Article
Operations Research & Management Science
Juan Li, Bin Xin, Panos M. Pardalos, Jie Chen
Summary: This paper investigates the uncertain stochastic resource allocation problem and proposes bi-objective models to control the risk brought by uncertainties. Two solutions are presented for RAPs and MWTA problems, and two evolutionary algorithms are applied to solve the formulated bi-objective problem. Experimental results show that DMOEA-epsilon C outperforms MOEA/D-AWA on the majority of test instances.
ANNALS OF OPERATIONS RESEARCH
(2021)
Article
Automation & Control Systems
Huangke Chen, Ran Cheng, Witold Pedrycz, Yaochu Jin
Summary: This paper proposes a method to solve multiobjective optimization problems through multi-stage evolutionary search, highlighting convergence and diversity in different search stages. The algorithm balances and addresses the issues in multiobjective optimization through two stages.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Computer Science, Information Systems
Biao Xu, Dunwei Gong, Yong Zhang, Shengxiang Yang, Ling Wang, Zhun Fan, Yonggang Zhang
Summary: In this study, a cooperative co-evolutionary algorithm is proposed to effectively solve multi-objective optimization problems with changing decision variables by dynamically grouping them. The experimental results demonstrate that the presented method outperforms other methods in terms of diversity, convergence, and spread of solutions.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Yongkuan Yang, Jianchang Liu, Shubin Tan
Summary: Many MOEAs are developed to solve CMOPs, but they encounter low efficiency for steady-state CMOPs. This paper proposes a multi-objective evolutionary algorithm named FACE, which maintains the known feasible solution in the second population and evolves together with the main population. Performance comparisons show the efficiency and scalability of FACE for steady-state CMOPs.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Wanting Yang, Jianchang Liu, Wei Zhang, Xinnan Zhang
Summary: This paper proposes a resource allocation-based multi-objective optimization evolutionary algorithm to address the curse of dimensionality in large-scale multi-objective optimization problems. The algorithm divides decision variables into convergence-related variables and diversity-related variables using a proposed variable classification method. It then applies resource allocation-based convergence optimization for the former and diversity optimization for the latter. Experimental results show that the proposed algorithm performs competitively compared to state-of-the-art algorithms.
Article
Computer Science, Information Systems
Yiming Wang, Weifeng Gao, Maoguo Gong, Hong Li, Jin Xie
Summary: This paper proposes a new two-stage based evolutionary algorithm for balancing convergence and diversity in multi-objective optimization problems. Experimental results show that the algorithm has competitive performance.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Mingming Xia, Qing Chong, Minggang Dong
Summary: The tradeoff between feasibility and optimality is critical in handling CMOPs. To address this issue, we propose a novel CMOEA-TSRA that considers both feasibility and optimality throughout the entire evolutionary process by dividing it into two stages. In the first stage, fewer individuals are allocated to roughly exploit the discovered feasible regions and more individuals are allocated to explore potentially optimal feasible regions. In the second stage, the allocation is adjusted to further exploit the discovered feasible regions and explore potentially optimal feasible regions.
SWARM AND EVOLUTIONARY COMPUTATION
(2023)
Article
Multidisciplinary Sciences
Omar Dib
Summary: This work proposes a novel three-Phase Hybrid Evolutionary Algorithm (3PHEA) that incorporates the Lin-Kernighan Heuristic, Non-Dominated Sorting Genetic Algorithm, and Pareto Variable Neighborhood Search to solve the Bi-objective Traveling Salesman Problem (BTSP). The algorithm is compared with three existing approaches on 20 BTSP instances of varying difficulty and size, and evaluated using multiple performance indicators. Experimental results show that the 3PHEA method outperforms existing approaches by achieving over 80% coverage of the true Pareto fronts.
SCIENTIFIC REPORTS
(2023)
Article
Computer Science, Artificial Intelligence
Yinan Shao, Jerry Chun-Wei Lin, Gautam Srivastava, Dongdong Guo, Hongchun Zhang, Hu Yi, Alireza Jolfaei
Summary: This article introduces a method for optimizing deep reinforcement learning models using neural evolutionary algorithms to solve combinatorial optimization problems. The proposed end-to-end multi-objective neural evolutionary algorithm demonstrates competitive and robust performance on the classic travel salesman problem and knapsack problem, and also performs well in inference time.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Chunliang Zhao, Yuren Zhou, Yuanyuan Hao
Summary: This paper proposes a decomposition-based evolutionary algorithm adopting dual adjustments to address MaOPs with irregular PFs. It divides an MaOP into a set of subproblems and selects appropriate solutions using specified scalarizing functions. By updating the scalarizing functions and weight vectors, and introducing reminding solutions for fine-tuning, the algorithm performs well in experiments.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Yi Zhao, Jianchao Zeng, Ying Tan
Summary: The proposed method combines reference vector guided evolutionary algorithm and radial basis function networks to optimize individuals and introduces an infill strategy, showing competitive performance in solving computationally expensive many-objective optimization problems.
APPLIED SOFT COMPUTING
(2021)
Article
Automation & Control Systems
Lingjie Li, Qiuzhen Lin, Zhong Ming, Ka-Chun Wong, Maoguo Gong, Carlos A. Coello Coello
Summary: This article proposes an immune-inspired resource allocation strategy to better balance convergence and diversity in many-objective optimization. By defining the diversity distances of solutions, resource allocation is realized using an immune cloning operator to explore sparse regions of the search space. A novel archive update mechanism is also designed to provide high-quality solutions. The experimental results validate the superiority of this method in solving complex MOPs with 5 to 15 objectives.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Information Systems
Zhiwei Xu, Kai Zhang, Juanjuan He, Xiaoming Liu
Summary: In this research, a novel membrane-inspired evolutionary framework with a hybrid dynamic membrane structure is proposed to solve multi-objective multi-task optimization problems. The algorithm improves convergence and diversity, and reduces negative information transfer through the information molecule concentration vector.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Chunliang Zhao, Yuren Zhou, Xinsheng Lai
Summary: This paper develops an integrated information-based evolutionary framework for solving multi-scenario multi-objective optimization problems. Two integration algorithms, scenario-based dominance principle evolutionary algorithm and decomposition-based evolutionary algorithm, are constructed using the framework, and crucial components are developed under the multi-scenario environment.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Jie Cao, Jianlin Zhang, Fuqing Zhao, Zuohan Chen
Summary: A novel algorithm named MOEA/D-TS is proposed in this paper, which effectively solves multi-objective optimization problems through two-stage evolution strategies. The performance of the algorithm is validated in real world problems and shows advantages in terms of convergence and diversity over other algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Green & Sustainable Science & Technology
Anna Raschke, J. Sebastian Hernandez-Suarez, A. Pouyan Nejadhashemi, Kalyanmoy Deb
Summary: Bioenergy is increasingly seen as a viable alternative to fossil fuels, with various bioenergy feedstocks being considered environmentally friendly solutions that can positively impact stream health and carbon sequestration. However, most evaluations of bioenergy feedstocks have not taken a holistic approach to understanding the implications of bioenergy crop production. Using multi-objective optimization, this study in a Michigan watershed identifies optimal trade-off solutions for stream health, environmental emissions/carbon footprint, and economic feasibility. The results suggest that a diverse mix of rotations is necessary to optimize all three objectives, demonstrating the importance of a multi-objective approach in bioenergy crop production evaluation.
Article
Computer Science, Artificial Intelligence
Zhichao Lu, Ian Whalen, Yashesh Dhebar, Kalyanmoy Deb, Erik D. Goodman, Wolfgang Banzhaf, Vishnu Naresh Boddeti
Summary: This study proposes an evolutionary algorithm for searching neural architectures, which fills a set of architectures through genetic operations to approximate the entire Pareto frontier, improves computational efficiency, and reinforces shared patterns among past successful architectures through Bayesian model learning. The method achieves competitive performance in image classification tasks, while considering multiple objectives.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Zhichao Lu, Gautam Sreekumar, Erik Goodman, Wolfgang Banzhaf, Kalyanmoy Deb, Vishnu Naresh Boddeti
Summary: NAT method efficiently generates task-specific models competitive under multiple conflicting objectives by learning task-specific supernets and integrating online transfer learning and many-objective evolutionary search. It significantly improves performance in various image classification tasks, particularly on small-scale fine-grained datasets.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Sukrit Mittal, Dhish Kumar Saxena, Kalyanmoy Deb, Erik D. Goodman
Summary: This article presents an approach that uses machine learning to learn the relationships between top solutions in optimization problems, helping offspring solutions progress. The method involves balancing tradeoffs between convergence and diversity, using the Random Forest method, and changing the application of machine learning models.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Engineering, Multidisciplinary
Abhiroop Ghosh, Kalyanmoy Deb, Erik Goodman, Ronald Averill
Summary: This article introduces a multi-objective evolutionary algorithm framework that combines problem-specific knowledge and online innovization approaches to solve real-world large-scale multi-objective problems. The framework utilizes the knowledge of experienced users and the inter-variable relationships in good solutions to improve candidate solutions through repair operators for faster finding of good solutions.
ENGINEERING OPTIMIZATION
(2022)
Article
Engineering, Multidisciplinary
Bhuvan Khoshoo, Julian Blank, Thang Q. Pham, Kalyanmoy Deb, Shanelle N. Foster
Summary: This article investigates a complex electric machine design problem and proposes a computationally efficient optimization method based on evolutionary algorithms. The method generates feasible solutions using a repair operator and addresses time-consuming objective functions by incorporating surrogate models. The study successfully establishes the superiority of the proposed method in optimization tasks.
ENGINEERING OPTIMIZATION
(2023)
Article
Computer Science, Artificial Intelligence
Kalyanmoy Deb, Zhichao Lu, Ian Kropp, J. Sebastian Hernandez-Suarez, Rayan Hussein, Steven Miller, A. Pouyan Nejadhashemi
Summary: Many societal and industrial problems can be decomposed into hierarchical subproblems. This article introduces a new evolutionary approach that allows upper level decision makers to analyze the impact of lower level decision making when choosing a solution. This method can be applied to similar hierarchical management problems to achieve minimum deviation and more reliable outcomes.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Dhish Kumar Saxena, Sukrit Mittal, Sarang Kapoor, Kalyanmoy Deb
Summary: This article proposes a high-fidelity-dominance principle that factors in all three critical human decision-making elements and implements it in a computationally efficient many-objective evolutionary algorithm (MaOEA). The experimental results show statistically better performance in about 60% of instances, making it practical and worthy of further investigation and application.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Deepanshu Yadav, Palaniappan Ramu, Kalyanmoy Deb
Summary: Evolutionary multi-objective optimization (EMO) algorithms are commonly used to solve multi- and many-objective optimization problems and find the Pareto front. It is important for decision makers to consider objective vectors that are less sensitive to perturbations in design variables and problem parameters. This paper proposes and evaluates different algorithmic implementations that integrate multi-objective optimization, robustness consideration, and multi-criterion decision-making. The results provide insights for developing more efficient multi-objective robust optimization and decision-making procedures for practical problems with uncertainties.
PROCEEDINGS OF THE 2023 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, GECCO 2023
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Ritam Guha, Wei Ao, Stephen Kelly, Vishnu Boddeti, Erik Goodman, Wolfgang Banzhaf, Kalyanmoy Deb
Summary: Automated machine learning (AutoML) greatly simplifies architecture engineering by building machine-learning algorithms using basic primitives. AutoML-Zero expands on this concept by exploring novel architectures beyond human knowledge without utilizing feature or architectural engineering. However, it currently lacks a mechanism to satisfy real-world application constraints. We propose MOAZ, a multi-objective variant of AutoML-Zero, which trades off accuracy with computational complexity, distributes solutions on a Pareto front, and efficiently explores the search space.
PROCEEDINGS OF THE 2023 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, GECCO 2023
(2023)
Article
Computer Science, Artificial Intelligence
Deepanshu Yadav, Palaniappan Ramu, Kalyanomy Deb
Summary: This paper proposes an approach that combines the Pareto-Race MCDM method with the interpretable self-organizing map (iSOM) based visualization method. The approach assists decision makers in multi-criteria decision-making by generating iSOM plots of objectives and considering metrics such as closeness to constraint boundaries, trade-off value, and robustness. The proposed iSOM-enabled Pareto-Race approach improves the quality of preferred solutions.
APPLIED SOFT COMPUTING
(2023)
Article
Mathematics, Interdisciplinary Applications
Kalyanmoy Deb, Matthias Ehrgott
Summary: This paper analyzes the properties of generalized dominance structures and introduces the concept of anti-dominance structure to explain the identification of resulting optimal solutions. The anti-dominance structure is applied to analyze the optimal solutions of commonly used dominance structures.
MATHEMATICAL AND COMPUTATIONAL APPLICATIONS
(2023)
Article
Automation & Control Systems
Yashesh Dhebar, Kalyanmoy Deb, Subramanya Nageshrao, Ling Zhu, Dimitar Filev, Yashesh Deepakkumar Dhebar
Summary: This article proposes a nonlinear decision-tree approach to approximate and explain the control rules of a pretrained black-box deep reinforcement learning agent. The approach uses nonlinear optimization and a hierarchical structure to find simple and interpretable rules while maintaining comparable closed-loop performance.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Abhiroop Ghosh, Yashesh Dhebar, Ritam Guha, Kalyanmoy Deb, Subramanya Nageshrao, Ling Zhu, Eric Tseng, Dimitar Filev
Summary: This paper explores the interpretability of DNN/RL systems by using NLDT framework, which simplifies the state-action logic and provides simplistic rules to explain the system's decisions. Applying this methodology to a mountain car control problem, the study derives analytical decision rules involving six critical cars and further simplifies them for English-like interpretation of the lane change problem.
2021 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI 2021)
(2021)
Review
Mathematics, Interdisciplinary Applications
Kalyanmoy Deb, Proteek Chandan Roy, Rayan Hussein
Summary: This paper discusses various metamodeling frameworks for multiobjective optimization problems, some of which independently find Pareto-optimal solutions while others find multiple Pareto-optimal solutions simultaneously. By statistically comparing the accuracy of metamodels, an adaptive switching based metamodeling (ASM) approach is proposed and outperforms individual frameworks in multiobjective optimization tasks.
MATHEMATICAL AND COMPUTATIONAL APPLICATIONS
(2021)
