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, Artificial Intelligence
Jesus Guillermo Falcon-Cardona, Raquel Hernandez Gomez, Carlos A. Coello Coello, Ma. Guadalupe Castillo Tapia
Summary: This paper presents a survey of parallel implementations of multi-objective evolutionary algorithms (pMOEAs), discussing their significance in tackling computationally expensive applications, describing taxonomy and methods review, and proposing open questions for further research.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Automation & Control Systems
Sheng-Long Jiang, Qie Liu, I. David L. Bogle, Zhong Zheng
Summary: Scheduling is crucial in steelmaking manufacturing systems. This study introduces a resilient scheduling model that allows for flexible decisions and quick recovery from random disturbances in steelmaking plants. A dynamic multi-objective optimization problem (DMOP) is formulated and a resilient scheduling optimization framework is proposed to solve it. Experimental evidence confirms the effectiveness of the proposed model and framework in solving dynamic scheduling problems in steelmaking plants.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Yongkuan Yang, Pei-Qiu Huang, Xiangsong Kong, Jing Zhao
Summary: This paper proposes a novel constrained multi-objective evolutionary algorithm called CMAOO, which optimizes an (M+1)-objective optimization problem consisting of the original M objective functions and the degree of constraint violation. It constructs a main population and saves all feasible solutions in an external archive. The main population and the external archive are evolved to search the whole space and the feasible regions, respectively, and their offspring update the external archive and the main population separately. Experimental studies show that CMAOO is competitive in solving constrained multi-objective optimization problems compared to four state-of-the-art algorithms.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Yanping Wang, Yuan Liu, Juan Zou, Jinhua Zheng, Shengxiang Yang
Summary: Balancing convergence and diversity in constrained multi-objective optimization problems is challenging. Existing evolutionary algorithms are insufficient, hence a novel algorithm named DTAEA is proposed. DTAEA divides the population's evolutionary process into two phases to improve exploration capability and guide population distribution in feasible regions.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Mohammad A. Abido, Ashraf Elazouni
Summary: The study proposed a modified Multi-Objective Evolutionary Programming (MOEP) algorithm to solve scheduling problems of multi-mode activities, outperforming the benchmarked algorithms of SPEA-II and NSGA-II in terms of diversity and quality of the Pareto optimal set.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Hui Zhang, Xiaojuan Zheng
Summary: This paper proposes a knowledge-driven adaptive evolutionary multi-objective scheduling algorithm (KAMSA) for optimizing makespan and cost of workflow execution in cloud platforms. It divides large-scale decision variables into groups using divide-and-conquer technology to improve evolutionary search efficiency. Comparison with five state-of-the-art competitors demonstrates KAMSA's advantages in 18 out of 20 test cases.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Xianpeng Wang, Hangyu Lou, Zhiming Dong, Chentao Yu, Renquan Lu
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, Information Systems
Natasha Nigar, Muhammad Kashif Shahzad, Shahid Islam, Olukayode Oki, Jose Manappattukunnel Lukose
Summary: This paper proposes a software project scheduling (SPS) model based on employees' skills and task requirements to address the issue of "employee turnover". A novel multi-objective evolutionary optimization algorithm is employed to handle dynamic events, using domain knowledge for population initialization. Experimental results show that hiring new employees with recommended skills based on the proposed approach can reduce project cost without increasing project duration.
Article
Computer Science, Information Systems
Yi Xiang, Jinhua Zheng, Yaru Hu, Yuan Liu, Juan Zou, Qi Deng, Shengxiang Yang
Summary: This paper proposes a multimodal multi-objective algorithm based on weak relationship indicators, which allows the population to retain solutions from different Pareto sets during exploration. An archive based on weak convergence indicators is also introduced to retain excellent solutions.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Artificial Intelligence
Ihsan Elahi, Hamid Ali, Muhammad Asif, Kashif Iqbal, Yazeed Ghadi, Eatedal Alabdulkreem
Summary: This article proposes an improved multi-objective group counseling optimizer algorithm, which shows better performance in solving problems compared to other algorithms. Additionally, applying this algorithm for optimization scheduling can significantly reduce freshwater consumption in the textile dyeing industry.
PEERJ COMPUTER SCIENCE
(2022)
Article
Computer Science, Artificial Intelligence
Yali Wang, Steffen Limmer, Markus Olhofer, Michael Emmerich, Thomas Baeck
Summary: The newly proposed AP-DI-MOEA algorithm can automatically generate preference regions and achieve better solutions within them, especially compared to other MOEA algorithms under the same budget.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Xiangsong Kong, Yongkuan Yang, Zhisheng Lv, Jing Zhao, Rong Fu
Summary: This paper proposes a dynamic dual-population co-evolution multi-objective evolutionary algorithm (DDCMEA) to address the issue of balancing feasibility, convergence, and diversity in constrained multi-objective optimization problems. DDCMEA employs a dynamic dual-population co-evolution strategy to balance convergence and feasibility by adjusting the offspring number of the two populations. In the early stage, the algorithm focuses on convergence and generates more offspring of the first population, while in the late stage, it focuses on feasibility and generates more offspring of the second population. The results show that DDCMEA achieves competitive performance in handling constrained multi-objective optimization problems.
APPLIED SOFT COMPUTING
(2023)
Article
Computer Science, Interdisciplinary Applications
Nitin Srinath, I. Ozan Yilmazlar, Mary E. Kurz, Kevin Taaffe
Summary: This paper presents two metaheuristics for solving the multi-objective scheduling problem in the dyeing process. Through comparative analysis, it is found that hybrid-optimal approaches provide higher quality solutions but suffer from longer computation times.
COMPUTERS & INDUSTRIAL ENGINEERING
(2023)
Article
Computer Science, Interdisciplinary Applications
Mohamed Abouhawwash, Adam M. Alessio
Summary: This study proposes a multi-objective optimization algorithm for PET image reconstruction using a genetic algorithm to generate solutions optimal for multiple tasks. The method demonstrates improved objective function values compared to conventional approaches and identifies a diverse set of solutions in the multi-objective function space.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2021)
Article
Engineering, Multidisciplinary
Ping Mei, Jingzhi Fu, Yunping Liu
MATHEMATICAL PROBLEMS IN ENGINEERING
(2015)
Article
Automation & Control Systems
Jing Zhi Fu
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2013)
Article
Automation & Control Systems
Jingzhi Fu
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2014)
Article
Computer Science, Artificial Intelligence
Xiaoning Shen, Min Zhang, Jingzhi Fu
INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL
(2014)