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An improved multi-objective evolutionary optimization algorithm with inverse model for matching sensor ontologies

SOFT COMPUTING 2021年第18期25卷 12227-12240页

作者： Xue, Xingsi Jiang, Chao Wang, Haolin Tsai, Pei-Wei Mao, Guojun Zhu, Hai Fujian Univ Technol Fujian Prov Key Lab Big Data Min & Applicat Fuzhou 350118 Fujian Peoples R China Fujian Univ Technol Intelligent Informat Proc Res Ctr Fuzhou 350118 Fujian Peoples R China Fujian Univ Technol Sch Comp Sci & Math Fuzhou 350118 Fujian Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Automat Detecting Technol & Instr Guilin 541004 Guangxi Peoples R China Swinburne Univ Technol Dept Comp Sci & Software Engn John St Hawthorn Vic 3122 Australia Zhoukou Normal Univ Sch Network Engn Zhoukou 466001 Henan Peoples R China

To address the heterogeneity problem of sensor data, it is necessary to conduct the Sensor Ontology Matching (SOM) process to find the mappings among diverse sensor data with the same semantics connotation. Currently, many multi-objective evolutionary algorithms (MOEAs) have been used to match the ontologies, which aim at finding a set of solutions called Pareto Set (PS) in the Pareto Front (PF) to represent a set of trade-off proposals for different Decision Makers (DMs). Being inspired by the success of MOEA with Inverse Model (IM-MOEA) in solving complicated optimization problems, in this work, an Improved IM-MOEA (I-IM-MOEA)-based matching technique is further proposed to enhance the algorithm's matching efficiency as well as the alignment's quality. To overcome the drawback of IM-MOEA that has poor performance on irregular PF, an adjusted selection mechanism is employed to avert the massive reduction in non-domination solutions on irregular PF, a dynamic Reference Vectors (RVs) is used to decrease the computational resources and boost the efficiency of the algorithm, and a local search strategy is introduced to promote the results' quality. The experiment employs the benchmark provided by Ontology Alignment Evaluation Initiative (OAEI) and three sensor ontologies to assess the performance of I-IM-MOEA, and the experimental results show that I-IM-MOEA is both effective and efficient.

关键词： Sensor ontology matching multi-objective evolutionary algorithm Inverse modeling

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An effective hybrid multi objective evolutionary algorithm for solving real time event in flexible job shop scheduling problem

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MEASUREMENT 2018年 114卷 78-90页

作者： Reddy, M. B. S. Sreekara Ratnam, Ch. Rajyalakshmi, G. Manupati, V. K. KL Univ Dept Mech Engn Green Fields Vaddeswaram Andhra Pradesh India AUCE Dept Mech Engn Visakha Patnam Andhra Pradesh India VIT Univ Dept Mfg Engn Vellore Tamil Nadu India VIT Univ Dept Mech Engn Vellore Tamil Nadu India

This paper addresses the multi-objective model for a flexible job shop scheduling problem (FJSSP) to improve the system performance under the condition of machines break down as a real time event. It is important to identify the relevant performance measures to the mentioned problem for examining the system performance. Therefore, minimization of make span and minimization of total machine load variation is considered as two performance measures. Generally, it is very difficult to develop a mathematical model for the real-time situations in FJSSP. Hence, in this paper we divided the research work into two folds: Primarily, a mixed-integer non-linear programming (MINLP) model has been developed to represent the above-mentioned multi-objectives that subjected to constraints without considering machines break down. Secondarily, by incorporating the machines break down as the real-time event the performance of the system is examined. Solving conflicting objectives simultaneously for finding the optimal/near optimal solutions in a reasonable time is a challenge. In this paper, we proposed a new evolutionary based multi-objective teacher learning-based optimization algorithm (MOTLBO) to solve the above-mentioned complex problem. Moreover, to improve the obtained solutions a local search technique has been incorporated in the MOTLBO and comparisons has been made with existing multi-objective particle swarm optimization (MOPSO) and conventional non-dominated sorting genetic algorithm (CNSGA-II). Results found that the proposed multi-objective-based hybrid meta-heuristic algorithm produced high-quality solutions as proved by the tests we performed over a number of randomly generated test problems. Finally, comparisons also made with how the machines break down can affect the proposed systems performance.

关键词： Flexible job shop multi-objective evolutionary algorithm NP-hard Optimization

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Improving SVM Training Sample Selection Using multi-objective evolutionary algorithm and LSH

Improving SVM Training Sample Selection Using Multi-Objectiv...

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IEEE Symposium Series on Computational Intelligence

作者： Romaric Pighetti Denis Pallez Frederic Precioso I3S Lab. Univ. Nice Sophia-Antipolis Nice France

ISBN: (纸本)9781479975617

In this paper, we propose a new framework hybridizing a Support Vector Machine (SVM), a multi-objective Genetic algorithm (MOGA) and a Locality Sensitive Hashing (LSH). The goal is to tackle fine-grained classification challenges which means classifying many classes with high similarities between classes and poor similarities inside one class. SVM is used for its ability to learn multi-class problems from very few training data. MOGA is used to optimize training samples used by the SVM so as to improve its learning rate. As data define a discrete set of instances and not a continuous solution space, LSH is used to map "optimal solutions" obtained by MOGA onto the closest real instances contained in the dataset. We evaluate our method for content-based image classification on the standard image database Caltech256 (i.e. 30000 images distributed in 256 classes). Experiments show that our method outperforms state-of-the-art approaches.

关键词： HELLS gene Support Vector Network multi-objective evolutionary algorithm discrete set learning rate Image classification Dataset

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A multi-objective evolutionary algorithm Based on Cone Heart Point Adjustment Vectors to Solve Concave-convex Problems

A Multi-objective Evolutionary Algorithm Based on Cone Heart...

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第34届中国控制与决策会议

作者： Xin Li Xiaoli Li Kang Wang Faculty of Information Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Digital Community Ministry of Education

Decomposition-based multi-objective optimization aliorithms have been shown to be successful in solving multi-objective optimization problems(MOPs).Since the shape is unknown before the search,the classical decomposition method relies on a set of uniformly distributed reference vectors to divide the objective space into multiple ***,the the distribution of reference vectors produced by the Das and Dennis systematic approach is difficult to maintain the diversity of(extremely) concave and(extremely) convex *** remedy this issue,a multi-objective evolutionary algorithm based on cone heart point adjustment vectors is proposed to solve concave and convex problems(CHP-MOEA).In this paper,the concavity or convexity is first according to the intermediate objective ***,the distribution of reference vectors are adjusted according to the cone heart point on the basis of the traditional ***,experimental results on 31 benchmark problems show that the proposed algorithm exhibits better performance than other state-of-the-art algorithms.

关键词： Cone heart point Concavity or convexity multi-objective evolutionary algorithm

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multi-indicators multi-objective evolutionary algorithm with Q-Learning for Real-world Network Optimization

Multi-indicators Multi-objective Evolutionary Algorithm with...

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作者： Tung Truong THANH Rui WANG Jia-hua LI Lian-bo MA College of Software Northeastern University

Aiming at the deployment optimization of complex Internet of Things(IoT) systems, we propose a new multi-objective optimization algorithm using multiple indicators with reinforcement learning, called MIEA-RL. In MIEA-RL, a set of evaluation indicators are employed to guide the evolution of population, while a Q-learning method is designed to manage these indicators in an efficient way during the search. To be specific, each candidate indicator is determined by the performance improvement of the population selected by the current indicator. Moreover, the search biases of different indicators can be adaptively balanced according to a Q-learning table. Accordingly, the convergence and diversity can be maintained effectively while the algorithm complexity is not increased. Finally, the MIEA-RL is applied to resolve the real-world IoT optimization instances in the experiment. Results show the proposed algorithm is effective and efficient to handle with these problems.

关键词： multi-indicators multi-objective evolutionary algorithm Q-learning

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New search strategy for multi-objective evolutionary algorithm

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Heliyon 2024年第24期10卷 e40917页

作者： Yuejun, Liu Software School of Anyang Normal University Henan Anyang 455002 China Key Laboratory of Oracle Bone Inscriptions Information Processing Ministry of Education Henan Anyang 455002 China

To address the problem of low search efficiency of multi-objective evolutionary algorithm during iterations, we proposed a new idea which considering a single individual to generate better solutions in a single iteration as a starting point to improve the search performance of multi-objective evolutionary algorithm and designedthe neighbor strategy and guidance strategy based on this improved approach in this paper. We used our proposed new search strategy to improve NSGA-III algorithm(named as NSGA-III/NG) and MOEA/D algorithm(named as MOEA/D-NG). On ZDT, DTLZ and WFG public test sets, the NSGA-III/NG algorithm using the new search strategy was compared with NSGA-II algorithm, NSGA-III algorithm, ANSGA-III algorithm and NSGA-II/ARSBX algorithm. The MOEA/D-NG algorithm using the new search strategy was compared with MOEA/D algorithm, MOEA/D-CMA algorithm, MOEA/D-DE algorithm and CMOEA/D algorithm. Experimental results indicate that the performance of NSGA-III/NG algorithm using our search strategy is superior to NSGA-II, NSGA-III,ANSGA-III and NSGA-II/ARSBX algorithm and the performance of MOEA/D-NG algorithm using our search strategy is superior toMOEA/D, MOEA/D-CMA,MOEA/D-DE and CMOEA/D algorithm. Our proposed search strategy can improve the convergence speed of NSGA-III algorithm and MOEA/D algorithm by 12.54 %,the accuracy of the non dominated solution set by 3.67 %. This situation indicates that our search strategy could significantly improve the search capability of the multi-objective evolutionary algorithm. In addition, this strategy has excellent applicability and could be combined with mainstream multi-objective evolutionary algorithms. © 2024

关键词： Guidance strategy multi-objective evolutionary algorithm Neighbor strategy Search efficiency Search strategy

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A multi-objective Differential evolutionary algorithm Based on Spacial Distance

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3rd International Conference on Intelligence Computation and Applications

作者： Zheng, Jinhua Wu, Jun Lv, Hui Xiangtan Univ Inst Informat Engn Xiangtan 411105 Peoples R China

ISBN: (纸本)9783540921363

Differential Evolution (DE) is a relatively new EA and it has become a major branch of EA. Based on the classical Differential Evolution. we proposed a new multi-objective Differential evolutionary algorithm ill this paper. Compared with NSGA-II and epsilon-MOEA, the experimental results demonstrate that the new algorithm tends to be more effective in obtaining good convergence and can converge to the true Pareto front with comparable efficiency.

关键词： multi-objective evolutionary algorithm multi-objective optimal problem Differential evolutionary Spacial Distance

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Data Clustering Using multi-objective Hybrid evolutionary algorithm

Data Clustering Using Multi-Objective Hybrid Evolutionary Al...

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International Conference on Control, Automation and Systems

作者： Won, Jin-Myung Ullah, Sami Karray, Fakhreddine Voice Enabling Syst Technol Inc Waterloo ON Canada Dept Elect & Comp Engn Waterloo ON Canada Univ Waterloo Dept Elect & Comp Engn Waterloo ON Canada

ISBN: (纸本)9788995003893

This paper proposes a multi-objective evolution strategy (ES) hybridized with a k-means algorithm to address a data clustering problem whose objective is minimizing both clustering error and cluster number. Contrary to the conventional data clustering problem with a predetermined number of clusters, the bi-objective problem considered in this study has a set of clustering solutions whose cluster numbers are different from one another. This enables to secure the best clustering result that fits specific needs without restricting the cluster number. To find the solution set, the hybrid ES evolves a population of solution candidates each of which represents a variable number of cluster centroids. While evolving the population, special ES operators dedicated to the bi-objective clustering problem are used. Whenever the hybrid ES creates a new set of cluster centroids, it is fine-tuned by the k-means algorithm. The experiment results show that the hybrid ES outperforms the conventional ES and KMA.

关键词： Clustering multi-objective evolutionary algorithm k-means algorithm

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Hybrid escalating multi-objective evolutionary algorithm with its application to flow shop problems

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Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice 2006年第8期26卷 101-108页

作者： Shi, Rui-Feng Zhou, Hong Shangguan, Chun-Xia School of Economics and Management Beijing University of Aeronautics and Astronautics Beijing 100083 China

A hybrid escalating multi-objective evolutionary algorithm (HEMEA), which has a new evolution structure compared with the existing ones, was proposed in this paper. The new algorithm enhanced the efficiency of optimization by using an innovative escalating evolutionary scheme with an elitism selection and variable Pareto local search strategy. A series of bi-objective flow shop optimization problems from OR-Library and one typical tri-objective flow shop optimization problem which was first studied in Bagchi's work, were re-optimized by NSGA-II, MOGLS, ENGA and our HEMEA respectively. The comparison of the optimization results has shown the outstanding performance of HEMEA with respect to the others', which were well-known for their good performance in multi-objective evolutionary computation. thus, the effectiveness and efficiency of HEMEA were demonstrated.

关键词： Escalating evolutionary Flow shop Local search multi-objective evolutionary algorithm

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Ultra-Wideband Planar Antenna Optimized by a multi-objective evolutionary algorithm

Ultra-Wideband Planar Antenna Optimized by a Multi-Objective...

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2007 Asia Pacific Microwave Conference (APMC 2007), vol.3

作者： Xue-Song Yang Kim Fung Man Sai Ho Yeung Jia-Lin Li Bing-Zhong Wang 2 Institute of Applied Physics University of Electronic Science and Technology of China Chengdu China 1 Department of Electronic Engineering City University of Hong Kong Kowloon Hong Kong SAR China

An ultra-wideband (UWB) planar antenna optimized by a newly developed multi-objective evolutionary algorithm-^sJumping Genes Genetic algorithm (JGGA) is proposed. The optimization procedure aims at finding an antenna not only with low voltage standing wave ratio (VSWR) and small size, but also with stable radiation pattern over the wide band. There are five objective functions and they are based on VSWR, antenna size, radiation pattern and gain. Both the simulated and measured results are given and they agree well with each other.

关键词： multi-objective evolutionary algorithm Jumping Genes Genetic algorithm UWB antenna

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