Article
Computer Science, Artificial Intelligence
Xiaoxia Han, Yingchao Dong, Lin Yue, Quanxi Xu, Gang Xie, Xinying Xu
Summary: In this article, a novel multi-objective optimization algorithm MOSTASA is proposed, which combines state-transition operators and the concept of Pareto dominance to generate and store Pareto optimal solutions, achieving a uniform distribution of solutions. Simulation experiments show that MOSTASA outperforms other algorithms in terms of efficiency and reliability.
APPLIED INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Sumit Kumar, Natee Panagant, Ghanshyam G. Tejani, Nantiwat Pholdee, Sujin Bureerat, Nikunj Mashru, Pinank Patel
Summary: Multi-objective structure optimization is a complex design issue that involves dealing with multiple conflicting objectives and various constraints. A powerful optimizer called MOMVO2arc has been proposed and evaluated for solving large structure optimization problems with less computation time.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Zhenwu Wang, Wenteng Zhang, Yinan Guo, Mengjie Han, Benting Wan, Shangchao Liang
Summary: In this paper, a multi-objective chicken swarm optimization algorithm based on dual external archives and boundary learning strategy (MOCSO-DABL) is proposed. The algorithm aims to improve convergence speed and uniformity of Pareto-optimal solutions. Experimental results demonstrate its significant superiority over other five state-of-the-art algorithms on 14 benchmark functions.
APPLIED SOFT COMPUTING
(2023)
Article
Automation & Control Systems
S. Alireza Davari, Vahab Nekoukar, Cristian Garcia, Jose Rodriguez
Summary: This article introduces an online weighting factor optimization method based on the simulated annealing algorithm, which converges in a few steps using ripple energy as a convergence criterion and does not require cumbersome computations. It is applicable for an induction motor as well as other applications, and has been validated through experimental tests.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Mathematics
Nour Elhouda Chalabi, Abdelouahab Attia, Abderraouf Bouziane, Mahmoud Hassaballah, Abed Alanazi, Adel Binbusayyis
Summary: This article introduces a guided multi-objective equilibrium optimizer (GMOEO) algorithm based on the equilibrium optimizer (EO), which integrates an external archive and candidate population to guide and explore the search space. The algorithm utilizes Pareto dominance and e-dominance principles to update archive solutions and ensure better exploration and diversity. Experimental results demonstrate that the proposed GMOEO algorithm is a powerful tool for solving multi-objective optimization problems (MOPs).
Article
Automation & Control Systems
Soumaia Kahloul, Djaafar Zouache, Boualem Brahmi, Adel Got
Summary: This paper introduces a new evolutionary algorithm MOHGSO, which combines HGSO with multi-objective optimization, using Pareto dominance relation for comparison and proposing efficient archiving and leader selection strategies. Experimental results show that the MOHGSO algorithm outperforms other algorithms in various test functions and engineering design problems.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Automation & Control Systems
Nour Elhouda Chalabi, Abdelouahab Attia, Abderraouf Bouziane, M. Hassaballah
Summary: This paper proposes a modified GMOMPA algorithm to solve multi-objective optimization problems, which incorporates an external archive to store the optimal Pareto set solution and guide particle exploration. The algorithm utilizes the concepts of Pareto dominance and epsilon dominance to obtain non-dominated solutions and update the archive's sorted solutions. Furthermore, it introduces fast non-dominated solution and crowding distance to update particle positions and maintain diversity. The proposed algorithm is evaluated on various benchmark test functions and outperforms state-of-the-art algorithms in most cases.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Engineering, Chemical
Jian Wang, Wenwei Chen, Yan Li, Jin Xu, Weifang Yu, Ajay K. Ray
Summary: The study investigates the performance of SMBR in reversible reactions and finds that both kinetics and adsorptive separation are important factors. Non-isothermal operation can significantly improve unit throughput under certain conditions, while feed concentration and reaction enthalpy have minor effects on the optimal solutions. Decision variables based on flow rate ratios and internal concentration profiles can explain the trends of Pareto optimal solution.
Article
Engineering, Electrical & Electronic
Yundong Tang, Hang Su, Tao Jin, Rodolfo Cesar Costa Flesch
Summary: Magnetic nanofluid hyperthermia (MNH) is a method that uses magnetic nanoparticles (MNPs) exposed to an alternating magnetic field to generate heat and damage malignant cells. This study proposes a control method for an MNH system using a proportional-integral-derivative (PID) control algorithm and dynamically optimizing the PID coefficients with a simulated annealing (SA) algorithm. The proposed system effectively modulates the power dissipation of MNPs and accurately regulates the treatment temperature to the desired value, while also adapting to changes in the nanofluid concentration distribution during therapy.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Automation & Control Systems
Wu Tianwei, An Siguang, Han Jianqiang, Shentu Nanying
Summary: This paper proposes an improved multi-objective evolutionary algorithm using Κ-domination and boundary protection strategy to increase selection pressure and improve the distribution and convergence of solutions. Experimental results demonstrate that the proposed method is competitive and significantly improves efficiency in solving multi-objective problems.
JOURNAL OF SYSTEMS ENGINEERING AND ELECTRONICS
(2022)
Article
Computer Science, Information Systems
Vikas Palakonda, Rammohan Mallipeddi, Ponnuthurai Nagaratnam Suganthan
Summary: Based on the proposed ENMOEA framework, a competitive ensemble approach which combines advantages of different MOEAs, this study demonstrates robustness and effectiveness to algorithm parameters, as well as improvement in ensemble performance with increased diversity of constituent algorithms.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Ayla Gulcu, Zeki Kus
Summary: The study models a CNN hyper-parameter optimization problem as a bi-criteria optimization problem and develops a MOSA algorithm for high-quality solutions. The MOSA algorithm performs better in a multi-objective setting on the CIFAR-10 dataset compared to the single-objective SA method.
PEERJ COMPUTER SCIENCE
(2021)
Article
Biochemical Research Methods
Surama Biswas, Sriyankar Acharyya
Summary: In this study, four algorithms based on the Archived Multi Objective Simulated Annealing (AMOSA) framework were proposed for parameter learning in Recurrent Neural Network (RNN) modeling of Gene Regulatory Network (GRN). Comparative studies on performance metrics, including recall, precision and f1 score, showed that the modified algorithms, AMOFSA and AMOTSA, outperformed AMOSAR and other state-of-the-art algorithms in terms of the number of GRNs obtained in the final non-dominated front.
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
(2021)
Article
Computer Science, Interdisciplinary Applications
Weixun Yong, Jian Zhou, Danial Jahed Armaghani, M. M. Tahir, Reza Tarinejad, Binh Thai Pham, Van Van Huynh
Summary: This research develops three soft-computing techniques for predicting the ultimate-bearing capacity of a pile, with the SA-GP model performing the best in terms of correlation coefficient and mean square error. The pile's Q(ult) is most affected by the pile cross-sectional area and pile set.
ENGINEERING WITH COMPUTERS
(2021)
Article
Computer Science, Artificial Intelligence
Sumanto Dutta, Animesh Das, Bidyut Kr. Patra
Summary: Mobility analysis is essential for many applications, and clustering is a crucial technique in developing these applications. Traditional clustering techniques have limitations, such as being trapped in local optima and less effective in varying densities. To overcome these issues, a new multi-objective criterion-based evolutionary clustering method called CLUSTMOSA is proposed. It utilizes archived multi-objective simulated annealing (AMOSA) for clustering and improves the search capability. The performance of CLUSTMOSA, along with a new segmentation method, is compared with state-of-the-art methods, and the experiments prove its superiority.
APPLIED SOFT COMPUTING
(2022)
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
Biochemical Research Methods
Snehalika Lall, Sumanta Ray, Sanghamitra Bandyopadhyay
Summary: Annotation of cells in single-cell clustering requires a homogeneous grouping of cell populations. sc-CGconv is an unsupervised feature extraction and clustering approach that utilizes copula correlation and graph convolution network to formulate and aggregate cell-cell relationships, which can identify homogeneous clusters with small sample sizes, model the expression co-variability of a large number of genes, preserve cell-to-cell variability, and provide a topology-preserving embedding of cells in low dimensional space.
PLOS COMPUTATIONAL BIOLOGY
(2022)
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
Computer Science, Artificial Intelligence
Sumanta Ray, Snehalika Lall, Anirban Mukhopadhyay, Sanghamitra Bandyopadhyay, Alexander Schoenhuth
Summary: This article introduces the use of artificial intelligence and deep learning techniques to screen drug repositories and find therapeutic options against COVID-19. By constructing a comprehensive molecular interaction network and predicting connections between drugs and human proteins, novel host-directed therapy options are established, providing a new approach for fighting the virus.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(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)
Article
Geochemistry & Geophysics
Monidipa Das, Soumya K. Ghosh, Sanghamitra Bandyopadhyay
Summary: This article proposes a MARINE model to address the catastrophic forgetting issue that neural networks encounter when trained in a sequential manner, particularly in the presence of a large degree of subregional variations or heterogeneity in spatial zones. MARINE demonstrates competitive results in spatio-temporal prediction tasks and outperforms other methods in avoiding catastrophic forgetting, especially in highly heterogeneous spatial environments.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(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)
