Article
Automation & Control Systems
Rui Zhang, Shiji Song, Cheng Wu
Summary: This article discusses the hot strip mill scheduling problem in the steel industry, with a focus on reducing energy consumption. The problem is complicated by uncertain rolling processing times, and the robust optimization approach is used to address this issue. Extensive computational tests show that the proposed algorithms can achieve satisfactory solutions under various conditions.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Ying Xu, Huan Zhang, Lei Huang, Rong Qu, Yusuke Nojima
Summary: This research investigates the grid-based decomposition methods in multi-objective optimization to address the issues of diversity and convergence. A new concept of Pareto Front grid and a statistical analysis-based nadir point estimation strategy are proposed to improve computational efficiency. Furthermore, a novel grid-based knee point selection method is proposed. Experimental analysis demonstrates the effectiveness of the proposed PFG-MOEA algorithm.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Information Systems
Wenqing Feng, Dunwei Gong, Zekuan Yu
Summary: A multi-objective evolutionary optimization method is proposed in this study, which takes advantage of online perceiving Pareto front characteristics to efficiently handle complex multi-objective optimization problems. The method extracts information associated with the Pareto front, divides it based on concavity/convexity and continuity, selects different reference points for each sub-front, and designs a multi-objective evolutionary algorithm targeting the characteristics of the Pareto front. Evaluation on 31 test problems shows competitive performance in handling irregular Pareto fronts.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Derya Deliktas, Ender Ozcan, Ozden Ustun, Orhan Torkul
Summary: The study introduces evolutionary algorithms to solve the bi-objective flexible job shop scheduling problem and compares their performance across various configurations. The transgenerational memetic algorithm using weighted sum method outperforms others and achieves the best-known results for almost all instances of bi-objective flexible job shop cell scheduling.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Information Systems
Yuxuan Li, Yu Zhang, Wang Hu
Summary: In this paper, a novel gBest selection strategy based on a new defined virtual generational distance indicator is proposed for selecting the most appropriate Pareto optimal solution, and an adaptive pBest selection strategy based on the evolutionary state is designed. The experimental results show that the MOPSO with these new selection strategies outperforms ten state-of-the-art competitive algorithms on benchmark problems and demonstrates effectiveness in a real-world application.
INFORMATION SCIENCES
(2023)
Article
Engineering, Chemical
Zhiyong Luo, Xintong Liu, Shanxin Tan, Haifeng Xu, Jiahui Liu
Summary: Work-flow scheduling is a NP-hard problem that involves balancing the constraints of time, cost, and quality. To tackle this problem, a multi-objective nonlinear virtual work-flow model is proposed, along with a multi-objective staged scheduling optimization algorithm. This algorithm includes three phases: virtualization, virtual scheduling, and generation. The algorithm outperforms other algorithms in terms of production quality, time constraint, and cost savings.
Article
Computer Science, Artificial Intelligence
Junfeng Tang, Handing Wang, Lin Xiong
Summary: In preference-based multi-objective optimization, knee solutions are the implicit preferred promising solutions. However, finding knee solutions is difficult and computationally expensive. To address this issue, we propose a surrogate-assisted evolutionary multi-objective optimization algorithm that uses surrogate models to replace expensive evaluations. Experimental results show that our proposed algorithm outperforms state-of-the-art knee identification evolutionary algorithms on most test problems within a limited computational budget.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Interdisciplinary Applications
Sheng-Long Jiang, Weigang Li, Xuejun Zhang, Chuanpei Xu
Summary: This study considers the temporal and technical constraints of hot rolling production and proposes a solution based on the Pareto local search algorithm, which can effectively solve multi-objective hot rolling scheduling problems.
COMPUTERS & INDUSTRIAL ENGINEERING
(2022)
Article
Mathematics
Narayanan Ganesh, Rajendran Shankar, Kanak Kalita, Pradeep Jangir, Diego Oliva, Marco Perez-Cisneros
Summary: The effectiveness of a novel optimizer called MOSOS/D for multi-objective problems was investigated in this research. It was based on the symbiotic organisms' search and incorporated a decomposition framework for better performance. Both qualitative and quantitative analyses were conducted, showing the superiority of MOSOS/D in solving large complex multi-objective problems.
Article
Computer Science, Artificial Intelligence
Mustafa Altiok, Erdinc Halis Alakara, Mesut Gunduz, Melih Naci Agaoglu
Summary: This article introduces a multi-objective optimization approach for the optimization problem in hot mix asphalt. By using a fuzzy logic expert system as the objective function, it is able to optimize three outputs simultaneously, providing an effective solution for real-world problems.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Automation & Control Systems
Ziyu Hu, Zihan Li, Hao Sun, Lixin Wei
Summary: Generating feasible solutions and selecting valuable solutions are crucial issues in dealing with complex multi-objective problems. This paper analyzes the mechanism of multi-objective problems based on evolutionary history and environmental information. The proposed MOEA3H algorithm, which incorporates hierarchical decision, heuristic learning, and historical environment, achieves the best performance on a majority of test problems.
Article
Computer Science, Information Systems
Mazen Farid, Rohaya Latip, Masnida Hussin, Nor Asilah Wati Abdul Hamid
Summary: Finding an near-optimum permutation for scheduling work-flows on virtual machines in a multi-cloud environment is a fundamental problem. This paper proposes a new multi-objective minimum weight algorithm to derive the Pareto front, considering conflicting objectives such as reliability, cost, resource utilization, risk probability, and makespan. A new decision-making approach called minimum weight optimization (MWO) is proposed to take into account consumers' needs and service providers' interests.
CMC-COMPUTERS MATERIALS & CONTINUA
(2022)
Article
Computer Science, Artificial Intelligence
Chunteng Bao, Diju Gao, Wei Gu, Lihong Xu, Erik D. Goodman
Summary: This article proposes an adaptive decomposition-based evolutionary algorithm (ADEA) to solve multi-objective optimization problems with complex Pareto fronts. In ADEA, candidate solutions are used as reference vectors, allowing for automatic adjustment according to the shape of the Pareto front. Moreover, reference vectors are successively generated and the corresponding sub-objective space is dynamically decomposed. Empirical results demonstrate the competitive performance of ADEA on various benchmark MOPs.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Operations Research & Management Science
Javad Behnamian, Seyyed Mohammad Taghi Fatemi Ghomi
Summary: This paper discusses a multi-factory scheduling problem with heterogeneous factories and parallel machines, considering multiple objectives such as earliness, tardiness, and total completion time. A mixed-integer linear programming model is used to address the scheduling problem, with existing multi-objective techniques analyzed and a new method applied. Due to the NP-hard nature of the problem, a heuristic algorithm is proposed to generate a set of Pareto optimal solutions, and algorithms are suggested to improve and cover the Pareto front. Computational experiments on randomly generated test problems show the effectiveness of the heuristic algorithm and the model implemented by CPLEX.
RAIRO-OPERATIONS RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Jie Cao, Zesen Yan, Zuohan Chen, Jianlin Zhang
Summary: This paper proposes a novel method called PeCMOEA to address the balance of convergence, diversity, and feasibility in constrained multi-objective optimization problems. It identifies pivotal solutions using an achievement scalarizing function, and formulates adaptive fitness functions to evaluate convergence- and diversity-oriented populations. The method also uses a self-adaptive penalty function to repair infeasible solutions. Experimental results show that PeCMOEA exhibits competitive performance in solving this family of problems.
APPLIED INTELLIGENCE
(2023)
