Article
Computer Science, Artificial Intelligence
Gustavo A. Prudencio de Morais, Lucas Barbosa Marcos, Filipe Marques Barbosa, Bruno H. G. Barbosa, Marco Henrique Terra, Valdir Grassi
Summary: This study proposes a robust recursive controller designed via multiobjective optimization to overcome the challenges of system uncertainties, along with a local search method for multiobjective optimization problems. This method is applicable to any established multiobjective evolutionary algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Liang Chen, Hanyang Wang, Darong Pan, Hao Wang, Wenyan Gan, Duodian Wang, Tao Zhu
Summary: In this paper, a dynamic multiobjective evolutionary algorithm (DMOEA) with an adaptive response mechanism selection strategy is proposed. The proposed algorithm combines an adaptive response mechanism selection (ARMS) strategy and a multiobjective evolutionary algorithm based on decomposition (MOEA/D). It is tested on two groups of test instances and compared with other algorithms, and the results show its effectiveness.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Wei Zhou, Liang Feng, Kay Chen Tan, Min Jiang, Yong Liu
Summary: Dynamic multiobjective optimization problem refers to a multiobjective optimization problem that varies over time. To solve this kind of problem, evolutionary search with prediction approaches have been developed to estimate the changes in the problem. However, existing prediction methods only focus on the change in the decision space. In this article, a new approach is proposed that conducts prediction from both the decision and objective spaces. Experimental results show the effectiveness of the proposed method in solving both benchmark and real-world DMOPs.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Information Systems
Yingbo Xie, Junfei Qiao, Ding Wang, Baocai Yin
Summary: The paper proposes a novel multiobjective optimization evolutionary algorithm, MOEA/D-IMA, based on improved adaptive dynamic selection strategies and elite archive strategy to enhance population diversity and convergence; experimental results show that MOEA/D-IMA significantly improves optimization performance when dealing with MOPs.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Fangqing Gu, Haosen Liu, Yiu-ming Cheung, Hai -Lin Liu
Summary: This study proposes an adaptive constraint regulation method to balance the feasibility and convergence of solutions by adjusting the constraint violation of infeasible solutions. Experimental results demonstrate that the proposed method effectively achieves solution balance and improves solution diversity.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Weiwei Yu, Li Zhang, Ning Ge
Summary: This paper studies the dynamic multiobjective flexible scheduling problem in a flexible job shop with uncertain disturbances in the manufacturing environment. It proposes a scheduling scheme based on rescheduling index and adaptive nondominated sorting genetic algorithm (NSGA-II). By improving the mathematical model and proposing a rescheduling hybrid driving mechanism, the shortcomings of the traditional algorithm are addressed, and the effectiveness is verified through experiments.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Automation & Control Systems
Zhengping Liang, Tiancheng Wu, Xiaoliang Ma, Zexuan Zhu, Shengxiang Yang
Summary: In recent years, dynamic multiobjective optimization problems (DMOPs) have gained increasing attention. This article proposes a dynamic multiobjective evolutionary algorithm (DMOEA-DVC) based on decision variable classification, aiming to balance population diversity and convergence. Experimental results comparing DMOEA-DVC with six other algorithms on 33 benchmark DMOPs demonstrate its superior overall performance.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Engineering, Multidisciplinary
Noha Hamza, Ruhul Sarker, Daryl Essam, Saber Elsayed
Summary: The number of research works on dynamic constrained optimization problems has been increasing rapidly over the past two decades. However, no research on dynamic problems with changes in the coefficients of the constraint functions has been reported. In this paper, a new evolutionary framework with multiple novel mechanisms is proposed to deal with such problems, and the results demonstrate its significant contribution in achieving good quality solutions, high feasibility rates, and fast convergence in rapidly changing environments.
ALEXANDRIA ENGINEERING JOURNAL
(2023)
Article
Computer Science, Information Systems
Yingjie Zou, Yuan Liu, Juan Zou, Shengxiang Yang, Jinhua Zheng
Summary: Sparse large scale multiobjective optimization problems (sparse LSMOPs) have a high degree of sparsity in the decision variables of their Pareto optimal solutions. Existing evolutionary algorithms for sparse LSMOPs fail to achieve sufficient sparsity due to inaccurate location of nonzero decision variables and lack of interaction between the locating process and optimizing process. To address this, a dynamic sparse grouping evolutionary algorithm (DSGEA) is proposed, which groups decision variables with comparable numbers of nonzero variables and applies improved evolutionary operators for optimization. DSGEA outperforms current EAs in experiments on real-world and benchmark problems, achieving sparser Pareto optimal solutions with precise locations of nonzero decision variables.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Xuemin Ma, Jingming Yang, Hao Sun, Ziyu Hu, Lixin Wei
Summary: This paper introduces a multiregional co-evolutionary dynamic multiobjective optimization algorithm, which effectively addresses the dynamic multiobjective optimization problems through a combination of multiregional prediction strategy and multiregional diversity maintenance mechanism, achieving good performance in experiments.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Hui Wang, Zichen Wei, Gan Yu, Shuai Wang, Jiali Wu, Jiawen Liu
Summary: This paper proposes a two-stage many-objective evolutionary algorithm, TS-DGPD, which accelerates convergence and maintains population diversity by using cosine distance and Lp norm, and increases selection pressure using dynamic generalized Pareto dominance. Experimental results show that the algorithm performs well in terms of convergence and diversity.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Jinhua Zheng, Qishuang Wu, Juan Zou, Shengxiang Yang, Yaru Hu
Summary: Responding quickly to environmental changes is crucial in solving dynamic multi-objective optimization problems (DMOPs). Most existing methods perform well on predicting individuals but struggle with improving the accuracy of the predicted population. This paper proposes an approach called RVCP, which combines an adjusted reference vector with a multi-objective evolutionary algorithm to predict the population and effectively tackle DMOPs.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Engineering, Mechanical
Qiang He, Zheng Xiang, Peng Ren
Summary: In recent years, the dynamic multiobjective optimization problems have attracted great attention, and reusing experiences to establish prediction models has proven to be useful. However, existing methods overlook the importance of environmental selection. This research proposes a new algorithm based on environmental selection and transfer learning to effectively deal with dynamic multiobjective optimization problems.
NONLINEAR DYNAMICS
(2022)
Article
Automation & Control Systems
Min Jiang, Zhenzhong Wang, Liming Qiu, Shihui Guo, Xing Gao, Kay Chen Tan
Summary: A new memory-driven manifold transfer learning-based evolutionary algorithm for dynamic multiobjective optimization (MMTL-DMOEA) is proposed in this article. By combining the mechanism of memory to preserve the best individuals from the past with the feature of manifold transfer learning to predict the optimal individuals, the algorithm significantly improves the quality of solutions at the initial stage and reduces the computational cost required in existing methods.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Computer Science, Artificial Intelligence
Peidi Wang, Yongjie Ma
Summary: The DMOEA is a powerful solver for DMOPs, but the current algorithms lack strategies in both the environment response and static optimization stages. To address this, a new algorithm was proposed that incorporates different strategies in both stages to balance convergence and diversity. The algorithm uses nondominated solutions-guided evolution in the static optimization stage and fine prediction strategy in the environment response stage to improve performance in dynamic environments.
APPLIED INTELLIGENCE
(2023)
Review
Green & Sustainable Science & Technology
Vishwamitra Oree, Sayed Z. Sayed Hassen, Peter J. Fleming
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2017)
Editorial Material
Substance Abuse
Robin C. Purshouse, Alan Brennan, John Holmes, Petra S. Meier
Article
Energy & Fuels
Vishwamitra Oree, Sayed Z. Sayed Hassen, Peter J. Fleming
Article
Computer Science, Artificial Intelligence
Joao A. Duro, Yiming Yan, Ioannis Giagkiozis, Stefanos Giagkiozis, Shaul Salomon, Daniel C. Oara, Ambuj K. Sriwastava, Jacqui Morison, Claire M. Freeman, Robert J. Lygoe, Robin C. Purshouse, Peter J. Fleming
Summary: The article introduces a tool named Liger designed for non-specialists in industry to perform optimization. Users can interact with Liger through a visual programming language to create optimization workflows and solve multi-objective optimization problems. Liger includes a novel optimization library called Tigon, offering various multi-objective evolutionary algorithms and support for implementing optimization models of different problem types.
APPLIED SOFT COMPUTING
(2021)
Review
Substance Abuse
Jennifer Boyd, Olivia Sexton, Colin Angus, Petra Meier, Robin C. Purshouse, John Holmes
Summary: This study examined the explanatory factors of the alcohol harm paradox in high-income countries, identifying 41 unique explanations grouped into 16 themes. While there are various potential explanations for the AHP, most research focuses on risk behaviors, while other explanations lack empirical testing.
Editorial Material
Substance Abuse
Robin C. Purshouse, Charlotte Buckley, Alan Brennan, John Holmes
Review
Environmental Sciences
Jennifer Boyd, Clare Bambra, Robin C. Purshouse, John Holmes
Summary: There are significant socioeconomic disparities in alcohol-related harm, with lower socioeconomic groups experiencing greater harm despite consuming the same or less alcohol compared to higher socioeconomic groups. Current alcohol research has focused on individual behavior, but there is a call for a new approach drawing on health inequality theories to understand the alcohol harm paradox (AHP).
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2021)
Editorial Material
Substance Abuse
Juergen Rehm, Robin C. Purshouse
Summary: Establishing causality in epidemiological research, especially in relation to alcohol control policy, is a complex issue. The sufficient-component cause model is theoretically clear but involves probabilistic elements in operationalization. Recent advances in agent-based modeling may improve operationalization and have direct implications for alcohol control policy.
DRUG AND ALCOHOL REVIEW
(2021)
Article
Substance Abuse
Klajdi Puka, Charlotte Buckley, Nina Mulia, Robin C. Purshouse, Aurelie M. Lasserre, William Kerr, Jurgen Rehm, Charlotte Probst
Summary: This study estimated the probability of transitioning between different categories of alcohol use among US adults and examined the effects of socio-demographic characteristics on those transitions. The findings suggest that certain demographic subgroups are more likely to transition into or persist in higher levels of alcohol consumption.
Article
Management
Joao A. Duro, Umud Esat Ozturk, Shaul Salomon, Daniel C. Oara, Robert J. Lygoe, Richard Burke, Robin C. Purshouse
Summary: Engineering design optimization problems often require expensive simulation models for evaluation. Bayesian optimization algorithms (BOAs) are proposed as an alternative to traditional multi-objective evolutionary algorithms, and this study investigates their performance on real-world design problems. The BOAs outperform the NSGA-II algorithm, a popular competitor, in terms of convergence and identifying improved designs. The study recommends the use of constrained mixed-integer BOAs for expensive engineering design optimization problems.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Political Science
Clementine Hill O'Connor, Katherine Smith, Ceri Hughes, Petra Meier, Robin Purshouse
Summary: Advocates of inclusive growth believe that it offers a way to combine economic success and social inclusivity, making it highly appealing in various contexts. Through studying three UK policy organizations, we argue that inclusive growth is a flexible concept that can obscure unresolved tensions. While this flexibility helps build alliances, it also makes operationalizing inclusive growth difficult in governance settings that prioritize metrics.
PUBLIC ADMINISTRATION
(2023)
Review
Substance Abuse
Jurgen Rehm, Jayadeep Patra, Alan Brennan, Charlotte Buckley, Thomas K. Greenfield, William C. Kerr, Jakob Manthey, Robin C. Purshouse, Pol Rovira, Paul A. Shuper, Kevin D. Shield
Summary: Alcohol use has been shown to impact various forms of liver disease, causing a significant number of incident cases and deaths. This additional disease burden, often in interaction with other risk factors, is not currently reflected in the classification of alcoholic liver disease. Clinical work and prevention policies need to take this into consideration.
DRUG AND ALCOHOL REVIEW
(2021)
Article
Substance Abuse
Robin C. Purshouse, Alan Brennan, Daniel Moyo, James Nicholls, Paul Norman
ALCOHOL AND ALCOHOLISM
(2017)
Article
Management
Kathrin Klamroth, Sanaz Mostaghim, Boris Naujoks, Silvia Poles, Robin Purshouse, Guenter Rudolph, Stefan Ruzika, Serpil Sayin, Margaret M. Wiecek, Xin Yao
JOURNAL OF MULTI-CRITERIA DECISION ANALYSIS
(2017)
Article
Computer Science, Artificial Intelligence
Rui Wang, Fuxing Zhang, Tao Zhang, Peter J. Fleming
INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION
(2018)