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Mining High Quality Patterns Using multi-objective evolutionary algorithm

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2022年第8期34卷 3883-3898页

作者： Fang, Wei Zhang, Qiang Sun, Jun Wu, Xiaojun Jiangnan Univ Dept Comp Sci & Technol Jiangsu Prov Engn Lab Pattern Recognit & Computat Wuxi 214122 Jiangsu Peoples R China

Pattern mining (PM) refers to the process of discovering patterns of interest to users from data. However, most studies have considered only one pattern, such as frequent pattern or high-utility pattern. With the continuous requirement of businesses in various industries, the single-objective PM methods are difficult to meet the increasingly diverse needs of users. In this paper, a multi-objective problem model for high quality pattern mining (HQPM) is proposed, where the objectives are support, occupancy, and utility. In order to solve the proposed three-objective problem efficiently, an improved multi-objective evolutionary algorithm for HQPM (MOEA-PM) is proposed. Two kinds of population initialization strategies are designed, which is used to ensure the population is effectively distributed in the feasible solution space. By taking the properties of the model into consideration, an auxiliary tool is proposed to accelerate the convergence of the algorithm. Experimental results on real-world datasets show that the proposed three-objective problem model with the MOEA-PM algorithm can discover patterns that are both frequently occurring and has a high utility in the transaction datasets, while at the same time being relatively complete. Compared with the state-of-the-art MOEA-based HQPM algorithms, MOEA-PM has better performance in terms of efficiency, quality, and convergence speed.

关键词： Data mining Itemsets Sociology Statistics Optimization evolutionary computation Pattern mining support utility occupancy multi-objective evolutionary algorithm

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An adaptive disturbance multi-objective evolutionary algorithm based on decomposition

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INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL 2022年第4期41卷 306-315页

作者： Shi, Yanfang Shi, Jianguo Hebei Software Inst Baoding 071000 Hebei Peoples R China

In solving multi-objective optimisation problems, the uniformly distributed weight vector of decomposition based multi-objective evolutionary algorithm (MOEA/D) is not completely suitable for the non-uniformly distributed Pareto front (PF). In order to solve the situation above, this paper proposes an adaptive disturbance multi-objective evolutionary algorithm based on decomposition (AD-MOEA/D), which introduces the disturbance individuals and disturbance weight vectors during the evolution. The disturbance individuals maintain the population diversity and improve convergence accuracy. The disturbance weight vectors assist the weight vectors to adjust adaptively and improve the distribution of PF. Besides, both disturbance individuals and disturbance weight vectors are produced according to the actual evolution, which will not participate in evolution when it is not necessary. The experimental results on multi-objective test functions show that the PF optimised by AD-MOEA/D has better convergence and distribution.

关键词： multi-objective evolutionary algorithm disturbance individuals disturbance weight vectors decomposition

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Local Search Enhanced multi-objective evolutionary algorithm for Fuzzy Flexible Job Shop Scheduling

Local Search Enhanced Multi-Objective Evolutionary Algorithm...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Zhang, Xuwei Zhao, Ziyan Liu, Shixin College of Information Science and Engineering Northeastern University The State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China

ISBN: (纸本)9781665430654

The uncertainty in actual manufacturing systems often manifests as uncertain processing times, especially in flexible manufacturing systems. This work proposes a Decomposition-based evolutionary algorithm with Local Search (DLSEA) to solve flexible scheduling with fuzzy processing times by minimizing makespan and total machine workload. Considering the different scales of objectives, two normalization methods are employed on subpopulations, respectively, aiming to mitigate the potential detrimental effects of a single normalization method. This work also introduces a local search method to enhance the performance of DLSEA. The proposed DLSEA is compared with four state-of-the-art algorithms on two series of cases. The experimental results show that DLSEA exhibits superior search capabilities. © 2023 IEEE.

关键词： Fuzzy flexible job shop scheduling Local search multi-objective evolutionary algorithm Normalization

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A multi-objective evolutionary algorithm for robust positive-unlabeled learning

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INFORMATION SCIENCES 2024年 678卷

作者： Qiu, Jianfeng Tang, Qi Tan, Ming Li, Kaixuan Xie, Juan Cai, Xiaoqiang Cheng, Fan Anhui Univ Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei 230601 Peoples R China Anhui Univ Sch Artificial Intelligence Hefei 230601 Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China Anhui Jianzhu Univ Sch Math & Phys Hefei 230601 Peoples R China Hefei Univ Sch Artificial Intelligence & Big Data Hefei 230601 Peoples R China

Positive and unlabeled (PU) learning is to learn a binary classifier with good generalization ability from PU data. A variety of PU learning algorithms with promising performance have been proposed. However, most of them assume that PU samples are "clean", which is not true in real applications due to the existing noisy or redundant samples. Thus, how to obtain a robust PU classifier with better performance is a challenging problem. To this end, we propose a novel multi -objective evolutionary algorithm to tackle it, named BPUSS-MOEA. Specifically, we firstly transform the robust PU learning into a bi-objective PU sample selection (BPUSS) problem, in which two objectives are designed. One is the number of selected "clean" PU samples and the other is the PU accuracy. Then, a dual -coding scheme is designed to represent the selected "clean" PU samples and the labels of U samples. With the dual -coding scheme, a novel offspring generation strategy is developed to achieve the offsprings with high quality. To further improve the performance of BPUSS-MOEA, an effective population initialization strategy is designed. Experiments on 10 datasets with different noise levels show that compared with the state -ofthe -arts, the proposed algorithm demonstrates its robustness in terms of the PU accuracy.

关键词： Robust PU learning multi-objective evolutionary algorithm PU sample selection Dual-coding scheme

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An Effective multi-objective evolutionary algorithm Based on Decomposition for A Robot Rescue Path Planning Problem 41

An Effective Multi-objective Evolutionary Algorithm Based on...

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第41届中国控制会议

作者： Loulei Dai Hongguo Wang Quan-ke Pan School of Mechanical and Electrical Engineering and Automation Shanghai University Liaocheng Vocational & Technical College

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

The paper addresses a post-disaster robot rescue path planning problem where a number of people are trapped and a robot is used to rescue *** objective functions are to minimize the number of deaths and the number of serious *** first,a path matrix between two trapped people is obtained by using an improved A *** then,an improved multiobjective evolutionary algorithm is proposed to find rescue paths with Pareto optimal or near-optimal *** last,the proposed algorithm is compared with other multi-objective *** experimental results demonstrate the proposed algorithm can effectively plan a shorter path to reduce the number of deaths and serious injuries after disasters.

关键词： Robot Rescue Path planning A~* algorithm multi-objective evolutionary algorithm

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Automated optimization for steel mold of precast components based on multi-objective evolutionary algorithm

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JOURNAL OF CONSTRUCTIONAL STEEL RESEARCH 2024年 221卷

作者： Xu, Chengran Zhou, Xuhong Lin, Xuchuan Peng, Weibing Zhang, Chao Zhejiang Univ Technol Coll Civil Engn 288 Liuhe St Hangzhou 310024 Zhejiang Peoples R China Chongqing Univ Sch Civil Engn 174 Shazheng St Chongqing 400044 Peoples R China China Earthquake Adm Inst Engn Mech Key Lab Earthquake Engn & Engn Vibrat 29 Xuefu Rd Harbin 150000 Peoples R China

Designing an economical and reliable steel mold for precast concrete (PC) components can accelerate the promotion of prefabricated buildings. This paper presents an automated design framework for a steel mold of PC components to generate manufacturing information, including dimensions and location of each plate. The profile and dimensions of PC components are extracted from the shop drawings, and a finite element model of the steel mold is established and initialized. A multi-objective optimization model considering material usage and manufacturing convenience is developed to determine the geometry and quantity of plates in steel molds. The non-dominated sorting genetic algorithm II (NSGA-II) is applied to obtain a Pareto solution set for steel mold design. The production information in DXF format can be automatically generated by parsing Industry Foundation Classes (IFC) model. The cost-effectivity and practicality of the proposed framework were verified via the design case for a real PC stair mold.

关键词： Precast concrete components Steel mold Industry foundation classes Finite element (FE) method multi-objective evolutionary algorithm

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An effective multi-objective evolutionary algorithm for multiple spraying robots task assignment problem

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SWARM AND evolutionary COMPUTATION 2024年 87卷

作者： Dong, Jin-Shuai Pan, Quan-Ke Miao, Zhong-Hua Sang, Hong-Yan Gao, Liang Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China Liaocheng Univ Sch Comp Sci Liaocheng 252000 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

This paper addresses a multiple agricultural spraying robots task assignment problem in the greenhouse environment. The objective of the problem is to obtain a set of Pareto solutions that simultaneously optimize the total travel distance and maximum completion time of all robots. To solve this problem, an effective multi-objective evolutionary algorithm is proposed. In the proposed algorithm, an initial population with high quality and diversity is generated by a heuristic allocation strategy based on robot capacity constraints. During the evolutionary phase, a crossover strategy based on information in the non-dominated solution set is designed for exploration in the global scope. A multi-objective local search with an iterated greedy idea is introduced to improve the exploration ability of the algorithm. Meanwhile, a restart operator based on the ideal point is presented to jump out of the local optimum. Finally, extensive experiments based on different scales are conducted. The results show that the proposed algorithm significantly outperforms several state-of-the-art multiobjective algorithms in the literature.

关键词： Agricultural spraying robots Task assignment multi-objective evolutionary algorithm Heuristic

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A stable-state multi-objective evolutionary algorithm based on decomposition

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 239卷

作者： Wang, Jing Zheng, Yuxin Huang, Pengcheng Peng, Hu Wu, Zhijian Jiangxi Univ Finance & Econ Sch Software & IoT Engn Nanchang 330032 Peoples R China JiuJiang Univ Sch Comp & Big Data Sci Jiujiang 332005 Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China

The decomposition-based multi-objective evolutionary algorithm (MOEA/D) has been shown to effectively solve real-world multi-objective optimization problems (MOPs). The uniformly distributed weight vectors guide the population to continuously evolve towards the Pareto front (PF). However, the random matching mode between individuals and weight vectors leads to some excellent individuals being replaced in the evolution process. Meanwhile, the number of replaced individuals in the neighborhood is not controlled. This causes a waste of computing resources and fails to balance the diversity and convergence of the population effectively. Referring to the influence on body adjustment of homeostasis in the medical field, the stable-state mechanism is proposed to keep the dynamic balance between exploration and exploitation. Therefore, this paper presents the stable-state multi-objective evolutionary algorithm based on decomposition (MOEA/D-SS) that adopts a new stable-state replacement strategy to adjust the number of replaced individuals within each neighborhood. Furthermore, a stable-state neighborhoods adjustment strategy was proposed to adjust the size of each neighborhood. This mechanism can adjust the convergence and diversity of newly generated individuals at different stages. Finally, several benchmark test suites (i.e., ZDT, DTLZ, and UF) and a practical optimization problem are used to test the performance of MOEA/D-SS. The experimental results demonstrate that the proposed algorithm outperforms other comparative algorithms.

关键词： multi-objective evolutionary algorithm Decomposition Stable-state replacement Matching mechanism Neighborhood adjustment

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A novelty-based multi-objective evolutionary algorithm for identifying functional dependencies in complex technical infrastructures from alarm data

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Environment Systems and Decisions 2022年第2期42卷 177-188页

作者： Antonello, Federico Baraldi, Piero Zio, Enrico Serio, Luigi Energy Department Politecnico di Milano Via Lambruschini 4 Milan 20156 Italy MINES ParisTech PSL Research University CRC Sophia Antipolis 75006 France Technology Department CERN Geneva 23 1211 Switzerland

In this work, a multi-objective evolutionary algorithm (MOEA) is developed to identify Functional Dependencies (FDEPs) in Complex Technical Infrastructures (CTIs) from alarm data. The objectives of the search are the maximization of a measure of novelty, which drives the exploration of the solution space avoiding to get trapped in local optima, and of a measure of dependency among alarms, which drives the uncovering of functional dependencies. The main contribution of the work is the direct identification of patterns of dependent alarms;this avoids going through the preliminary step of mining association rules, as typically done by state-of-the-art methods which, however, fail to identify rare functional dependencies due to the need of setting a balanced minimum occurrence threshold. The proposed framework for FDEPs identification is applied to a synthetic alarm database generated by a simulated CTI model and to a real large-scale database of alarms collected at the CTI of CERN (European Organization for Nuclear Research). The obtained results show that the framework enables the thorough exploration of the solution space and captures also rare functional dependencies. © 2022, The Author(s).

关键词： Abnormal behaviours Alarm data Complex technical infrastructures Functional dependencies Genetic algorithm multi-objective evolutionary algorithm Novelty search Particle accelerator

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A multi-objective evolutionary algorithm Based on Cone Heart Point Adjustment Vectors to Solve Concave-convex Problems 34

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

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34th Chinese Control and Decision Conference (CCDC)

作者： Li, Xin Li, Xiaoli Wang, Kang Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Minist Educ Beijing Key Lab Computat Intelligence & Intellige Engn Res Ctr Digital Commun Beijing 100124 Peoples R China

ISBN: (纸本)9781665478960

Decomposition-based multi-objective optimization algorithms 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 subregions. However, 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 problems. To 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 vector. Then, the distribution of reference vectors are adjusted according to the cone heart point on the basis of the traditional vectors. Finally, 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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