Review
Computer Science, Interdisciplinary Applications
Natee Panagant, Nantiwat Pholdee, Sujin Bureerat, Ali Riza Yildiz, Seyedali Mirjalili
Summary: This study compares the performance of 14 new and established multi-objective metaheuristics in solving truss optimization problems, providing insights into the pros and cons of these algorithms and aiding in designing customized algorithms for such problems.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Wenhua Li, Tao Zhang, Rui Wang, Shengjun Huang, Jing Liang
Summary: Multimodal multi-objective problems (MMOPs) are common in the real world, where distant solutions in decision space have very similar objective values. To obtain more Pareto optimal solutions for MMOPs, many multimodal multi-objective evolutionary algorithms (MMEAs) have been proposed. However, there have been few studies comparing the performance of representative MMEAs. In this study, we review the related works and compare the performance of 15 state-of-the-art algorithms utilizing different diversity-maintaining techniques on various types of MMOPs, providing guidance for selecting/designing MMEAs in specific scenarios.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Interdisciplinary Applications
Ali R. Kashani, Amir H. Gandomi, Koorosh Azizi, Charles Camp
Summary: This study investigates the performance of four multi-objective optimization algorithms in the design of a reinforced concrete cantilever retaining wall. The results show that different algorithms perform differently under different design strategies.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2022)
Article
Energy & Fuels
Zhixing Tian, Yu Liu, Chenglong Wang, Kailun Guo, Dalin Zhang, Wenxi Tian, Suizheng Qiu, G. H. Su
Summary: This study investigates and optimizes high-temperature heat pipes, providing important insights through multi-objective optimization. The results reveal that mesh screen and annular artery perform better, different wick materials have different effects on the performance of different wick structures, and as the temperature increases within the specified range, the thermal resistance decreases while the mass and transport capacity increase.
INTERNATIONAL JOURNAL OF ENERGY RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Murat Emre Erkoc, Nurhan Karaboga
Summary: The study summarizes the development of efficient sparse signal recovery algorithms in compressive sensing theory, including various methods such as convex optimization, non-convex optimization, and greedy methods. It also introduces the use of intelligent optimization techniques such as multi-objective approaches, and evaluates multi-objective sparse recovery methods in the literature based on their optimization algorithms, local search methods, and knee point selection methods.
ARTIFICIAL INTELLIGENCE REVIEW
(2022)
Article
Computer Science, Artificial Intelligence
Ke-Jing Du, Jian-Yu Li, Hua Wang, Jun Zhang
Summary: Evolutionary multi-objective multi-task optimization is an emerging paradigm for solving multi-objective multi-task optimization problems using evolutionary computation. This paper proposes treating these problems as multi-objective multi-criteria optimization problems and develops an algorithm framework that utilizes the knowledge of all tasks in the same population. The algorithm selects fitness evaluation functions as criteria, guided by a probability-based selection strategy and an adaptive parameter learning method. Extensive experiments show the effectiveness and efficiency of the proposed algorithm. Treating MO-MTOP as MO-MCOP is a potential and promising direction for solving these problems.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Biochemical Research Methods
Surama Biswas, Sriyankar Acharyya
Summary: In this study, four algorithms based on the Archived Multi Objective Simulated Annealing (AMOSA) framework were proposed for parameter learning in Recurrent Neural Network (RNN) modeling of Gene Regulatory Network (GRN). Comparative studies on performance metrics, including recall, precision and f1 score, showed that the modified algorithms, AMOFSA and AMOTSA, outperformed AMOSAR and other state-of-the-art algorithms in terms of the number of GRNs obtained in the final non-dominated front.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2021)
Article
Thermodynamics
Mohamed Y. Nassar, Mohamed L. Shaltout, Hesham A. Hegazi
Summary: This study develops multi-objective optimum energy management strategies for pre-and post-transmission parallel hybrid electric vehicles. The strategies aim to improve fuel economy, electric system efficiency, battery performance, and life. Multi-objective genetic algorithm is used to solve the energy management problems and generate optimum control inputs. Vehicle dynamics and drivetrain models are integrated with the strategies in a simulation environment. Results show significant improvement in battery performance and varied effects on fuel consumption. The pre-transmission drivetrain configuration shows better battery performance than the post-transmission configuration.
ENERGY CONVERSION AND MANAGEMENT
(2023)
Article
Operations Research & Management Science
Gabriele Eichfelder, Leo Warnow
Summary: The paper presents a proximity measure for KKT conditions violation, which can be used as an indicator for measuring the proximity of a point to the set of efficient solutions. It is well suited for algorithmic use within evolutionary algorithms.
JOURNAL OF GLOBAL OPTIMIZATION
(2021)
Article
Computer Science, Artificial Intelligence
Amarjeet Prajapati
Summary: In this study, the performance of nine large-scale multi-objective optimization optimizers was evaluated and compared over five large-scale many-objective software clustering problems. The results showed that S3-CMA-ES and LMOSCO performed better in most cases, while H-RVEA was the worst performer.
COMPLEX & INTELLIGENT SYSTEMS
(2021)
Article
Engineering, Mechanical
George H. Cheng, G. Gary Wang, Yeong-Maw Hwang
Summary: This study combines the SAKS method with the MTRO strategy to propose the SAKS-MTRO method for MOO problems with expensive black-box constraint functions, demonstrating superior performance.
JOURNAL OF MECHANICAL DESIGN
(2021)
Article
Thermodynamics
J. Nondy, T. K. Gogoi
Summary: This study compares four different ORC configurations and finds that, under optimal conditions, the RR-ORC outperforms the other three configurations. The Regenerative and Recuperative ORCs are ranked as the second and third-best configurations. In all four configurations, the cost rate of exergy loss accounts for approximately 60% of the total system cost rate.
ENERGY CONVERSION AND MANAGEMENT
(2021)
Article
Computer Science, Artificial Intelligence
Kai Zhang, Minshi Chen, Xin Xu, Gary G. Yen
Summary: The paper proposes an evolution strategy MMO-MOES for solving multimodal multi-objective optimization problems, focusing on searching for multiple groups of optimal solutions in decision space. By using a novel niching strategy and requiring a small population size, MMO-MOES is effective in finding well-distributed and well-converged Pareto optimal solutions. Experimental results show exceptional performance compared to leading-edge MMOEAs in various test problems.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Liangjie Zhang, Yuling Xie, Jianjun Chen, Liang Feng, Chao Chen, Kai Liu
Summary: This paper presents a study on multiform multi-objective evolutionary optimization, which aims to construct multiple forms of a given MOP and simultaneously optimize them using evolutionary search to enhance multi-objective optimization performance. Comprehensive empirical studies were conducted to evaluate the proposed multiform paradigm for multi-objective optimization.
Article
Chemistry, Physical
Md Zafar Ahmed, Nitin Padhiyar
Summary: This study examines the optimal operation of a methane reformer using multi-objective optimization (MOO) for spherical reactor, comparing the results with that of cylindrical reactor. Three objective functions were considered: maximizing hydrogen production, minimizing carbon dioxide emission, and minimizing power loss due to pressure drop. Optimizing the feed conditions, including variables such as inlet temperature and molar feed ratios of oxygen to methane & steam to methane, were essential for solving four MOO problems.
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
(2021)
Article
Computer Science, Theory & Methods
Mojgan Pourhassan, Vahid Roostapour, Frank Neumann
THEORETICAL COMPUTER SCIENCE
(2020)
Article
Computer Science, Artificial Intelligence
Tat-Jun Chin, Zhipeng Cai, Frank Neumann
INTERNATIONAL JOURNAL OF COMPUTER VISION
(2020)
Article
Computer Science, Software Engineering
Amritanshu Agrawal, Tim Menzies, Leandro L. Minku, Markus Wagner, Zhe Yu
EMPIRICAL SOFTWARE ENGINEERING
(2020)
Editorial Material
Computer Science, Artificial Intelligence
Thomas Weise, Markus Wagner, Bin Li, Xingyi Zhang, Joerg Laessig
APPLIED SOFT COMPUTING
(2020)
Article
Computer Science, Artificial Intelligence
Jonatas B. C. Chagas, Julian Blank, Markus Wagner, Marcone J. F. Souza, Kalyanmoy Deb
Summary: This paper proposes a method to solve a bi-objective variant of the well-studied traveling thief problem (TTP) by using a biased-random key genetic algorithm with customizations, incorporating domain knowledge, and addressing the bi-objective aspect through an elite population based on non-dominated rank and crowding distance. The method has shown success in BI-TTP competitions at EMO and GECCO conferences, consistently producing high-quality solutions.
JOURNAL OF HEURISTICS
(2021)
Article
Computer Science, Artificial Intelligence
Francesco Quinzan, Andreas Goebel, Markus Wagner, Tobias Friedrich
Summary: The study proposes a new mutation operator for evolutionary algorithms and analyzes its performance on a specific class of problems, showing that it competes effectively with existing operators. The new operator is able to find a (1/3)-approximation ratio on certain types of problems in polynomial time, outperforming combinatorial local search algorithms with constant probability. Experimental evaluations demonstrate the superiority of the new operator over uniform mutation and dynamic schemes in real-world graph and air pollution problems.
Article
Computer Science, Artificial Intelligence
Domagoj Jakobovic, Stjepan Picek, Marcella S. R. Martins, Markus Wagner
Summary: Boolean functions have various applications in different fields, and heuristics play a vital role in their construction. This research investigates the influence of different optimization criteria and variation operators through fitness landscape analysis and Local Optima Networks, observing correlations between local optima fitness, interconnection degree, and basin sizes.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Software Engineering
Martin Schlueter, Mehdi Neshat, Mohamed Wahib, Masaharu Munetomo, Markus Wagner
Summary: The GTOPX space mission benchmark collection is an extension of the GTOP database, featuring ten benchmark instances representing real-world interplanetary space trajectory design problems. By linking to Python and Matlab based on dynamic link libraries, GTOPX ensures fast and accurate reproduction of benchmark results in all programming languages. GTOPX aims to support researchers who wish to tackle nonlinear challenges with advanced algorithms.
Article
Operations Research & Management Science
Jonatas B. C. Chagas, Markus Wagner
Summary: In this article, we address the Thief Orienteering Problem (ThOP) by combining swarm intelligence with a randomized packing heuristic, achieving significant improvements on almost all 432 benchmarking instances.
OPTIMIZATION LETTERS
(2022)
Article
Computer Science, Information Systems
Derek Weber, Frank Neumann
Summary: Political misinformation, astroturfing and organised trolling are online malicious behaviors with significant real-world effects that can influence democratic systems and government policy. Studying these behaviors requires focusing on the small groups behind campaigns and utilizing novel temporal window approaches to detect latent networks of cooperating accounts.
SOCIAL NETWORK ANALYSIS AND MINING
(2021)
Review
Computer Science, Information Systems
Terence Wong, Markus Wagner, Christoph Treude
Summary: Self-adaptive systems (SAS) are characterized by their context-awareness and ability to act on the context to achieve sustained goal achievement. The field of autonomic computing research has experienced significant development over the past 20 years, with applications in various domains.
INFORMATION AND SOFTWARE TECHNOLOGY
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Feng Shi, Xiankun Yan, Frank Neumann
Summary: This paper investigates a chance-constrained version of the Makespan Scheduling problem and analyzes the performance of two algorithms. The processing times of jobs in real life scenarios are often stochastic and may be influenced by external factors.
PARALLEL PROBLEM SOLVING FROM NATURE - PPSN XVII, PPSN 2022, PT II
(2022)
Proceedings Paper
Computer Science, Software Engineering
Hirad Assimi, Frank Neumann, Markus Wagner, Xiaodong Li
Summary: This study addresses the truss optimization problem using exact enumeration and novelty-driven optimization approaches. Experimental results demonstrate that the proposed method outperforms existing methods and obtains multiple high-quality solutions.
EVOLUTIONARY COMPUTATION IN COMBINATORIAL OPTIMIZATION, EVOCOP 2022
(2022)
Proceedings Paper
Computer Science, Theory & Methods
Tobias Friedrich, Frank Neumann, Ralf Rothenberger, Andrew M. Sutton
Summary: Recent interest has been in studying non-uniform random k-SAT models to address the non-uniformity of formulas in real-world applications, challenging the algorithmic complexity of heterogeneous distributions. It is known that dense formulas guaranteed to be satisfiable are easy on average. A broad class of non-uniform random k-SAT models are characterized by distributions over variables that satisfy a balancing condition.
THEORY AND APPLICATIONS OF SATISFIABILITY TESTING, SAT 2021
(2021)
Review
Management
Vinicius N. Motta, Miguel F. Anjos, Michel Gendreau
Summary: This survey presents a review of optimization approaches for the integration of demand response in power systems planning and highlights important future research directions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Philipp Schulze, Armin Scholl, Rico Walter
Summary: This paper proposes an improved branch-and-bound algorithm, R-SALSA, for solving the simple assembly line balancing problem, which performs well in balancing workloads and providing initial solutions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Roshan Mahes, Michel Mandjes, Marko Boon, Peter Taylor
Summary: This paper discusses appointment scheduling and presents a phase-type-based approach to handle variations in service times. Numerical experiments with dynamic scheduling demonstrate the benefits of rescheduling.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Oleg S. Pianykh, Sebastian Perez, Chengzhao Richard Zhang
Summary: Efficient scheduling is crucial for optimizing resource allocation and system performance. This study focuses on critical utilization and efficient scheduling in discrete scheduling systems, and compares the results with classical queueing theory.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Review
Management
Hamed Jahani, Babak Abbasi, Jiuh-Biing Sheu, Walid Klibi
Summary: Supply chain network design is a large and growing area of research. This study comprehensively surveys and analyzes articles published from 2008 to 2021 to detect and report financial perspectives in SCND models. The study also identifies research gaps and offers future research directions.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Patrick Healy, Nicolas Jozefowiez, Pierre Laroche, Franc Marchetti, Sebastien Martin, Zsuzsanna Roka
Summary: The Connected Max-k-Cut Problem is an extension of the well-known Max-Cut Problem, where the objective is to partition a graph into k connected subgraphs by maximizing the cost of inter-partition edges. The researchers propose a new integer linear program and a branch-and-cut algorithm for this problem, and also use graph isomorphism to structure the instances and facilitate their resolution. Extensive computational experiments show that, if k > 2, their approach outperforms existing algorithms in terms of quality.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Victor J. Espana, Juan Aparicio, Xavier Barber, Miriam Esteve
Summary: This paper introduces a new methodology based on the machine learning technique MARS for estimating production functions that satisfy classical production theory axioms. The new approach overcomes the overfitting problem of DEA through generalized cross-validation and demonstrates better performance in reducing mean squared error and bias compared to DEA and C2NLS methods.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Stefano Nasini, Rabia Nessah
Summary: In this paper, the authors investigate the impact of time flexibility in job scheduling, showing that it can significantly affect operators' ability to solve the problem efficiently. They propose a new methodology based on convex quadratic programming approaches that allows for optimal solutions in large-scale instances.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Zhiqiang Liao, Sheng Dai, Timo Kuosmanen
Summary: Nonparametric regression subject to convexity or concavity constraints is gaining popularity in various fields. The conventional convex regression method often suffers from overfitting and outliers. This paper proposes the convex support vector regression method to address these issues and demonstrates its advantages in prediction accuracy and robustness through numerical experiments.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Kuo-Hao Chang, Ying-Zheng Wu, Wen-Ray Su, Lee-Yaw Lin
Summary: The damage and destruction caused by earthquakes necessitates the evacuation of affected populations. Simulation models, such as the Stochastic Pedestrian Cell Transmission Model (SPCTM), can be utilized to enhance disaster and evacuation management. The analysis of SPCTM provides insights for government officials to formulate effective evacuation strategies.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Qinghua Wu, Mu He, Jin-Kao Hao, Yongliang Lu
Summary: This paper studies a variant of the orienteering problem known as the clustered orienteering problem. In this problem, customers are grouped into clusters and a profit is associated with each cluster, collected only when all customers in the cluster are served. The proposed evolutionary algorithm, incorporating a backbone-based crossover operator and a destroy-and-repair mutation operator, outperforms existing algorithms on benchmark instances and sets new records on some instances. It also demonstrates scalability on large instances and has shown superiority over three state-of-the-art COP algorithms. The algorithm is also successfully applied to a dynamic version of the COP considering stochastic travel time.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Bjorn Bokelmann, Stefan Lessmann
Summary: Estimating treatment effects is an important task for data analysts, and uplift models provide support for efficient allocation of treatments. However, evaluating uplift models is challenging due to variance issues. This paper theoretically analyzes the variance of uplift evaluation metrics, proposes variance reduction methods based on statistical adjustment, and demonstrates their benefits on simulated and real-world data.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Congzheng Liu, Wenqi Zhu
Summary: This paper proposes a feature-based non-parametric approach to minimizing the conditional value-at-risk in the newsvendor problem. The method is able to handle both linear and nonlinear profits without prior knowledge of the demand distribution. Results from numerical and real-life experiments demonstrate the robustness and effectiveness of the approach.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Laszlo Csato
Summary: This paper compares the performance of the eigenvalue method and the row geometric mean as two weighting procedures. Through numerical experiments, it is found that the priorities derived from the two eigenvectors in the eigenvalue method do not always agree, while the row geometric mean serves as a compromise between them.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
Article
Management
Guowei Dou, Tsan-Ming Choi
Summary: This study investigates the impact of channel relationships between manufacturers on government policies and explores the effectiveness of positive incentives versus taxes in increasing social welfare. The findings suggest that competition may be more effective in improving sustainability and social welfare. Additionally, government incentives for green technology may not necessarily enhance sustainability.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2024)
