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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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A decomposition-based multi-objective evolutionary algorithm using infinitesimal method

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APPLIED SOFT COMPUTING 2024年 167卷

作者： Wang, Jing Mei, Shunce Liu, Changxin Peng, Hu Wu, Zhijian Jiangxi Univ Finance & Econ Sch Software & IoT Engn Nanchang 330013 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

multi-objective evolutionary algorithm based on decomposition (MOEA/D) has been extensively employed to address a diverse array of real-world challenges and has shown excellent performance. However, the initial collection of aggregate weight vectors proves unsuitable for multi-objective optimization problems (MOPs) featuring intricate Pareto front (PF) structures, and the solving performance will be greatly affected when MOEA/D solves these irregular MOPs. In light of these challenges, a refined MOEA/D algorithm utilizing infinitesimal method is proposed. This algorithm incorporates the notion of global decomposition stemming from infinitesimal method to streamline the feature information of PF, thereby facilitating the adjustment of the weight vector towards optimal distribution. Consequently, enhancements in resource allocation efficiency and algorithmic performance are achieved. In the empirical investigation, the algorithm's performance is tested on 28 benchmarks from ZDT,DTLZ and WFG test suits. Wilcoxon's rank-sum test and Fredman's test were carried out on performance metrics, which proved that the proposed MOEA/D-DKS was superior to other comparison algorithms.

关键词： multi-objective evolutionary algorithm Decomposition Infinitesimal method Weight vector adjustment

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A multi-objective evolutionary algorithm with New Reproduction and Decomposition Mechanisms for the multi-Point Dynamic Aggregation Problem

A Multi-objective Evolutionary Algorithm with New Reproducti...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Gao, Guanqiang Xin, Bin Mei, Yi Lu, Shengyu Ding, Shuxin Beijing Inst Technol Beijing Peoples R China Victoria Univ Wellington Wellington New Zealand China Acad Railway Sci Corp Ltd Signal & Commun Res Inst Beijing Peoples R China

ISBN: (纸本)9781450392372

An emerging optimisation problem from real-world applications, named the multi-point dynamic aggregation (MPDA) problem, has become an active research of the multi-robot system. This paper focuses on a multi-objective MPDA (MO-MPDA) problem which is to design execution plans of robots for minimising the cost of used robots and maximising the efficiency of task execution. The MOMPDA problem has the issues of conflicting objectives, redundant representation, and variable-length encoding, posing extra challenges to address the MO-MPDA problem effectively. Combining the..-constraint method and decomposition mechanisms, a novel multi-objective evolutionary algorithm is proposed. The proposed algorithm selects the efficiency objective as the main objective and converts the cost objective as constraints. Thus, the multi-objective problem is decomposed into a series of scalar constrained optimisation subproblems by assigning each subproblem with an upper bound constraint. All the subproblems are optimised and evolved simultaneously with the transferring knowledge from other subproblems to solve the MO-MPDA problem parallelly and efficiently. Besides, considering the characteristics of parent individuals, this paper designs a hybrid reproduction mechanism to transmit effective information to offspring individuals for tackling the encoding redundancy and varying-length. Experimental results show that the proposed algorithm significantly outperforms the state-of-the-art algorithms in terms of most-used metrics.

关键词： multi-objective evolutionary algorithm hybrid reprodcution mechanism multi-robot system multi-point dynamic aggregation

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Knee based multimodal multi-objective evolutionary algorithm for decision making

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INFORMATION SCIENCES 2021年 544卷 39-55页

作者： Zhang, Kai Shen, Chaonan He, Juanjuan Yen, Gary G. Wuhan Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430065 Hubei Peoples R China Hubei Prov Key Lab Intelligent Informat Proc & Re Wuhan 430065 Hubei Peoples R China Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA

Recently, many advanced multimodal multi-objective evolutionary algorithms (MMOEAs) have been presented to solve multimodal multi-objective optimization problems (MMOPs). However, most of these approaches struggle not only to find the entire Pareto sets and Pareto front, but also ignoring the problem of multi-Criteria Decision Making (MCDM) to be handled at a later stage. As a matter of fact, it is a very eclectic and challenging burden for decision makers to choose some trade-off optimal solutions from an enormous collection of non-dominated Pareto-optimal individuals. In this paper, we propose a knee based evolutionary algorithm, named MMO-EvoKnee, which incorporates MCDM strategy into solving MMOPs. The proposed algorithm is designed to search for a complete set of global knee solutions instead of an entire Pareto front and Pareto sets. Firstly, the EvoKnee Selection is adopted to find target global knee solutions and boundary individuals. Secondly, the Knee multimodal Solutions Selection focuses on decision space exploitation to obtain well-converged multiple Pareto-optimal knee solutions. Thirdly, the Maximum Spanning Subset Selection is designed to accurately identify a complete number of knee solutions from overcrowded Pareto regions. Fourthly, the Boundary multimodal Solutions Selection can find and maintain well-diversified and well-converged boundary solutions. As a result, MMO-EvoKnee can consistently find all knee solutions of interest quickly, and it relieves the burden for decision makers. Finally, the performance of the proposed algorithm is evaluated on thirteen benchmark MMOPs. The experiment results clearly show that the proposed MMO-EvoKnee provides a competitive edge over the chosen state-of-the-art MMOEAs. (C) 2020 Elsevier Inc. All rights reserved.

关键词： multi-objective evolutionary algorithm multimodal multi-objective optimization multi-criteria decision making multi-objective optimization multimodal optimization

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Automatic Optimization of Guidance Guardrail Layout Based on multi-objective evolutionary algorithm

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Complex System Modeling and Simulation 2024年第4期4卷 353-367页

作者： Wei-Li Liu Yixin Chen Xiang Li Jinghui Zhong Rongjun Chen Hu Jin School of Computer Science Guangdong Polytechnic Normal UniversityGuangzhou 510006China School of Computer Science and Engineering South China University of TechnologyGuangzhou 510006China School of Electrical Engineering Hanyang UniversityAnsan 15588Republic of Korea

Guardrails commonly play a significant role in guiding pedestrians and managing crowd flow to prevent congestion in public ***,existing methods of the guardrail layout mainly rely on manual design or mathematical models,which are not flexible or effective enough for crowd control in large public *** address this limitation,this paper introduces a novel automated optimization framework for guidance guardrails based on a multi-objective evolutionary *** paper incorporates guidance signs into the guardrails and designs a coding-decoding scheme based on Gray code to enhance the flexibility of the guardrail *** addition to optimizing pedestrian passage efficiency and safety,the paper also considers the situation of pedestnan counterflow,making the guardrai layout more *** results have demonstrated the effectiveness of the proposed method in alleviating safety hazards caused by potential congestion,as well as its significant improvements in passage effciency and prevention of pedestrian counte rflow.

关键词： automatical guardrall layout optimization,crowd simulation multi-objective evolutionary algorithm

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Dynamic multi-objective evolutionary algorithm for IoT services

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APPLIED INTELLIGENCE 2021年第3期51卷 1177-1200页

作者： Fang, Shun-shun Chai, Zheng-yi Li, Ya-lun Tiangong Univ Sch Comp Sci & Technol Tianjin 300387 Peoples R China Tianjin Key Lab Autonomous Intelligence Technol & Tianjin 300387 Peoples R China Tiangong Univ Sch Elect & Informat Engn Tianjin 300387 Peoples R China

The primary goal of the Internet of things(IoT) is to provide people with anywhere services in real life. But intelligent IoT shouldn't only provide services, but also consider how to allocate heterogeneous resources reasonably, which has become a very challenging problem. To obtain the best resource allocation scheme, it is crucial to minimize the service cost and service time. Since the two objectives are contradictory, we have modelled IoT services as a dynamic multi-objective optimization problem. Then a dynamic multi-objective evolutionary algorithm for dynamic IoT services(dMOEA/DI) is proposed. In dMOEA/DI, we have designed operators such as the appropriate encoding method, dynamic detection operator, filtering strategy, differential evolution, and polynomial mutation. Based on the single service strategy and collaborative service strategy, experimental research is performed on the agricultural IoT services with dynamic requests under different distributions. The simulation experimental results prove that dMOEA/DI performs better than the contrasted algorithms on the IoT service optimization problems.

关键词： Agriculture Differential evolution Dynamic multi-objective optimization Internet of things multi-objective evolutionary algorithm

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A multi-objective evolutionary algorithm based on dimension exploration and discrepancy evolution for UAV path planning problem

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INFORMATION SCIENCES 2024年 657卷

作者： Xu, Xiuju Xie, Chengyu Luo, Zongfu Zhang, Chuanfu Zhang, Tao Sun Yat Sen Univ Sch Syst Sci & Engn Guangzhou 510275 Peoples R China

Path planning is a crucial process for unmanned aerial vehicles (UAVs) and involves finding a path that is both short and safe. However, with the ever-increasing complexity of the environment, solving the UAV path-planning problem is challenging. Traditional path-planning methods cannot handle conflicting goals effectively, and existing objective methods lack targeted exploration mechanisms, resulting in unsatisfactory outcomes. By modeling the UAV path-planning problem via multi-objective optimization, this study designed a reasonable objective function composition for the model and considered obstacle avoidance as a hard constraint to satisfy the actual situation. A multi-objective evolutionary algorithm based on dimensional exploration and discrepancy evolution (MOEA-2DE) is presented. In particular, MOEA-2DE utilizes dimensional perturbation to identify key dimensions to facilitate prior exploration and enhance the targeted search. An adaptive evolution strategy based on population discrepancy was employed to assess the evolution process, and various methods were adopted to balance convergence and diversity. The effectiveness of the MOEA-2DE was demonstrated through the design of two intricate terrain sets and comparisons with various classic and state-of-the-art multi-objective evolutionary algorithms (MOEAs), including those designed for UAV path planning across multiple metrics. The results verify the superiority of MOEA-2DE in terms of both convergence speed and final effect.

关键词： Path planning UAV multi-objective optimization multi-objective evolutionary algorithm

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A direction vector-guided multi-objective evolutionary algorithm for variable linkages problems

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APPLIED SOFT COMPUTING 2023年 145卷

作者： Gu, Qinghua Zhang, Shaopeng Wang, Qian Xiong, Neal N. Xian Univ Architecture & Technol Sch Resources Engn Xian 710055 Shaanxi Peoples R China Xian Univ Architecture & Technol Xian Key Lab Intelligent Ind Percept Calculat & D Xian 710055 Shaanxi Peoples R China Northeastern State Univ Dept Math & Comp Sci Tahlequah OK USA Xian Univ Architecture & Technol Sch Management Xian 710055 Shaanxi Peoples R China

Recombination is a key component for the evolutionary algorithm to provide promising offspring solutions. However, conventional recombination operators cannot generate high-quality solutions for variable linkages problems due to the particularity of the Pareto optimal set (PS). To tackle this problem, a two-stage multi-objective evolutionary algorithm based on direction vector guidance (DSMOEA) is proposed in this paper. Firstly, a portion of the population is transformed by the eigenmatrix of the covariance matrix to increase the probability of generating high-quality offspring. Then the representative solutions are selected in the transformed population to create the direction vectors. Under the guidance of the direction vectors, the population rapidly approaches PS and generates promising offspring solutions. Finally, Differential Evolution (DE) is performed for searching globally to increase the diversity of the population. The proposed algorithm is tested on three classes of variable linkages problems with 30, 50, and 100 dimensions to verify its performance. The results show that the algorithm is promising for variable linkages problems.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： multi-objective evolutionary algorithm Variable linkages problem Direction vector DE algorithm Two-stage

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Real-time data-driven automatic design of multi-objective evolutionary algorithm: A case study on production scheduling

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APPLIED SOFT COMPUTING 2023年 138卷

作者： Zhang, Biao Meng, Lei-lei Lu, Chao Li, Jun-qing Liaocheng Univ Sch Comp Sci Liaocheng 25200 Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Shandong Normal Univ Sch Comp Sci Jinan 252000 Peoples R China

multi-objective evolutionary algorithms (MOEAs) have become an important choice for solving multi -objective optimization problems. The performance of MOEAs is highly dependent on the algorithm configuration. Therefore, the algorithm configuration is an essential task in the development and application of MOEAs. In this paper, a real-time data-driven automatic design method for configuring an MOEA with minimal user interference is developed. Real-time data are driven in two ways. One lies in that the learning model is constructed based on the elite configurations selected by the Iterated F-Race (I/F-Race), which is used to bias the sampling toward the best configurations. Another is that the decision tree model is constructed by collecting the evaluated configurations in the process of I/F-Race as the data, and used to help identify the potential configurations to improve the sampling quality. In addition, a configurable MOEA (CMOEA) framework is summarized by integrating three general fitness assignment methods. In the experimental study, a case study on a multi-objective hybrid flowshop scheduling problem is conducted. By comparing with other variants of I/F-Race, the developed method is verified to have the ability of evaluating the promising configurations more fully and conceiving the best MOEA. Compared with the famous frameworks and state-of-the-art MOEAs, the proposed CMOEA framework and the automated algorithm show their superiorities based on different performance metrics.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： multi-objective evolutionary algorithm Automatic algorithm design Hybrid flowshop scheduling problem I F-race Decision tree

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