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33rd IEEE International Symposium on Software Reliability Engineering (ISSRE)

作者： Ayerdi, Jon Arrieta, Aitor Pobee, Ernest Bota Arratibel, Maite Mondragon Unibertsitatea Arrasate Mondragon Spain Orona Hernani Spain

ISBN: (纸本)9781665451321

Metamorphic testing is a technique that has shown great potential to alleviate the test oracle problem by exploiting the relations among the inputs and outputs of different executions of a system. However, this approach requires multiple test executions. In applications like Cyber-Physical Systems (CPSs), where the test executions can be very expensive in terms of time and resources needed, this can supose a problem. Therefore, it is paramount to optimize the test suite to reduce the costs of verifying the system. Test case selection is an optimization technique which accomplishes this by selecting a subset of test cases while aiming to preserve the effectiveness of the original test suite as much as possible. While there are many approaches for test case selection in the existing literature, none of them has been proposed for the metamorphic test case selection problem, where each metamorphic test case consists of a source and, at least, a follow-up test case pair. In this work, we present an evolutionary multi-objective approach for the metamorphic test case selection problem, adapting existing multi-objective test selection techniques and proposing new evolutionary operators and objective functions. Furthermore, we evaluate our approach with a set of metamorphic tests developed for an industrial case study from the elevation domain. The results suggest that our approach outperforms both Random Search and the same metaheuristic algorithm without the new evolutionary operators we propose.

关键词： Cyber-Physical Systems Elevators Metamorphic Testing Test Selection

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Investigating the conflicts between different stakeholders' preferences in a blood supply chain at emergencies: a trade-off between six objectives

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SOFT COMPUTING 2021年第21期25卷 13389-13410页

作者： Behroozi, F. Monfared, M. A. S. Hosseini, S. M. H. Alzahra Univ Fac Engn Dept Ind Engn Tehran Iran Shahrood Univ Technol Dept Ind Engn & Management Shahrood Iran

Building and solving many-objective optimization models to tackle real-world problems is still very challenging in practical terms. A Blood supply chain (BSC) network is an especial case of the general supply chain usually investigated using single or multi-objective approaches. However, there are always many incompatible targets for this problem in the real world. This paper aims to deal with the problem of BSC network design in emergencies using a six-objective optimization framework. Such a model can appropriately meet and balance the expectations of different stakeholders and the BSC network, considering the immediate and dynamic requirements of a specific emergency event. To formulate the problem at hand, a new mathematical model is developed as a mixed-integer linear programming (MIP) model. Since the problem is strongly NP-hard, two recently developed solution algorithms called NSGA-III and FR-NSGA-II are adapted and used to solve the problem in a reasonable time. The proposed solution algorithms provide some proper trade-offs between different objectives that can be valuable for related managers. The result shows that a more stable Pareto-optimal solution can be produced using the FR-NSGA-II. Supplementary analysis indicates the superiority of the proposed approach compared to the previous studies in BSC network design.

关键词： Blood supply chain Many-objective optimization Blood substitution preferences Facility location and allocation FR-NSGA-II NSGA-III

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Evolutionary Algorithms for Complex multi-objective optimization Problems

Evolutionary Algorithms for Complex Multi-Objective Optimiza...

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International Symposium on System and Software Reliability (ISSSR)

作者： Shuqu Qian School of Mathematics and Physics Anshun University

ISBN: (数字)9798331501242

ISBN: (纸本)9798331501259

Dynamic Economic Emission Dispatch (DEED) problems present a critical multi-objective optimization challenge in power systems, requiring simultaneous minimization of conflicting objectives of costs and emissions while addressing complex operational constraints. This problem becomes particularly intricate when considering the Valve-Point Effect Cost (VPEC) in generation units, which exhibits non-smooth, non-convex characteristics that significantly complicate solution landscapes. To address these challenges, this talk will discuss a Hybrid Constrained multi- objective Algorithm (HCMOA) incorporating three innovative mechanisms. First, a clonal selection mechanism enhances local exploitation through the systematic generation of highquality, non-dominated solutions. Second, a most-crowded neighborhood update strategy combats premature convergence in high-dimensional DEED scenarios while optimizing archive diversity and computational efficiency. Third, a step-by-step repair strategy ensures operational feasibility through systematic output refinement. Comprehensive evaluations on 10-unit and 15-unit systems with varying VPEC configurations demonstrate HCMOA's superiority over ten state-of-the-art algorithms. Experimental results reveal that HCMOA achieves the Pareto-optimal set with superior spread and distribution uniformity compared to existing approaches. These findings collectively establish HCMOA as an effective solution for practical DEED implementations, particularly in scenarios requiring rigorous consideration of non-convex VPEC characteristics.

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Physics-informed data-driven modeling approach for commuting-oriented hybrid powertrain optimization

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ENERGY CONVERSION AND MANAGEMENT 2024年 299卷

作者： Lei, Nuo Zhang, Hao Li, Rulong Yu, Jun Wang, Hong Wang, Zhi Tsinghua Univ Sch Vehicle & Mobil Beijing 100084 Peoples R China Dongfeng Motor Corp Ltd Wuhan 430058 Peoples R China

The physics-informed data-driven approach is instructive in powertrain modeling with high-fidelity, which can be adopted for powertrain optimization to further improve vehicle performances. In this paper, a plug-in hybrid electric vehicle (PHEV) equipped with a novel powertrain is considered as the research object, and a series of vehicle bench experiments are carried out for operative data acquisition. With the collected data, the physicsinformed data-driven models for engine fuel consumption and battery state of health (SOH) loss are proposed. The accuracy and superiority of the proposed model are demonstrated compared with other approaches. In order to realize the commuting-oriented powertrain optimization, the driving cycle representing the commuting scenario is reconstructed based on the collected traffic data in Beijing. The synthesized driving cycle generated by the proposed method can describe the practical application scenario in Beijing within the allowable error range. In addition, the non-dominated sorting genetic algorithm-II (NSGA-II) is adopted to further optimize the parameters for the hybrid powertrain according to vehicle performances. The multi-objective optimization is completed with the engine fuel consumption and battery state of health (SOH) loss as targets. The results indicate that the optimized configuration parameters can reduce fuel consumption by 4.85% and battery capacity loss by 3.7% compared with the original vehicle.

关键词： Physics-informed data-driven Hybrid powertrain Vehicle bench experiment Commuting scenario reconstruction Evolutionary optimization

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A systematic review of optimization approaches for integrated energy system

A systematic review of optimization approaches for integrate...

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4th Energy Conversion and Economics Annual Forum (ECE Forum 2024)

作者： Feng Li Hui Hou Yingxiang Wang Pingfan Wang School of Automation Wuhan University of Technology Wuhan 430070 People's Republic of China Economics and Technology Research Institute State Grid Hubei Electric Power Company Wuhan 430077 People's Republic of China

ISBN: (数字)9781837242924

Integrated energy system (IES) serves as a critical physical platform for advancing the clean energy transition and achieving carbon reduction goals. This paper systematically reviews the core issues in IES optimization, focusing on objective functions, constraints, and solution algorithms. First, the review outlines a multi-tier IES architecture. Next, it explores the formulation of objective functions in IES optimization, addressing multiple dimensions including economic, decarbonization, safety and loss, etc. It then systematically evaluates various types of constraints in IES, including operational and pricing constraints, among others. Finally, the paper summarizes solution algorithms for IES optimization, such as mathematical optimization, heuristic algorithms, and dynamic programming methods, etc. This review provides a comprehensive theoretical framework and practical basis for optimizing IES.

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Unconventional Surrogate-Assisted approaches to EM-Driven Antenna Design. Modeling and optimization: Global, multi-objective, Statistical

Unconventional Surrogate-Assisted Approaches to EM-Driven An...

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European Conference on Antennas and Propagation, EuCAP

作者： Slawomir Koziel Anna Pietrenko-Dabrowska Engineering Optimization & Modeling Center Reykjavik University Reykjavik Iceland Faculty of Electronics Telecommunications and Informatics Gdansk University of Technology Gdansk Poland

Design of contemporary antenna systems relies heavily on full-wave electromagnetic (EM) simulation tools. EM analysis ensures reliability but tends to be computationally expensive, which becomes a serious issue in the context of design automation. In recent years, utilization of surrogate modeling methods has been fostered to alleviate the cost-related difficulties. Yet, the existing procedures typically follow a few major approaches, e.g., different architectural variations of deep neural networks, hyper-parameter/model adjustments in Bayesian optimization, or more or less standard machine learning frameworks. This paper reviews several unconventional surrogate-assisted techniques that employ less common algorithmic tools such as the response feature technology, domain confinement, dimensionality reduction, or supplementary inverse predictors. We demonstrate how these tools can be incorporated into practical procedures for global and multi-criterial optimization, statistical analysis, and design-oriented behavioral modeling. A discussion of the algorithm operating principles is supplemented by antenna design cases.

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Random Point Evolution-Based Heuristic for Resource Efficiency in OCDMA Networks

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JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT 2023年第1期31卷 25-25页

作者： Martinez, Andre Luis Machado Martinez, Cristiane A. Pendeza A. Abrao, Taufik Univ Tecnol Fed Parana Acad Dept Math Ave Alberto Carazzai 1640 BR-86300000 Cornelio Procopio PR Brazil Univ Estadual Londrina Dept Elect Engn Rod Celso Garcia Cid PR445POB 10-011 BR-86057970 Londrina PR Brazil

Several practical engineering problems can be modeled as a multi-objective optimization (MOO) problems. In this paper we propose three new heuristics for MOO problems, namely based on the dominance of random points, the evolution dominance of random points, and evolution of one random point (EORP) to Pareto frontier. In particular, we explore the jointly energy efficiency (EE) and spectral efficiency (SE) of optical code division multiple access (OCDMA) communication systems. Such bi-objective optimization (BOO) heuristic approaches do not depend on merit functions, and are based on the Pareto's dominance concept. The proposed heuristics generate, in acceptable computational time, a set of dominant solutions very close to the Pareto frontier, allowing the choice of a solution that satisfies specific interests. These heuristics are developed aiming to attain suitable EE-SE trade-offs in practical OCDMA network scenarios. Such trade-offs rely on the jointly EE and SE improvement. We first formulated the SE-EE optimization as a BOO problem with guarantee of minimum data-rate per user. As the three heuristics are applied in the context of OCDMA systems, we compare the performance-complexity of the proposed algorithms with two existing methods, the weighted sum (WS) combined with the evolutionary heuristic particle swarm optimization (PSO), and the exact optimization approach based on the sequential quadratic programming method (SQP). Extensive numerical results are carried out considering realistic OCDMA system scenarios with wide range of optical nodes, while the obtained solutions are represented in the Pareto frontier, corroborating the superiority of the proposed heuristics. Specifically, for larger OCDMA network configurations, the heuristic methods demonstrated convergence to the Pareto frontier, with clear superiority of the EORP method, attaining excellent quantity and well-distributed solution points in the Pareto front. The proposed EORP is promising, surpassi

关键词： Evolutionary heuristic optimization OCDMA networks Spec-tral efficiency (SE) multi-objective optimization (MOO) Weighted sum (WS) Sequential quadratic programming (SQP)

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optimization of Rulebooks via Asymptotically Representing Lexicographic Hierarchies for Autonomous Vehicles

arXiv

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arXiv 2024年

作者： Penlington, Matteo Zanardi, Alessandro Frazzoli, Emilio Institute for Dynamic Systems and Control ETH Zürich Switzerland

A key challenge in autonomous driving is that Autonomous Vehicles (AVs) must contend with multiple, often conflicting, planning requirements. These requirements naturally form in a hierarchy – e.g., avoiding a collision is more important than maintaining lane. While the exact structure of this hierarchy remains unknown, to progress towards ensuring that AVs satisfy pre-determined behavior specifications, it is crucial to develop approaches that systematically account for it. Motivated by lexicographic behavior specification in AVs, this work addresses a lexicographic multi-objective motion planning problem, where each objective is incomparably more important than the next – consider that avoiding a collision is incomparably more important than a lane change violation. This work ties together two elements. Firstly, a multi-objective candidate function that asymptotically represents lexicographic orders is introduced. Unlike existing multi-objective cost function formulations, this approach assures that returned solutions asymptotically align with the lexicographic behavior specification. Secondly, inspired by continuation methods, we propose two algorithms that asymptotically approach minimum rank decisions – i.e., decisions that satisfy the highest number of important rules possible. Through a couple practical examples, we showcase that the proposed candidate function asymptotically represents the lexicographic hierarchy, and that both proposed algorithms return minimum rank decisions, even when other approaches do not. Copyright © 2024, The Authors. All rights reserved.

关键词： Cost functions

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Hybrid Evolutionary multi-objective optimization Algorithm for Helping multi-Criterion Decision Makers

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INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING MANAGEMENT 2021年第2期16卷 94-106页

作者： Abouhawwash, Mohamed Mansoura Univ Fac Sci Dept Math Mansoura 35516 Egypt Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA

Obtaining a specific region from the efficient front for multi-objective and practical optimization problems helps decision-makers. Reference point approaches are suggested to reach the region of interest. Many evolutionary algorithms integrated with a reference point idea to obtain apart from the efficient front solutions close to the reference solution. This paper integrated a penalty boundary intersection (PBI) with the non-dominated sorting genetic algorithm (NSGA-II) to reach the efficient front close to the reference point. The proposed approach allows theoretically convergent solutions to be found. Also, using the advantage of PBI, our algorithm is able to reach a specific part for a set multi and many-objective optimization problems effectively. The proposed algorithm compared with other evolutionary algorithms, such as the original reference-based NSGA-II (R-NSGA-II), r-NSGA-II, and g-NSGA-II. The R-metric values show that the proposed algorithm outperforms the compared algorithms. Using the proposed algorithm for solving the multi-objective engineering design problem helps find solutions according to the decision-makers interest.

关键词： Decision maker evolutionary algorithms reference point penalty boundary intersection convergence

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multi-objective sparse synthesis optimization of concentric circular antenna array via hybrid evolutionary computation approach

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期231卷

作者： Li, Jiahui Sun, Geng Wang, Aimin Zheng, Xiaoya Chen, Zhaoyu Liang, Shuang Liu, Yanheng Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China Jilin Univ Dept Math Changchun 130012 Peoples R China Northeast Normal Univ Sch Informat Sci & Technol Changchun Peoples R China

Sparse synthesis can reduce the maximum sidelobe level (SLL) and overhead of the concentric circular antenna array (CCAA) by switching off partial array elements. However, previous works have not considered finding a balance between the number of switched-on elements of CCAA and reducing the maximum SLL, or jointly optimizing the excitation current weights of array elements to further suppress the maximum SLL. In this work, we address these challenges by formulating a hybrid multi-objective optimization problem (MOP) with discrete and continuous solutions. To solve this hybrid NP-hard problem, we consider two approaches: the two-step and one-step methods. In the two-step method, we convert the original MOP into two subproblems and propose an enhanced non-dominated sorting genetic algorithm-II (ENSGA-II) and an improved genetic algorithm with new mutation and attraction (IGAnMA) to solve these two subproblems. ENSGA-II and IGAnMA involve the hierarchy mechanism and attraction operator to enhance the solving abilities, respectively. In the one-step method, we propose an improved hybrid non-dominated sorting genetic algorithm-II (IHNSGA-II) with global search and hybrid solution update methods to solve the MOP in one step. Numerical simulation results show that the proposed methods are effective and stable for the sparse synthesis of CCAA. Additionally, electromagnetic simulation results demonstrate the validity of the proposed approaches in a more practical environment.

关键词： Beam pattern Concentric circular antenna array Evolutionary computation optimization Sidelobe level

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