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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Peeking beyond peaks: Challenges and research potentials of continuous multimodal multi-objective optimization
Christian Grimme, Pascal Kerschke, Pelin Aspar, et al.
Computers & Operations Research (2021) Vol. 136, pp. 105489-105489
Open Access | Times Cited: 24

Showing 24 citing articles:

Balancing Convergence and Diversity in Objective and Decision Spaces for Multimodal Multi-Objective Optimization
Fei Ming, Wenyin Gong, Ling Wang, et al.
IEEE Transactions on Emerging Topics in Computational Intelligence (2022) Vol. 7, Iss. 2, pp. 474-486
Closed Access | Times Cited: 37
Coevolutionary Framework for Generalized Multimodal Multi-Objective Optimization
Wenhua Li, Xingyi Yao, Kaiwen Li, et al.
IEEE/CAA Journal of Automatica Sinica (2023) Vol. 10, Iss. 7, pp. 1544-1556
Open Access | Times Cited: 28
Multi-Objective Hyperparameter Optimization in Machine Learning—An Overview
Florian Karl, Tobias Pielok, Julia Moosbauer, et al.
ACM Transactions on Evolutionary Learning and Optimization (2023) Vol. 3, Iss. 4, pp. 1-50
Open Access | Times Cited: 24
Multi-modal multi-objective wolf pack algorithm with circumferential scouting and intra-niche interactions
Jia Zhao, Fujun Chen, Renbin Xiao, et al.
Swarm and Evolutionary Computation (2025) Vol. 93, pp. 101842-101842
Closed Access
MO-IOHinspector: Anytime Benchmarking of Multi-objective Algorithms Using IOHprofiler
Diederick Vermetten, Jeroen Rook, Oliver Ludger Preuß, et al.
Lecture notes in computer science (2025), pp. 242-256
Closed Access
Constrained multimodal multi-objective optimization: Test problem construction and algorithm design
Fei Ming, Wenyin Gong, Yueping Yang, et al.
Swarm and Evolutionary Computation (2022) Vol. 76, pp. 101209-101209
Closed Access | Times Cited: 22
Characterization of constrained continuous multiobjective optimization problems: A feature space perspective
Aljoša Vodopija, Tea Tušar, Bogdan Filipic̆
Information Sciences (2022) Vol. 607, pp. 244-262
Open Access | Times Cited: 21
Finding the Set of Nearly Optimal Solutions of a Multi-Objective Optimization Problem
Oliver Schütze, Angel E. Rodríguez-Fernandez, Carlos Segura, et al.
IEEE Transactions on Evolutionary Computation (2024) Vol. 29, Iss. 1, pp. 145-157
Closed Access | Times Cited: 3
Balancing exploration and exploitation in dynamic constrained multimodal multi-objective co-evolutionary algorithm
Guoqing Li, Weiwei Zhang, Caitong Yue, et al.
Swarm and Evolutionary Computation (2024) Vol. 89, pp. 101652-101652
Closed Access | Times Cited: 3
On the Potential of Multi-objective Automated Algorithm Configuration on Multi-modal Multi-objective Optimisation Problems
Oliver Ludger Preuß, Jeroen Rook, Heike Trautmann
Lecture notes in computer science (2024), pp. 305-321
Closed Access | Times Cited: 2
Multi-Objective Hyperparameter Optimization -- An Overview
Florian Karl, Tobias Pielok, Julia Moosbauer, et al.
arXiv (Cornell University) (2022)
Open Access | Times Cited: 10
Reinvestigating the R2 Indicator: Achieving Pareto Compliance by Integration
Lennart Schäpermeier, Pascal Kerschke
Lecture notes in computer science (2024), pp. 202-216
Closed Access | Times Cited: 1
Multi-Modal Multi-Objective Test Problems with an Infinite Number of Equivalent Pareto Sets
Hisao Ishibuchi, Yiming Peng, Lie Meng Pang
2022 IEEE Congress on Evolutionary Computation (CEC) (2022), pp. 1-8
Closed Access | Times Cited: 5
Plotting Impossible? Surveying Visualization Methods for Continuous Multi-Objective Benchmark Problems
Lennart Schäpermeier, Christian Grimme, Pascal Kerschke
IEEE Transactions on Evolutionary Computation (2022) Vol. 26, Iss. 6, pp. 1306-1320
Closed Access | Times Cited: 5
Multimodal multi-objective optimization with multi-stage-based evolutionary algorithm
Tianyong Wu, Fei Ming, Hao Zhang, et al.
Memetic Computing (2023) Vol. 15, Iss. 4, pp. 377-389
Closed Access | Times Cited: 2
Self-organizing map based differential evolution with dynamic selection strategy for multimodal optimization problems
Shihao Yuan, Hong Zhao, Jing Liu, et al.
Mathematical Biosciences & Engineering (2022) Vol. 19, Iss. 6, pp. 5968-5997
Open Access | Times Cited: 4
On the potential of automated algorithm configuration on multi-modal multi-objective optimization problems
Jeroen Rook, Heike Trautmann, Jakob Bossek, et al.
Proceedings of the Genetic and Evolutionary Computation Conference Companion (2022)
Open Access | Times Cited: 4
The Importance of Diversity in the Variable Space in the Design of Multi-Objective Evolutionary Algorithms
Carlos Segura, Joel Chacón Castillo, Oliver Schütze
Applied Soft Computing (2023) Vol. 136, pp. 110069-110069
Closed Access | Times Cited: 2
Hybrid Operator and Strengthened Diversity Improving for Multimodal Multi-Objective Optimization
Guoting Zhang, Yonghao Du, Xiaobin Zhu, et al.
Tsinghua Science & Technology (2024) Vol. 29, Iss. 5, pp. 1409-1421
Open Access
BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems
Jonathan Heins, Jeroen Rook, Lennart Schäpermeier, et al.
Lecture notes in computer science (2022), pp. 192-206
Closed Access | Times Cited: 2
The objective that freed me: a multi-objective local search approach for continuous single-objective optimization
Pelin Aspar, Vera Steinhoff, Lennart Schäpermeier, et al.
Natural Computing (2022) Vol. 22, Iss. 2, pp. 271-285
Open Access | Times Cited: 1
Peak-A-Boo! Generating Multi-objective Multiple Peaks Benchmark Problems with Precise Pareto Sets
Lennart Schäpermeier, Pascal Kerschke, Christian Grimme, et al.
Lecture notes in computer science (2023), pp. 291-304
Closed Access
Obtaining Smoothly Navigable Approximation Sets in Bi-objective Multi-modal Optimization
Renzo J. Scholman, Anton Bouter, Leah R. M. Dickhoff, et al.
Lecture notes in computer science (2022), pp. 247-262
Open Access
Solving Multimodal Problems Using Genetic Algorithms – A Case Study in Electromagnetics
Lavinia Ferariu, Camelia Petrescu
2021 International Conference on Electromechanical and Energy Systems (SIELMEN) (2021), pp. 274-279
Closed Access
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