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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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PAIDDE: A Permutation-Archive Information Directed Differential Evolution Algorithm

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IEEE ACCESS 2022年 10卷 50384-50402页

作者： Li, Xiaosi Wang, Kaiyu Yang, Haichuan Tao, Sichen Feng, Shuai Gao, Shangce Wesoft Co Ltd Dept Engn Kawasaki Kanagawa 2100024 Japan Univ Toyama Fac Engn Toyama 9308555 Japan China Unicom Smart Connect Technol Ltd Beijing 100037 Peoples R China

evolutionary algorithms have shown great successes in various real-world applications ranging in molecule to astronomy. As a mainstream evolutionary algorithm, differential evolution (DE) possesses the characteristics of simple algorithmic structure, easy implement, and efficient search performance. Nevertheless, it still suffers from the issues of local optimal trapping and premature of evolution problems. In this study, we innovatively improve the performance of DE by incorporating a full utilization of information feedback, which includes the population's holistic information and the direction of differential vectors. The proposed permutation-archive information directed differential evolution (PAIDDE) algorithm is verified on a set of 29 benchmark numerical functions and 22 real-world optimization problems. Extensive experimental and statistical results show that PAIDDE can significantly outperform other 12 state-of-the-art algorithms in terms of solution qualities. Additionally, the computational complexity, solution distribution, convergence speed, search dynamics, and population diversity of PAIDDE are systematically analyzed.

关键词： Statistics Sociology Evolution (biology) Optimization evolutionary computation Licenses Convergence Meta-heuristic algorithms differential evolution optimization population diversity evolutionary algorithms swarm intelligence

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Fuzzy Prediction Algorithm of Construction Cost Based on Improved Particle Swarm Evolution Calculation 3

Fuzzy Prediction Algorithm of Construction Cost Based on Imp...

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3rd International Conference on Artificial Intelligence and Autonomous Robot Systems, AIARS 2024

作者： Liu, Yuding Li, Xin Huang, Yuejuan Ding, Juan Zeng, Fanju Harbin Huade University Harbin Heilongjiang150025 China

ISBN: (纸本)9798350376173

To achieve precise and rapid cost predictions for industrial construction in the project's early stages, this study compares the predictive capabilities of neural networks and support vector machines optimized by various algorithms. The aim is to identify the most effective model for predicting industrial construction costs. Utilizing an enhanced particle swarm optimization technique, we reformulate the task of selecting the best cost evaluation approach as a dynamic optimization challenge involving potential cost scenarios constrained by multiple objectives. The fitness value undergoes iterative refinement. Simulations and comparative experiments reveal that our algorithm's numerical error typically falls within 1, whereas other methods often range between 1 and 2. In the best-case scenario, our approach achieves a numerical error of 0, exhibiting superior convergence speed and value. When optimizing multimodal functions, the hybrid operator-based strategy performs relatively better, with values generally exceeding 1 and peaking at 1.9. © 2024 IEEE.

关键词： evolutionary computation fuzzy prediction of engineering cost Particle swarm

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Resolving Goal-Conflicts and Scaling Synthesis through Mode-Based Decomposition 24

Resolving Goal-Conflicts and Scaling Synthesis through Mode-...

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44th International Conference on Software Engineering: Companion Proceedings

作者： Brizzio, Matias Univ Politecn Madrid IMDEA Software Inst Madrid Spain

ISBN: (纸本)9798400705021

Reactive synthesis, with its roots in the work of A. Church, presents a transformative approach for the formal methods community. It seeks to translate system behaviors expressed in Linear-Time Temporal Logic (LTL) into correct-by-construction models using synthesis tools. However, this approach faces substantial challenges. Among these challenges is the high computational complexity of LTL synthesis, which constrains its application to large-scale systems. Additionally, unrealizable specifications present a significant obstacle as they act as barriers, impeding the synthesis process. Furthermore, the presence of goal-conflicts within requirements introduces contradictions and ambiguity, further complicating the synthesis process. These issues collectively make synthesis demanding, often resulting in suboptimal or unviable systems. Our research is dedicated to establishing a robust framework that systematically addresses these challenges, effectively bridging the gap between high-level requirements and dependable system realization. We prioritize refining requirement precision and advancing scalable synthesis techniques, offering advanced tools and methodologies to practitioners and researchers.

关键词： Search-based Software Engineering Formal Methods Requirements Engineering evolutionary computation Reactive Synthesis

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A virtual sensor for backlash in robotic manipulators

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JOURNAL OF INTELLIGENT MANUFACTURING 2022年第7期33卷 1921-1937页

作者： Giovannitti, Eliana Nabavi, Sayyidshahab Squillero, Giovanni Tonda, Alberto COMAU Ind Automat & Robot Grugliasco Italy Politecn Torino Turin Italy INRAE UMR MIA 518 Paris France

Gear backlash is a quite serious problem in industrial robots, it causes vibrations and impairs the robot positioning accuracy. Backlash estimation allows targeted maintenance interventions, preserving robot performances and avoiding unforeseen equipment breakdowns. However, a direct measure of the backlash is hard to obtain, and dedicated auxiliary sensors are required for the measurement. This paper presents a method for estimating backlash in robotic joints that does not require the installation of extra devices. It only relies on data gathered from the motor encoder, which is always present in a robotic joint. The approach is based on the observation of a characteristic vibration pattern arising on the motor speed signal when backlash affects the joint transmission. By looking at the amplitude of this vibration some information about the entity of the backlash in the joint is gathered. Experimental results on simulated data are reported in the study to show the robustness of the method, also with respect of noise. Furthermore, tests on real-world data, gathered from robots installed in a production plant, demonstrate the efficacy of the technique. The approach is cost-effective, fast, and easily automatable, therefore convenient for the industrial world.

关键词： computational Intelligence Backlash Virtual Sensors evolutionary computation

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Application of Supervised Machine Learning Methods on the Multidimensional Knapsack Problem

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NEURAL PROCESSING LETTERS 2022年第2期54卷 871-890页

作者： Rezoug, Abdellah Bader-el-den, Mohamed Boughaci, Dalila Univ Mhamed Bougara Dept Comp Sci Boumerdes Algeria Univ Portsmouth Sch Comp Portsmouth Hants England Univ Sci & Technol Houari Boumedienne Dept Comp Sci Algiers Algeria

Machine Learning (ML) has gained much importance in recent years as many of its effective applications are involved in different fields, healthcare, banking, trading, gaming, etc. Similarly, Combinatorial Optimisation (CO) keeps challenging researchers by new problems with more complex constraints. Merging both fields opens new horizons for development in many areas. This study investigates how effective is to solve CO problems by ML methods. The work considers the Multidimensional Knapsack Problem (MKP) as a study case, which is an np-hard CO problem well-known for its multiple applications. The proposed approach suggests to use solutions of small-size MKP to build models with different ML methods;then, to apply the obtained models on large-size MKP to predict their solutions. The features consist of scores calculated based on information about items while the labels consist of decision variables of optimal solutions calculated from applying CPLEX Solver on small-size MKP. Supervised ML methods build models that help to predict structures of large-size MKP solutions and build them accordingly. A comparison of five ML methods is conducted on standard data set. The experiments showed that the tested methods were able to reach encouraging results. In addition, the study proposes a Genetic Algorithm (GA) that exploits ML outputs essentially in initialisation operator and to repair unfeasible solutions. The algorithm denoted GaPR explores the ML solution neighbourhood as a way of intensification to approach optimal solutions. The carried out experiments indicated that the approach was effective and competitive.

关键词： Machine learning Multidimensional knapsack problem Genetic algorithm Combinatorial optimisation evolutionary computation

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A free-cutting mesh strategy for optimal shape synthesis in magnetics

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2022年第6期16卷 337-352页

作者： Dassi, F. Di Barba, P. Russo, A. Univ Milano Bicocca Dipartimento Matemat & Applicaz Via Cozzi 55 Milan Italy Univ Pavia Dipartimento Ingn Ind & Informaz Via Adolfo Ferrata 5 Pavia Italy CNR IMATI Via Ferrata 5-A Pavia Italy

The authors propose an innovative technique for dealing with optimal shape design problems that exploits the flexibility of the virtual element method in generating meshes composed of general polygonal and polyhedral elements. Virtual element method and finite element method can coexist on the same discretized domain;therefore, the possibility of dealing with hanging nodes and gluing sub-domain meshes is ensured. Accordingly, the shape synthesis of a magnetic pole is considered as the case study. It is shown that the proposed technique is effective in handling the shape variations dictated by an algorithm of evolutionary optimisation.

关键词： optimisation magnetic pole virtual element method hanging nodes computational geometry evolutionary optimisation general polygonal elements optimal shape synthesis free-cutting mesh strategy shape variations subdomain meshes mesh generation optimal shape design problems innovative technique evolutionary computation discretized domain polyhedral elements

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Investigating the Use of Linear Programming and evolutionary Algorithms for Multi-Objective Electric Vehicle Charging Problem

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IEEE ACCESS 2022年 10卷 115322-115337页

作者： Singh, Hemant Kumar Ray, Tapabrata Rana, Md Juel Limmer, Steffen Rodemann, Tobias Olhofer, Markus Univ New South Wales Sch Engn & Informat Technol Canberra ACT 2600 Australia Honda Res Inst Europe D-63073 Offenbach Germany

With the increasing uptake of electric vehicles (EVs), the need for efficient scheduling of EV charging is becoming increasingly important. A charging station operator needs to identify charging/discharging power of the client EVs over a time horizon while considering multiple objectives, such as operating costs and the peak power drawn from the grid. evolutionary algorithms (EAs) are a popular choice when faced with problems involving multiple objectives. However, since the objectives and constraints of this problem can be expressed using linear functions, it is also possible to come up with improvised multi-objective formulations which can be solved with exact techniques such as mixed-integer linear programming (MILP). With both approaches having their potential strengths and pitfalls, it is worth investigating their use to inform the algorithmic choices, which this study aims to address. In doing so, it makes a number of contributions to the topic, including extension of an existing EV charging problem to a multi-objective form;observing some interesting properties of the problem to improve both the MILP and EA solution approaches;and comparing the performance of MILP and EA. The study provides some useful insights into the problem, initial results and quantitative basis for selecting solution approaches, and highlights some areas of further development.

关键词： Electric vehicle charging Optimization Modulation Mixed integer linear programming evolutionary computation Costs Batteries evolutionary algorithms electric vehicle charging mixed-integer linear programming multi-objective optimization

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SMARTEST: A Surrogate-Assisted Memetic Algorithm for Code Size Reduction

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IEEE TRANSACTIONS ON RELIABILITY 2022年第1期71卷 190-203页

作者： Jiang, He Gao, Guojun Ren, Zhilei Chen, Xin Zhou, Zhide Dalian Univ Technol Sch Software Dalian 116024 Peoples R China Dalian Univ Technol DUT Artificial Intelligence Inst Dalian 116024 Peoples R China Key Lab Ubiquitous Network & Serv Software Liaoni Dalian 116024 Peoples R China Hangzhou Dianzi Univ Coll Comp Sci & Technol Hangzhou 310018 Peoples R China

Compiling source code effectively to meet various criteria is a critical task in software engineering. Especially, code size reduction has attracted much attention from both industry and academia due to the requirement of resource utilization. Generally, developers rely on compiler optimization passes to realize code size reduction. However, it is impractical to select a desirable optimization sequence manually since a wide variety of optimization passes are integrated into a compiler. evolutionary algorithms offer an impressive way to alleviate this problem. Nevertheless, previous approaches fail to balance the exploitation and exploration of the search space. Moreover, the expensive fitness evaluation requires actual compilation, which makes the evolution rather time-consuming. To tackle the challenges, we propose a novel approach SMARTEST, which characterizes the systematic exploitation of a huge volume of historical compilation information. Specifically, SMARTEST comprises two components: 1) a local search operator to enhance the solution quality;and 2) a data-driven surrogate model to avoid expensive fitness evaluation. We evaluate the effectiveness of SMARTEST over the cBench benchmark suite. Experimental results indicate that SMARTEST outperforms the standard level -Os by 2.17% on average, and achieves 1.2 times code size reduction compared with the genetic algorithm. Furthermore, experimental results over the benchmark suite evidently show that SMARTEST gets a better result and takes less actual fitness evaluations than its variants, which demonstrates the contribution of the local search and the surrogate model.

关键词： Optimization Standards evolutionary computation Genetic algorithms Genetics Software Search problems Code size reduction compiler optimization selection computationally expensive problems memetic algorithm surrogate-assisted

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An adaptive replacement strategy-incorporated particle swarm optimizer for wind farmlayout optimization

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ENERGY CONVERSION AND MANAGEMENT 2022年 269卷

作者： Lei, Zhenyu Gao, Shangce Wang, Yirui Yu, Yang Guo, Lijun Univ Toyama Fac Engn Toyama 9308555 Japan Ningbo Univ Fac Elect Engn & Comp Sci Ningbo University 315211 Zhejiang Peoples R China Nanjing Univ Posts & Telecommun Coll Automat Nanjing 210023 Peoples R China Nanjing Univ Posts & Telecommun Coll Artificial Intelligence Nanjing 210023 Peoples R China

The wind farm layout optimization (WFLO) aims to maximize power generation of a wind farm by optimizing the location of wind turbines. Traditional mathematical methods cannot provide a satisfactory solution for a wind farm due to the high complexity of the problem. Meta-heuristic algorithms have been used to optimize it. Particularly, genetic algorithms (GA) have been widely used and obtained success in WFLO problems. However, GA still suffers from the issues of insufficient optimization efficiency. In this study, a genetic learning particle swarm optimization with an adaptive strategy, termed AGPSO, is proposed to optimize WFLO problems. The strategy adaptively adjusts the location of the worst turbine to improve the conversion efficiency of a wind farm. Four wind scenarios, including single wind speed with single wind direction, single wind speed with uniform multiple wind directions, single wind speed with nonuniform multiple directions, and multiple wind speeds with multiple wind directions scenarios ones, are utilized to verify the effectiveness of AGPSO and the effect of different wind scenarios for it. Twelve constraints and three different scales are used to further verify the robustness of AGPSO and the effect of wind turbine location on WFLO problems. Extensive numerical experiment results demonstrate that AGPSO performs significantly better than other twelve state-of-the-art competitors in terms of conversion efficiency under different wind farms, wind scenarios, and constraints. AGPSO obtains the best average of 89.92%, 92.90%, 95.39%, and 90.75% conversion efficiency in a wind farm with 25 wind turbines under four wind scenarios, respectively.

关键词： Wind farm layout optimization Particle swarm optimization Wake effect evolutionary computation Adaptive replacement strategy

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