Article
Computer Science, Artificial Intelligence
Ke Shang, Hisao Ishibuchi, Linjun He, Lie Meng Pang
Summary: This article provides a comprehensive survey on the hypervolume indicator widely used in the field of evolutionary multiobjective optimization. The goal is to help researchers deepen their understanding of the principles and applications of the hypervolume indicator, and to promote further utilization of it.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2021)
Article
Automation & Control Systems
Bing-Chuan Wang, Han-Xiong Li, Qingfu Zhang, Yong Wang
Summary: This paper utilizes decomposition-based multiobjective optimization to solve constrained optimization problems and introduces a restart strategy to enhance the optimization performance of the population. Extensive experiments on benchmark test functions demonstrate that the proposed method shows better or at least competitive performance against other state-of-the-art methods.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Geochemistry & Geophysics
Chuanlong Ye, Fazhi He, Jinkun Luo, Lyuyang Tong, Xiaoxin Gao, Tongzhen Si, Linkun Fan
Summary: This article proposes a multistrategy evolutionary multiobjective method based on roulette wheel selection and the genetic algorithm (RWS-GA) for hyperspectral endmember extraction. The method designs two parallel algorithms corresponding to global exploration and local exploitation. Experimental results show that the proposed method outperforms other endmember extraction methods.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Computer Science, Artificial Intelligence
Lin Shi, Yanyan Tan, Zeyuan Yan, Lili Meng, Li Liu
Summary: This study proposes a multiobjective evolutionary algorithm based on decomposition, which divides weight vectors into groups and assigns different reproduction operators to each group to handle complex multiobjective optimization problems. Comparative experiments have shown that this strategy has better performance.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Zeneng She, Wenjian Luo, Xin Lin, Yatong Chang, Yuhui Shi
Summary: This paper focuses on the study of multiparty multiobjective optimization problems (MPMOPs) and proposes a new algorithm OptMPNDS3 to solve these problems. Comparisons with other algorithms on a problem suite show that OptMPNDS3 performs strongly and similarly.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Engineering, Electrical & Electronic
Qinghui Xu, Sanyou Zeng, Fei Zhao, Ruwang Jiao, Changhe Li
Summary: For antenna designers, constrained multiobjective optimization problems are recommended as the most suitable type to model antenna arrays, and a dynamic constrained multiobjective evolutionary algorithm is a general and efficient algorithm that can solve various optimization problems.
IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION
(2021)
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
Guo Yu, Lianbo Ma, Yaochu Jin, Wenli Du, Qiqi Liu, Hengmin Zhang
Summary: This article provides a comprehensive survey of knee-oriented optimization, focusing on the suggestion to target naturally interesting regions in solving multi-objective optimization problems.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(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
Automation & Control Systems
Kai Zhang, Gary G. Yen, Zhenan He
Summary: In this article, a recursive evolutionary algorithm EvoKnee(R) is proposed to directly search for global knee solutions and multiple local knee solutions using the minimum Manhattan distance approach, instead of a large number of Pareto optimal solutions. Unlike traditional approaches, only nondominated solutions in rank one are preserved in each generation, reducing computational cost and allowing quick convergence to knee solutions.
IEEE TRANSACTIONS ON CYBERNETICS
(2021)
Article
Computer Science, Artificial Intelligence
Christian Cintrano, Javier Ferrer, Manuel Lopez-Ibanez, Enrique Alba
Summary: In the traffic light scheduling problem, evaluating candidate solutions through simulation under different scenarios is crucial. This study explores the combination of IRACE and evolutionary operators for optimizing traffic light programs. By reviewing previous research, new hybrid algorithms are proposed based on the best performing evolutionary operators. The experimental analysis on a realistic case study shows that the hybrid algorithm consisting of IRACE and differential evolution outperforms other algorithms when the simulation budget is low. However, IRACE performs better than the hybrids for a high simulations budget, despite an increase in optimization time.
EVOLUTIONARY COMPUTATION
(2023)
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
Weiyu Chen, Hisao Ishibuchi, Ke Shang
Summary: This article discusses the importance of subset selection in evolutionary multiobjective optimization and proposes efficient greedy algorithms based on submodular property. Computational experiments show that these algorithms are faster than the standard greedy algorithms and also contribute to the research on performance indicators.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Yatong Chang, Wenjian Luo, Xin Lin, Zhen Song, Carlos A. Coello Coello
Summary: This paper proposes the definition of the biparty multiobjective optimal power flow (BPMOOPF) problem and introduces a novel evolutionary biparty multiobjective optimization algorithm (BPMOOPF-EA) to solve the problem. Experimental results show that BPMOOPF-EA outperforms other algorithms in solving the MOOPF problem.
APPLIED SOFT COMPUTING
(2023)
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
Computer Science, Artificial Intelligence
K. Liagkouras, K. Metaxiotis
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
(2015)
Article
Computer Science, Artificial Intelligence
K. Liagkouras, K. Metaxiotis
JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE
(2017)
Article
Management
K. Liagkouras, K. Metaxiotis
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2018)
Review
Computer Science, Artificial Intelligence
K. Metaxiotis, K. Liagkouras
EXPERT SYSTEMS WITH APPLICATIONS
(2012)
Article
Computer Science, Artificial Intelligence
K. Liagkouras, K. Metaxiotis
EXPERT SYSTEMS WITH APPLICATIONS
(2014)
Article
Operations Research & Management Science
K. Liagkouras, K. Metaxiotis, G. Tsihrintzis
Summary: More and more companies are being pressured by the public to disclose information about their performance on environmental, social and governance (ESG) issues. However, there have been very few studies on the optimal ways to construct socially responsible portfolios. This study fills this gap by introducing an algorithm that performs screening and optimization processes to build ESG compliant portfolios. The study finds that investors who prioritize environmental and social impact may have to sacrifice some welfare to select asset combinations with subordinate returns and risks compared to other available investment opportunities.
ANNALS OF OPERATIONS RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
K. Liagkouras, K. Metaxiotis
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS
(2015)
Proceedings Paper
Engineering, Electrical & Electronic
K. Metaxiotis, K. Liagkouras
2013 IEEE 20TH INTERNATIONAL CONFERENCE ON ELECTRONICS, CIRCUITS, AND SYSTEMS (ICECS)
(2013)