinear equations systems, a self-adaptive levy mutation operation is proposed, and a multi-objective optimization evolutionary algorithm to solve the nonlinear equations systems was designed. Computer simulations demonstrate the proposed algorithm can not only increase the diversity of evolutionary population but also make the evolution population quickly to approach the optimal solution or satisfied precision requirement’s optimal solution.

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