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Article
Computer Science, Artificial Intelligence
Gilberto Rivera et al.
Summary: Many-objective optimization is an area of interest with real-world implications. Preference incorporation into Multi-Objective Evolutionary Algorithms (MOEAs) is a popular approach for solving Many-Objective Optimization Problems (MaOPs). This paper proposes a hyper-heuristic algorithm named Hyper-ACO that searches for the best combination of interval outranking models to solve MaOPs.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Construction & Building Technology
Zixuan Chen et al.
Summary: This paper presents a novel multi-objective mixed integer linear programming model that considers the selection of suitable suppliers, inventory management practices, and order quantities to optimize the trade-off between procurement cost and material delay impacts. The proposed model treats material prices, supplier capacities, and delays as fuzzy scenario-based parameters and is validated through numerical testing. The sensitivity analysis shows the importance of accurate estimation for uncertain parameters. The paper also demonstrates the higher performance of the proposed model compared to deterministic market conditions.
AUTOMATION IN CONSTRUCTION
(2023)
Article
Computer Science, Artificial Intelligence
Joao Luiz Junho Pereira et al.
Summary: In order to tackle challenging engineering problems, the state-of-the-art in multi-objective optimization is shifting towards using meta-heuristics and a posteriori decision-making methods. The Multi-objective Sunflower Optimization (MOSFO) algorithm, inspired by the phototropic life cycle of sunflowers, was created and validated in this work. MOSFO demonstrated significant convergence and coverage capabilities and outperformed other popular and recent algorithms in most of the test functions, making it a promising method for problems with multiple objectives.
Article
Computer Science, Artificial Intelligence
Mohammad Dehghani et al.
Summary: In this paper, a new metaheuristic algorithm called the Coati Optimization Algorithm (COA) is introduced, which simulates coati behavior in nature. The COA is implemented in two phases of exploration and exploitation, mimicking the natural behaviors of coatis when attacking, hunting, and escaping from predators. The performance of COA is evaluated on various objective functions and compared to other well-known metaheuristic algorithms, showing its superior exploration and exploitation capabilities. Furthermore, COA is shown to be effective in real-world applications by solving IEEE CEC-2011 test functions and practical optimization problems.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Engineering, Multidisciplinary
Limu Qin et al.
Summary: This paper proposes a parameter-adaptive blind deconvolution (BD) method (MOBD) based on the multi-objective adaptive guided differential evaluation algorithm (MOAGDE). The main advantage of MOBD is that it only requires bearing speed and type priories to achieve online detection of bearing faults. The results of simulation and experimental signals demonstrate that MOBD significantly outperforms the traditional BD method.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2023)
Article
Engineering, Environmental
Xianhao Meng et al.
Summary: This study aimed to develop an integrated model for identifying the potential environmental loads in waste tire treatment using approaches such as CUM, MFA, and LCA. The results showed a continuous growth in waste tire generation in China, with heavy-duty trucks as the main source. The recycling rate of waste tires in China is currently only 47.07%. The study identified five recycling pathways for waste tires, with reclaimed rubber production being the dominant recycling technology. The study also highlighted the importance of clean energy in mitigating the environmental loads of waste tire recycling.
RESOURCES CONSERVATION AND RECYCLING
(2023)
Article
Computer Science, Artificial Intelligence
Jesus Galeano-Brajones et al.
Summary: This study addresses the high energy consumption issue caused by the massive deployment of 5G mobile communication base stations. It proposes a hybrid multi-objective evolutionary metaheuristics approach that incorporates problem-specific operators to improve energy efficiency and quality of service. The results show that this approach outperforms the canonical algorithms and supports the hypothesis that hybridization can enhance the search process.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Jinhua Zheng et al.
Summary: In researching multi-objective evolutionary algorithms, a preference-based MOEA called MOEA/D-ND is proposed. It uses a normal distribution to generate a weight vector and incorporates the decision-maker's preference information to guide convergence. An angle-based niche selection strategy is adopted to prevent falling into local optima. Experimental results show that this algorithm outperforms in various benchmark problems with 2 to 15 goals.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Xianpeng Wang et al.
Summary: This paper investigates the VM and task joint scheduling problem and proposes a multi-objective mathematical model to optimize makespan, cost, and total tardiness. A problem-specific three-layer encoding approach is designed and a decomposition-based multi-objective evolutionary algorithm with pre-selection and dynamic resource allocation (MOEA/D-PD) is proposed. Experimental results show that the proposed algorithm outperforms existing approaches in the literature.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Interdisciplinary Applications
Vinod Kumar Chauhan et al.
Summary: Supplier selection and order allocation are strategic decisions in supply chain management that have a significant impact on supply chain performance. However, the lack of attention to scalability in the SSOA problem has hindered the adoption of SSOA algorithms by industrial practitioners. This paper proposes a novel double order allocation model with dual-sourcing and penalty constraints in a two-tier supply chain, promoting cooperation between suppliers and facilitating supplier preferences through bidding. Mixed-Integer Programming models and Genetic Algorithm approaches are proposed to solve the problem. A case study demonstrates the effectiveness of the model, showing that Mathematical Programming outperforms Genetic Algorithm in solving SSOA. The model has been successfully deployed in a large international sourcing conference, resulting in significant procurement cost reductions for a manufacturing company.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Computer Science, Theory & Methods
Yan-Yang Cheng et al.
Summary: Multi-task optimization uses knowledge transfer to optimize multiple tasks simultaneously. However, when the number of tasks increases to many-task optimization, the algorithm faces computational burden and degradation of performance due to decreased positive knowledge transfer rate. Existing many-task optimization algorithms have issues in high-dimensional objective space, such as decreased population diversity and slowed optimal solution search speed. To address many-objective and many-task optimization problems, we propose a reference-points-based nondominated sorting approach called MOMaTO-RP. MOMaTO-RP enables knowledge transfer from multiple highly similar tasks and maintains population diversity in high-dimensional objective space, resulting in improved convergence speed. The algorithm is compared to other related algorithms on a classical benchmark set, showing faster convergence speed and better distribution performance.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Chong Peng et al.
Summary: This paper proposes a method to optimize the hyperparameters of support vector regression (SVR) using the multi-objective slime mould algorithm (MOSMA). The performance of MOSMA-SVR is evaluated on two datasets and compared with seven other prediction models, showing better results.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Fengxia Wang et al.
Summary: This paper proposes a new dynamic constrained multi-objective evolutionary algorithm (NDCMOEA) to address the issue of dynamic constrained multi-objective optimization problems. The NDCMOEA integrates constraint deviation values in the objective space and similarity deviation values in the decision space through a constraint handling strategy based on a novel penalty function. The algorithm also employs a dynamic response strategy based on random initialization and an inverse Gaussian process model (IGPM) predictor to handle environmental changes and obtain a better initial population in the new environment.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Jinping Liu et al.
Summary: High-quality flow measurement is crucial in various process industries, and configuring flow sensors is a multi-objective optimization problem. An exact algorithm and a novel multi-objective evolutionary algorithm are proposed for solving this problem in small-scale and large-scale industrial processes, respectively. Experimental results demonstrate the superiority of the proposed algorithms.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Green & Sustainable Science & Technology
Erfan Babaee Tirkolaee et al.
Summary: This study develops a mathematical model to design a sustainable mask Closed-Loop Supply Chain Network (CLSCN) during the COVID-19 outbreak. A multi-objective Mixed-Integer Linear Programming (MILP) model is proposed to address various decisions within a multi-period multi-echelon multi-product supply chain. Sustainable development is considered by minimizing the total cost, total pollution, and total human risk. Two algorithms, MOGWO and NSGA-II, are used to solve the model and find Pareto optimal solutions. The results show that MOGWO performs better in terms of dispersion and solution quality. A real case study and sensitivity analyses are conducted to validate the model. Practical implications and managerial insights are discussed.
JOURNAL OF CLEANER PRODUCTION
(2022)
Article
Green & Sustainable Science & Technology
Madjid Tavana et al.
Summary: This study proposes an integrated multi-objective mixed-integer linear programming model for designing a sustainable closed-loop supply chain network. The model considers cross-docking, location-inventory-routing, time window, supplier selection, order allocation, transportation modes with simultaneous pickup and delivery, and uncertainty. An intelligent simulation algorithm is used to generate CLSC network data, and a fuzzy goal programming approach is employed to solve the model under uncertainty. The results demonstrate the effectiveness of the proposed model.
JOURNAL OF CLEANER PRODUCTION
(2022)
Article
Green & Sustainable Science & Technology
Yigit Kazancoglu et al.
Summary: This article introduces a complex multi-objective optimization model for green dual-channel supply chain network design, aiming to optimize network flow by achieving economic and environmental objectives. By reducing CO2 emissions and particulate matter concentration, environmental goals are met, while minimizing overall costs also meets economic objectives.
JOURNAL OF CLEANER PRODUCTION
(2022)
Article
Automation & Control Systems
Abhishek Kumar Kashyap et al.
Summary: This paper proposes a new strategy for trajectory planning in cluttered terrain using a hybridized controller modeled on modified MANFIS and MOSFO techniques. The proposed technique shows robustness in simulations and improvements compared to default controllers and existing navigational strategies.
Article
Computer Science, Artificial Intelligence
Paulo Pinheiro Junqueira et al.
Summary: Studies have shown that gradually adjusting weight vectors can lead to better results on irregular Pareto fronts. A multi-objective evolutionary algorithm based on decomposition has been proposed to adapt weight vectors to the shape of the Pareto front for overcoming irregularities. Experimental results indicate promising outcomes of the algorithm on irregular Pareto fronts.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Rui Hong et al.
Summary: This paper explores the properties of the two mainstream selection operators in decomposition-based multi-objective evolutionary algorithms (MOEA/D) and proposes an ensemble approach to integrate their merits and improve convergence and diversity. The proposal is compared with twelve baseline algorithms on different test cases and demonstrates its superiority in balancing diversity and convergence.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Hamdi Tolga Kahraman et al.
Summary: This study proposes a robust method to improve the search performance of multi-objective evolutionary algorithms (MOEAs) by using two different archiving mechanisms and a crowding distance-based handling mechanism, aiming to provide sustainable diversity and establish stable exploitation-exploration balance. Experimental results show that the proposed method has about 30% better success rate on multimodal multi-objective optimization problems (MMOPs) compared to its competitors.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Engineering, Environmental
Paulina Wisniewska et al.
Summary: Waste tire management is a major global environmental issue, and finding low-cost and industrial-scale tire recycling methods is gaining more attention. The sustainable development of rubber devulcanization technologies and the appropriate design of cradle-to-cradle loops for rubber goods are considered promising strategies for achieving a higher level of rubber recycling. This work presents an overview of patented waste tire rubber devulcanization technologies and discusses their performance characteristics, environmental aspects, and limitations. The findings show that the reclaimed rubbers described in patents have higher tensile strength and elongation break compared to literature data or commercial products, with significant differences attributed to devulcanization efficiency and rubber treatment conditions. Considering environmental and economic aspects, reactive extrusion is identified as the most promising method for further developing rubber devulcanization technologies.
Article
Computer Science, Artificial Intelligence
Jing Jiang et al.
Summary: The paper introduces a two-stage MOEA named TS-SparseEA tailored to large-scale sparse multiobjective problems. The method integrates prior information into evolution and enables population spreading over the Pareto front through two stages. It uses a binary weight optimization framework in the first stage and an improved evolutionary algorithm with hybrid encoding and specialized matching strategy in the second stage to address LSMOPs effectively.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Social Sciences, Interdisciplinary
Amirhossein Soon et al.
Summary: International laws and increasing consumer awareness have driven changes in supply chain network designs towards sustainability. Reverse logistics can be effective in terms of environmental and economic aspects, making closed-loop supply chain designs necessary. This study addresses the challenge of considering different quality levels for products and consumer tendencies towards environmental issues. A sustainable closed-loop supply chain model is proposed, balancing economic, environmental, and social responsibilities. The robust possibilistic programming method is employed to handle uncertainties in costs and customer demands for different product types and quality levels.
Article
Computer Science, Interdisciplinary Applications
Nathan Adelgren et al.
Summary: The study presents a generic branch-and-bound algorithm for finding all Pareto solutions of a biobjective mixed-integer linear program, primarily focusing on decision space search methods. New algorithms are introduced to handle challenges of dual bounds, node fathoming, presolve, and duality gap measurement. The algorithm aims to improve solution quality and speed in solving optimization problems with two linear objectives.
INFORMS JOURNAL ON COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Wu Lin et al.
Summary: This paper proposes a novel bicriteria assisted adaptive operator selection strategy for decomposition-based multiobjective evolutionary algorithms. By using two operator pools focusing on exploitation and exploration, and two criteria emphasizing convergence and diversity, a good balance between exploitation and exploration during evolutionary search can be achieved. The experimental results show that the proposed B-AOS outperforms existing state-of-the-art adaptive operator selection methods and can significantly improve performance on benchmark problems.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Review
Computer Science, Interdisciplinary Applications
Luttiely Santos Oliveira et al.
Summary: The research primarily focuses on systematizing optimization methods in closed-loop supply chains, with stochastic methods being found as the primary approach. Most publications solely discuss strategic decisions, and the economic dimension is the most investigated in terms of sustainability. Comprehensive research covering all levels of organizational decisions and sustainability dimensions is lacking in the current literature.
JOURNAL OF COMBINATORIAL OPTIMIZATION
(2021)
Article
Engineering, Industrial
Adel Aazami et al.
Summary: This paper develops a new multi-period production-distribution planning for perishable products in a seller-buyer system, optimizing the seller's profit in a three-level supply chain. It includes cooperative actions between factories and distribution centers, a vertical competition involving retailers, and strategies to encourage retailers. The proposed hierarchical heuristic approach shows efficient performance in solving the NP-hard problem.
JOURNAL OF MANUFACTURING SYSTEMS
(2021)
Article
Green & Sustainable Science & Technology
Amirhossein Salehi-Amiri et al.
Summary: Logistics is crucial in the supply chain network, and Closed-loop Supply Chain (CLSC) is gaining attention. A new CLSC network is designed for the walnut industry, minimizing costs using Mixed Integer Linear Programming (MILP). The study shows excellent consistency between the network design, algorithms, and its applicability.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2021)
Article
Computer Science, Artificial Intelligence
Behzad Mosallanezhad et al.
Summary: Recent developments in the food industry have attracted attention from both academic and industrial practitioners. This paper focuses on shrimp as a well-known seafood and proposes a mathematical model for the Shrimp Supply Chain (SSC) to minimize total costs. The study utilizes various metaheuristics and conducts real-world applications to validate the effectiveness of the model and solution methods.
Review
Green & Sustainable Science & Technology
Haseeb Yaqoob et al.
Summary: The article discusses the issue of energy demand in Pakistan and the potential of tire pyrolysis oil as an alternative energy, pointing out its economic and environmental viability.
Review
Management
Charles Audet et al.
Summary: In recent years, there has been significant growth in the development of new algorithms for multiobjective optimization, with a large number of performance indicators introduced to measure the quality of Pareto front approximations. A total of 63 performance indicators are reviewed in this work, categorized into four groups based on their properties: cardinality, convergence, distribution, and spread. Applications of these indicators are also presented.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Jesus Guillermo Falcon-Cardona et al.
Summary: Quality indicators have played a crucial role in promoting the design of new selection mechanisms for multiobjective evolutionary algorithms for almost 20 years. Each indicator-based MOEA has specific search preferences related to its baseline QI, leading to different properties in Pareto front approximations. In order to overcome the limitations of single-QI based IB-MOEAs, an island-based multiindicator algorithm (IMIA) that cooperates multiple IB-MOEAs was proposed. Experiment results show that IMIA outperforms panmictic versions of baseline IB-MOEAs and several state-of-the-art MOEAs, demonstrating robust performance across multiple QIs and invariance in Pareto front shapes.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Zhaopin Su et al.
Summary: The article introduces a MOTRAP model with a predetermined reliability and theoretically deduces new lower bounds on testing time invested in different modules based on the necessary condition for achieving the given reliability. Enhanced constraint-handling techniques (ECHTs) are developed to correct and reduce constraint violation in combination with MOEAs. The proposed ECHTs are evaluated and shown to work well with MOEAs, focusing the search on the feasible region of the predetermined reliability and providing better and more diverse choices in test planning for software project managers.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2021)
Article
Green & Sustainable Science & Technology
Behrooz Khorshidvand et al.
Summary: This paper proposes a two-stage approach to address the issue of Closed-Loop Supply Chains, optimizing decisions on pricing, green quality, and advertising to achieve sustainable objectives such as maximizing profit, reducing CO2 emissions, and improving social impacts. The performance of the model is evaluated through various numerical examples, demonstrating significant improvements in sustainable objectives and reduced computational time for large-scale instances.
JOURNAL OF CLEANER PRODUCTION
(2021)
Review
Green & Sustainable Science & Technology
Azar MahmoumGonbadi et al.
Summary: The paper evaluates the current modeling approaches for CLSC problems in supporting the transition towards Circular Economy, highlighting the focus on economic issues rather than sustainability. It also emphasizes the disregard for the founding principles of Circular Economy and the lack of empirical research in the field.
JOURNAL OF CLEANER PRODUCTION
(2021)
Article
Computer Science, Information Systems
Manoharan Premkumar et al.
Summary: This paper introduces a multi-objective Slime Mould Algorithm (MOSMA) for handling multi-objective optimization problems in industries, combining SMA mechanisms and non-dominated sorting approach to improve solution quality.
Article
Operations Research & Management Science
Bahman Naderi et al.
ANNALS OF OPERATIONS RESEARCH
(2020)
Review
Environmental Sciences
Beate Baensch-Baltruschat et al.
SCIENCE OF THE TOTAL ENVIRONMENT
(2020)
Article
Computer Science, Artificial Intelligence
Wenjiang Song et al.
SWARM AND EVOLUTIONARY COMPUTATION
(2020)
Review
Management
Syed Asif Raza
BENCHMARKING-AN INTERNATIONAL JOURNAL
(2020)
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Engineering, Industrial
Javid Ghahremani Nahr et al.
JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING
(2020)
Article
Engineering, Industrial
Dominik Zimon et al.
INTERNATIONAL JOURNAL FOR QUALITY RESEARCH
(2020)
Article
Computer Science, Artificial Intelligence
Mostafa Hajiaghaei-Keshteli et al.
NEURAL COMPUTING & APPLICATIONS
(2019)
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Computer Science, Artificial Intelligence
Zhihua Cui et al.
SWARM AND EVOLUTIONARY COMPUTATION
(2019)
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Green & Sustainable Science & Technology
Arul Arulrajah et al.
JOURNAL OF CLEANER PRODUCTION
(2019)
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Green & Sustainable Science & Technology
Ehsan Mardan et al.
JOURNAL OF CLEANER PRODUCTION
(2019)
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Management
R. Ghasemy Yaghin et al.
JOURNAL OF MODELLING IN MANAGEMENT
(2019)
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Computer Science, Artificial Intelligence
Reza Moghdani et al.
APPLIED SOFT COMPUTING
(2018)
Article
Computer Science, Artificial Intelligence
Amir Mohammad Fathollahi Fard et al.
APPLIED SOFT COMPUTING
(2018)
Article
Computer Science, Theory & Methods
Jiao-Hong Yi et al.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2018)
Article
Green & Sustainable Science & Technology
Navid Sahebjamnia et al.
JOURNAL OF CLEANER PRODUCTION
(2018)
Article
Engineering, Industrial
Benrong Zheng et al.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2017)
Article
Green & Sustainable Science & Technology
Yan Yan Cui et al.
JOURNAL OF CLEANER PRODUCTION
(2017)
Article
Green & Sustainable Science & Technology
Zhitao Xu et al.
JOURNAL OF CLEANER PRODUCTION
(2017)
Article
Computer Science, Interdisciplinary Applications
Hamed Soleimani et al.
COMPUTERS & INDUSTRIAL ENGINEERING
(2017)
Article
Engineering, Industrial
Mohannad Radhi et al.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2016)
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Management
Esmaeil Keyvanshokooh et al.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2016)
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Green & Sustainable Science & Technology
Mostafa Zohal et al.
JOURNAL OF CLEANER PRODUCTION
(2016)
Review
Management
Kannan Govindan et al.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2015)
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Computer Science, Information Systems
Seyed Hamid Reza Pasandideh et al.
INFORMATION SCIENCES
(2015)
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Green & Sustainable Science & Technology
Kiran Garg et al.
JOURNAL OF CLEANER PRODUCTION
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Review
Green & Sustainable Science & Technology
Payman Ahi et al.
JOURNAL OF CLEANER PRODUCTION
(2015)
Review
Management
Majid Eskandarpour et al.
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
(2015)
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Engineering, Environmental
Zhang Gui Tao et al.
RESOURCES CONSERVATION AND RECYCLING
(2015)
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Economics
Maryam Khatami et al.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2015)
Article
Engineering, Multidisciplinary
Majid Ramezani et al.
APPLIED MATHEMATICAL MODELLING
(2013)
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Automation & Control Systems
Hamed Soleimani et al.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2013)
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Engineering, Industrial
Suhaiza Zailani et al.
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
(2012)
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Engineering, Industrial
H. Moradi et al.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2011)
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Economics
Turan Paksoy et al.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2011)
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Gopalakrishnan Easwaran et al.
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Environmental Sciences
Ezutah Udoncy Olugu et al.
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Computer Science, Artificial Intelligence
Qingfu Zhang et al.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2007)
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TK Varadharajan et al.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2005)
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Computer Science, Artificial Intelligence
K Deb et al.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2002)
