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Efficient multi-objective genetic algorithm for hardware-software partitioning in embedded system design: ENGA

INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2009年第3-4期36卷 181-190页

作者： Jagadeeswari, M. Bhuvaneswari, M. C. Sri Ramakrishna Engn Coll Dept Elect & Commun Engn Coimbatore 641022 Tamil Nadu India P S G Coll Technol Dept Elect & Elect Engn Coimbatore 641004 Tamil Nadu India

This paper presents a novel multi-objective evolutionary algorithm for hardware software partitioning of embedded systems. Customised genetic algorithms have been effectively used for solving complex optimisation problems (NP Hard) but are mainly applied to optimise a particular solution with respect to a single objective. Many real world problems in embedded systems have multiple objective functions like area, performance, power, latency, etc., which are to be maximised or minimised at the early stage of the design process. Hence multi- objective formulations are realistic models for many complex engineering optimisation problems. A multi-objective optimisation problem usually has a set of Pareto-optimal solutions, instead of one single optimal solution. A method is put forward for generating Pareto solutions using elitist non-dominated sorting genetic algorithm (ENGA) whose complexity is only O(MN2), where M is the number of objectives and N is the population size. The algorithm is implemented using Visual C++ and the performance metrics for weighted-sum genetic algorithm (WSGA) and ENGA are compared. The results of extensive hardware/software partitioning technique on numerous benchmarks are also presented which can be used practically at the early stage of the design process. From the simulation results ENGA (NSGA-II) was found to perform better than WSGA.

关键词： hardware-software partitioning embedded systems multi-objective genetic algorithm elitist non-dominated sorting genetic algorithm ENGA (NSGA-II) Pareto-optimal solutions

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Workspace optimization of parallel robot by using multi-objective genetic algorithm

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High Technology Letters 2022年第4期28卷 411-417页

作者： WANG Jinhong LEI Jingtao School of Mechatronic Engineering and Automation Shanghai UniversityShanghai 200444P.R.China Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai 200444P.R.China

For the narrow workspace problem of the universal-prismatic-universal(UPU)parallel robotwith fixed orientation,a kind of multi-objective genetic algorithm is studied to optimize the robot’*** concept of the effective workspace and its solution method are *** effectiveworkspace height(EWH)and global condition number index(GCI)of Jacobi matrix are selected asthe optimized objective *** the robot in two different orientations,the geometric pa-rameters are optimized by the multi-objective genetic algorithm named non-dominated sorting geneticalgorithm II(NSGA-II),and a set of structural parameters is *** optimization results areverified by four indicators with the robot’s moving platform at different *** resultsshow that,after optimization,the fixed-orientation workspace volume,the effective workspace heightand the effective workspace volume increase by 32.4%,17.8%and 72.9%on average,*** decreases by 6.8%on average.

关键词： parallel robot multi-objective genetic algorithm workspace optimization

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Artificial neural network-based intrusion detection system using multi-objective genetic algorithm

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INTERNATIONAL JOURNAL OF INFORMATION AND COMPUTER SECURITY 2023年第3-4期21卷 320-335页

作者： Patel, N. D. Mehtre, B. M. Wankar, Rajeev Inst Dev & Res Banking Technol IDRBT Ctr Excellence Cyber Secur Hyderabad India Univ Hyderabad UoH Sch Comp & Informat Sci SCIS Hyderabad India

With recent advances in cyber-attacks, traditional rule-based intrusion detection systems are not adequate to meet the present-day challenge. Recently machine learning-based intrusion detection system (IDS) has been proposed to detect such advanced/unknown cyber-attacks. The performance of such machine learning-based IDS largely depends upon the feature set used. Generally, using more features increases the accuracy of attack detection and increases detection time. This paper proposes a new network intrusion detection system based on an artificial neural network (ANN), which uses a multi-objective genetic algorithm to satisfy the requirements: accuracy of attack detection and faster response. The performance of the proposed method is tested by using the KDD'99, NSL-KDD, and CIC-IDS-2017 datasets. The results show that the performance of the proposed method is better than the existing methods. Besides, the new process provides a trade-off on the number of features used vs. accuracy and time for detection.

关键词： intrusion detection system IDS advanced persistent threat KDD'99 NSL-KDD CIC-IDS-2017 feature selection artificial neural network ANN multi-objective genetic algorithm

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A Hybrid multi-objective genetic algorithm for Bandwidth multi-Coloring Problem

A Hybrid Multi-Objective Genetic Algorithm for Bandwidth Mul...

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12th International Conference on Hybrid Intelligent Systems (HIS)

作者： Bayindir, Ismail Ugur Mercan, Engin Korkmaz, Emin Erkan Yeditepe Univ Dept Comp Engn Istanbul Turkey

ISBN: (纸本)9781467351157;9781467351164

Standard genetic algorithm (GA) yields poor performance on the Graph Coloring Problem (GCP) and its variants. This paper presents a multi-objective genetic algorithm (MOGA) for Bandwidth multi-Coloring Problem (BMCP). The problem is a generalization of GCP. In the proposed method, genetic operations are replaced with new ones which suit better to the structure of the problem. Performance of this MOGA framework is further boosted by hybridizing it with a Local Search (LS) algorithm. The aim of this hybrid approach is to increase the variety within the population through the genetic operations and to improve those individuals further by using LS. Several tests were conducted on a collection of benchmarks from GEOM series and promising results are obtained.

关键词： Graph Coloring Problem Bandwidth multi-Coloring Problem Hybrid genetic algorithm multi-objective genetic algorithm

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An Interactive Evaluation Method of Decentralized Procurement Plan by multi-objective genetic algorithm 10

An Interactive Evaluation Method of Decentralized Procuremen...

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10th International Conference on E-Business (ICE-B)

作者： Kobayashi, Ryoji Mori, Masakatsu Samejima, Masaki Komoda, Norihisa Osaka Univ Grad Sch Informat Sci & Technol 2-1 Yamadaoka Suita Osaka Japan Hitachi Ltd Yokohama Res Lab Totsuka Ku Yokohama Kanagawa 2440817 Japan

ISBN: (纸本)9789897581281

This paper addresses evaluation of a decentralized procurement plan for the support of the discussion among decision-makers with considering a catastrophic disaster. For the evaluation of the decentralized procurement plan, we have formulated the decentralized procurement planning problem as 3-objective optimization problem. However, multiple-objective genetic algorithms (MOGA) to solve the problem take several minutes and display many Pareto solutions. We propose the interactive evaluation method of the decentralized procurement plan that is an expanded interactive MOGA (iMOGA) with loss evaluation simulator and solution selection by characteristics of the decentralized procurement plan. Experimental results show that the proposed method can allow the decision-makers to find their preference solutions with 38% fewer interactions than the basic iMOGA can.

关键词： Supply Chain Decentralized Procurement Plan multi-objective genetic algorithm Interactive Evaluation

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A multi-objective genetic algorithm Based on Simulated Annealing

A Multi-objective Genetic Algorithm Based on Simulated Annea...

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4th International Conference on multimedia Information Networking and Security (MINES)

作者： Tang Xin-hua Chang Xu Fang Zhi-feng Shandong Univ Polit Sci & Law Dept Informat Sci & Technol Jinan Peoples R China

ISBN: (纸本)9780769548524;9781467330930

Combined the characteristic of simulated annealing, we propose a multi-objective genetic algorithm based on simulated annealing. We take the advantage of simulated annealing, improve the traditional multi-objective genetic algorithm, and avoid the premature convergence of the algorithm. Experimental results show that the improved algorithm improve the solution efficiency of the traditional multi-objective genetic algorithm, and avoid the premature convergence of the algorithm effectively.

关键词： Simulated annealing algorithm multi-objective genetic algorithm genetic algorithm non-inferior solution

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multi-objective Optimization of Rolling Schedules for Tandem Hot Rolling Based on Opposition Learning multi-objective genetic algorithm

Multi-objective Optimization of Rolling Schedules for Tandem...

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第25届中国控制与决策会议

作者： Yong Li Xinhua Zhao Yu Wang Mingxu Ren School of Electrical Engineering Shenyang University of Technology Department of Automatic Control Shenyang Aerospace University

ISBN: (纸本)9781467355322

An opposition learning multi-objective genetic algorithm based multi-objective optimization approach of rolling schedules for tandem hot rolling is proposed. According to actual rolling process, the power distribution and rolling energy consumption are selected as cost functions. Then the multi-objective model of rolling schedules is established. The opposition learning multi-objective genetic algorithm is applied to the model. Result shows that the proposed method decreases the values of two objective functions simultaneously compared to actual rolling schedules. Furthermore the proposed approach archives better Pareto solution set of rolling schedules with less running time compared with NSGA II. The validity of the proposed approach is confirmed.

关键词： opposition based learning multi-objective genetic algorithm tandem hot rolling optimization of rolling schedules

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A novel multi-objective genetic algorithm approach to address class imbalance for disease diagnosis

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International Journal of Information Technology (Singapore) 2023年第2期15卷 1151-1166页

作者： Jain, Anju Ratnoo, Saroj Kumar, Dinesh CSE Department Guru Jambheshwar University of Science and Technology Haryana Hisar India Department of ECE Delhi Technological University (DTU) Shahbad Daulatpur Main Bawana Road New Delhi 42 India

The adverse consequences of class imbalance problem are prevalent while performing classification for disease diagnosis. Such classifiers predict most of the examples of negative class (instances with non-diseased label) correctly but fail to make correct predictions for the positive class examples (instances with diseased label). Since misclassification costs can be very high for a sensitive field like disease diagnosis, addressing the class imbalance issue becomes of utmost important. A number of sampling techniques have been applied for balancing data. However, these techniques reduce the overall accuracy of classifier models. This is due to the fact that there is an issue of trade-off between sensitivity and specificity of such classifiers. This paper first proposes a GA-based undersampling technique with a weighted fitness function to determine the trade-off between sensitivity and specificity followed by a multi-objective genetic algorithm (MOGA) approach to address the class imbalance problem for disease diagnosis. To determine the trade-off between sensitivity and specificity manually is an arduous task. The MOGA approach takes the two extreme training samples from Pareto optimal solutions, one optimally tuned with respect to sensitivity and the other one optimally tuned with respect to specificity on validation data. Two decision tree classification models are built based on these two training sets. The models are named as sensitivity prioritized model (SEPM) and specificity prioritized model (SPPM) respectively. These models are combined to make predictions on the test data. The results obtained through extensive experimentation confirm that the proposed multi-objective scheme makes correct predictions on minority class (SE) without compromising the correct prediction rate on the majority class (SP). © 2020, Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Class imbalance problem Classification for medical datasets related to disease diagnosis genetic algorithm multi-objective genetic algorithm

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MOSAT: Finding Safety Violations of Autonomous Driving Systems using multi-objective genetic algorithm 2022

MOSAT: Finding Safety Violations of Autonomous Driving Syste...

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30th ACM Joint European Software Engineering Conference / Symposium on the Foundations of Software Engineering (ESEC/FSE)

作者： Tian, Haoxiang Jiang, Yan Wu, Guoquan Yan, Jiren Wei, Jun Chen, Wei Li, Shuo Ye, Dan Univ Chinese Acad Sci State Key Lab Comp Sci ISCAS Beijing Peoples R China Univ Chinese Acad Sci Nanjing Coll Joint Lab Cyberspace Secur China Southern Power Grid Beijing Peoples R China Univ Chinese Acad Sci Chongqing Sch Beijing Peoples R China Univ Chinese Acad Sci Nanjing Coll Beijing Peoples R China Nanjing Inst Software Technol Nanjing Peoples R China

ISBN: (纸本)9781450394130

Autonomous Driving Systems (ADSs) are safety-critical systems, and safety violations of Autonomous Vehicles (AVs) in real traffic will cause huge losses. Therefore, ADSs must be fully tested before deployed on real world roads. Simulation testing is essential to find safety violations of ADS. This paper proposes MOSAT, a multi-objective search-based testing framework, which constructs diverse and adversarial driving environment to expose safety violations of ADSs. Specifically, based on atomic driving maneuvers, MOSAT introduces motif pattern, which describes a sequence of maneuvers that can challenge ADS effectively. MOSAT constructs test scenarios by atomic maneuvers and motif patterns, and uses multi-objective genetic algorithm to search for adversarial and diverse test scenarios. Moreover, in order to test the performance of ADS comprehensively during long-mile driving, we design a novel continuous simulation testing technique, which runs the scenarios generated by multiple parallel search processes alternately in the simulator and can continuously create different perturbations to ADS. We demonstrate MOSAT on an industrial-grade platform, Baidu Apollo, and the experimental results show that MOSAT can effectively generate safety-critical scenarios to crash ADSs and it exposes 11 distinct types of safety violations in a short period of time. It also outperforms state-of-the-art techniques by finding more 6 distinct safety violations on the same road.

关键词： Autonomous Driving System Safety Violation multi-objective genetic algorithm

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Research on a multi-objective genetic algorithm for rational Agent learning model

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International Conference on Information Technology for Manufacturing Systems (ITMS 2011)

作者： Li Ming Xiao Zhenhong Xie Zanfu Mo Xiaoyun Guangdong Polytech Normal Univ Coll Comp Sci Guangzhou Guangdong Peoples R China

ISBN: (纸本)9783037851494

As a software component which is capable of learning in an autonomous way, software agent should have the capability of learning in a dynamic environment. genetic algorithm has a wide perspective in the machine learning because of its unique characteristic (e.g. dynamic adaptability, self-organization, global convergence and robustness). But when applying GA to agent's dynamic learning model, it encounters a series of problem. In this paper, a Modifided multi-objective genetic algorithm(MMOGA) will be introduced to solve these problems. Finally, an Agent's Dynamic learning model based on a MMOGA which has the flexible dynamic learning capability, better global convergence and performance, will be introduced.

关键词： multi-Agent System agent technology multi-objective genetic algorithm Dynamic learning model

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