Article
Computer Science, Artificial Intelligence
Xujian Wang, Fenggan Zhang, Minli Yao
Summary: To handle the inconsistency between reference vectors (RVs) distribution and Pareto front shape in decomposition based multi-objective evolutionary algorithms, researchers have proposed various methods to adjust RVs during the evolutionary process. However, most existing algorithms adjust RVs either in each generation or at a fixed frequency without considering the evolving information of the population. To tackle this issue, the proposed MBRA algorithm adjusts RVs periodically and conditionally based on the improvement rate of convergence degree of subproblems computed through d(1) distance. Extensive experiments validate the effectiveness and competitiveness of MBRA on many-objective optimization problems, especially those with irregular Pareto fronts.
COMPLEX & INTELLIGENT SYSTEMS
(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, Information Systems
Jing Jiang, Fei Han, Jie Wang, Qinghua Ling, Henry Han, Zizhu Fan
Summary: This paper proposes a novel decomposition-based MOEA that considers the ideal point as the global reference point and the nadir point as conditionally the local reference point to improve search diversity. The study shows that the nadir point may aid the ideal point in some cases and be redundant in others.
INFORMATION SCIENCES
(2021)
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
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
Computer Science, Artificial Intelligence
Xin Lin, Wenjian Luo, Naijie Gu, Qingfu Zhang
Summary: This paper investigates the dynamic preferences of decision makers in multiobjective optimization problems and proposes an algorithm framework using a reference point change model. Experimental results show that the algorithm performs well in portfolio optimization problems.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Xin Lin, Wenjian Luo, Naijie Gu, Qingfu Zhang
Summary: In the field of preference-based evolutionary multiobjective optimization, this paper focuses on multiobjective optimization problems with dynamic preferences of the decision maker (DM). Prior to proposing a change model of the reference point to simulate the change of the preference over time, a dynamic preference-based multiobjective evolutionary algorithm framework is designed. Experimental results on portfolio optimization problems demonstrate the superior performance of the proposed algorithm among compared optimization algorithms.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Mengqi Gao, Xiang Feng, Huiqun Yu, Xiuquan Li
Summary: This paper proposes a multiobjective evolutionary algorithm with overlapping decomposition and adaptive reference point selection (MOEA-ODAR) for solving large-scale multiobjective problems. The algorithm decomposes the problem into subproblems using overlapping decomposition and optimizes each subcomponent using adaptive reference point selection. Experimental results demonstrate that the proposed algorithm outperforms existing algorithms in terms of convergence, population distributivity, and computational efficiency.
APPLIED INTELLIGENCE
(2023)
Article
Mathematics, Interdisciplinary Applications
Wenbo Qiu, Jianghan Zhu, Huangchao Yu, Mingfeng Fan, Lisu Huo
Summary: This paper aims to improve a decomposition-based algorithm by designing an adaptive reference vector adjustment strategy. An improved angle-penalized distance (APD) method is developed to better distinguish solutions with sound convergence performance in each subspace.
Article
Computer Science, Information Systems
Maoqing Zhang, Lei Wang, Wuzhao Li, Bo Hu, Dongyang Li, Qidi Wu
Summary: This paper proposes a Many-Objective Evolutionary Algorithm with Adaptive Reference Vector (MaOEA-ARV) that can ensure both the spread and convergence of candidate solutions by dynamically adjusting reference vectors and adaptively partitioning candidate solutions into clusters. Experimental results demonstrate the effectiveness of MaOEA-ARV on test suites with up to 12 objectives.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Keming Jiao, Jie Chen, Bin Xin, Li Li
Summary: Maintaining a balance between convergence and diversity is a challenge for multiobjective evolutionary optimization. An adaptive operator selection and reference vector based evolutionary algorithm (OVEA) is proposed, which chooses crossover operators using Q-learning and assists individual selection with reference vectors. The proposed algorithm achieves close approximation to the true Pareto Front and uniform distribution of objective vectors. Experimental results demonstrate the advantages of OVEA over state-of-the-art algorithms for benchmark problems with different objective numbers ranging from 2 to 10.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Interdisciplinary Applications
Ling Wang, Jing-jing Wang, Enda Jiang
Summary: This paper addresses the energy-aware welding shop scheduling problem and proposes a multiobjective evolutionary algorithm to minimize makespan and energy consumption. By designing initialization heuristics, optimizing search operators, and improving resource allocation strategy, the algorithm's computational efficiency and problem-solving capability are effectively enhanced.
COMPUTERS & INDUSTRIAL ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Lianghao Li, Cheng He, Ran Cheng, Hongbin Li, Linqiang Pan, Yaochu Jin
Summary: This study proposes a fast large-scale multiobjective evolutionary algorithm framework called FLEA, which tackles LSMOPs by guiding the sampling of promising solutions and adaptively allocating computation resources. It significantly improves the search speed and efficiency of conventional MOEAs in solving LSMOPs.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Information Systems
Yuan Liu, Yikun Hu, Ningbo Zhu, Kenli Li, Juan Zou, Miqing Li
Summary: Recently, decomposition-based multiobjective evolutionary algorithms (DMEAs) have become more prevalent than other patterns for solving multiobjective optimization problems. A DMEA with weights updated adaptively (DMEA-WUA) has been developed for problems regarding various Pareto fronts to improve efficiency. The algorithm is suitable for solving problems with various Pareto fronts, including those with regular and irregular shapes.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Hardware & Architecture
Wei Li, Junqing Yuan, Lei Wang
Summary: This paper proposes an enhanced multiobjective evolutionary algorithm, MOEA/D-ANED, with adaptive neighborhood operator and extended distance-based environmental selection to solve many-objective optimization problems. Experimental results show that the proposed algorithm is competitive compared to eight state-of-the-art multiobjective optimization algorithms.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Tapabrata Ray, Tobias Rodemann, Markus Olhofer, Hemant Kumar Singh, Kamrul Hasan Rahi
Summary: In multi/many-objective optimization, identifying solutions of interest (SOIs) is crucial for decision makers. Existing methods focus on measures based on the objective space and overlook the variable space. This paper proposes an approach that considers both objective and variable spaces to identify SOIs, and develops algorithms for different scenarios.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Bing Wang, Hemant Kumar Singh, Tapabrata Ray
Summary: In this study, a surrogate model-based approach is proposed for solving expensive multi-objective optimization problems. The predicted hypervolume maximization is used as the infill criterion, and a more scalable approach based on surrogate corner search is introduced. The proposed approach demonstrates improved performance and reliability in numerical experiments on benchmark problems with up to 5 objectives.
COMPLEX & INTELLIGENT SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Mohammad Mohiuddin Mamun, Hemant Kumar Singh, Tapabrata Ray
Summary: This letter proposes a novel approach to treat bilevel optimization problems as multifidelity optimization problems, reducing the number of function evaluations. By learning the appropriate fidelity to evaluate solutions, redundant evaluations can be significantly reduced, improving the efficiency of problem-solving.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Mohammad Mohiuddin Mamun, Hemant Kumar Singh, Tapabrata Ray
Summary: This study introduces a SAO approach for multi-objective optimization problems, which selectively evaluates the objectives of infill solutions. By leveraging principles of non-dominance and sparse subset selection, the approach aims to improve computational efficiency and identify infill solutions through maximization of probabilistic dominance measure.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Angus Kenny, Tapabrata Ray, Hemant Kumar Singh
Summary: Engineering design optimization often involves using numerical simulations to assess candidate designs. Multifidelity optimization methods aim to optimize computationally expensive problems within a limited budget by efficiently managing low-fidelity and high-fidelity evaluations. This article proposes an improved multifidelity approach called MFITS, which uses an iterative, two-stage scheme to search for good candidates for high-fidelity evaluation based on collective information from previously evaluated designs.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Angus Kenny, Tapabrata Ray, Steffen Limmer, Hemant Kumar Singh, Tobias Rodemann, Markus Olhofer
Summary: Tree-based pipeline optimization tool (TPOT) is a tool used to automatically construct and optimize machine learning pipelines for classification or regression tasks. This study integrates TPOT with Bayesian Optimization (BO) to extend its ability to search across continuous hyper-parameter spaces and improve its performance when there is a limited computational budget.
PROCEEDINGS OF THE 2023 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, GECCO 2023
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Quang Nhat Huynh, Hemant Kumar Singh, Tapabrata Ray
Summary: This paper focuses on uncovering implicit equations in the form of a general linear model. The author proposes improved formulations of linear programming and mixed-integer linear programming to solve this problem. The evaluation on 23 simulated benchmarks shows promising results and demonstrates the suitability of the proposed approach for integration with genetic programming frameworks.
PROCEEDINGS OF THE 2022 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION, GECCO 2022
(2022)
Article
Computer Science, Information Systems
Hemant Kumar Singh, Tapabrata Ray, Md Juel Rana, Steffen Limmer, Tobias Rodemann, Markus Olhofer
Summary: With the increasing popularity of electric vehicles, efficient scheduling of charging becomes crucial. This study extends and improves the charging problem, compares the performance of different solution approaches, and provides insights for further development.
Proceedings Paper
Computer Science, Artificial Intelligence
Hemant Kumar Singh, Juergen Branke
Summary: This research tackles the problem of finding a robust solution to disturbances of decision variables by identifying all stochastically non-dominated solutions. Empirical results demonstrate that the algorithm effectively finds these solutions, and proposed efficiency enhancements significantly reduce the required number of function evaluations while maintaining good solution quality.
PARALLEL PROBLEM SOLVING FROM NATURE - PPSN XVII, PPSN 2022, PT II
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Bing Wang, Hemant Kumar Singh, Tapabrata Ray
Summary: This study focuses on examining the effectiveness of direct solution transfer in bilevel optimization, proposing an improved approach based on correlations between neighboring landscapes. Experimental results show the competitive performance of the correlation-based approach on challenging modified problems, providing insights for future development of efficient transfer-based approaches for bilevel optimization.
2022 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Tapabrata Ray, Mohammad Mohiuddin Mamun, Hemant Kumar Singh
Summary: This study introduces a steady-state evolutionary algorithm for solving multi-modal, multi-objective optimization problems, which is simple in design and does not require additional user-defined parameters compared to existing algorithms. The algorithm demonstrates good performance across various benchmark test suites, and aims to promote the design of simple and efficient algorithms for practical applications.
2022 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)
(2022)
Article
Automation & Control Systems
Md Juel Rana, Forhad Zaman, Tapabrata Ray, Ruhul Sarker
Summary: Community microgrids provide resiliency in smart grid operation and have seen an increased penetration of eco-friendly electric vehicles (EVs) in recent years. However, the uncontrolled charging of EVs can overwhelm electric networks. In this work, an efficient demand response (DR) scheme based on dynamic pricing is proposed to enhance the capacity of microgrids in hosting a large number of EVs. The scheme utilizes a hierarchical optimization framework and employs evolutionary algorithms and mixed-integer linear programming models to solve the problems. The proposed DR scheme is tested on a microgrid system and proves to be effective compared to benchmark pricing policies.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)