Multi-Objective Evolutionary Optimization

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5 papers in the last four weeks, up 67% on the four weeks before. 0.0% of all new papers.

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When Fireflies Cluster; Enhancing Automatic Clustering via Centroid-Guided Firefly Optimization
May 18, 2026MKA Ariyaratne, Azwirman Gusrialdi, Yury Nikulin +1Multi-Objective Evolutionary Optimization Metaheuristic Optimization
Department of Computer Science, Faculty of Applied Sciences, University of Sri Jayewardenepura, Sri Lanka · Faculty of Engineering and Natural Sciences,Tampere University, Tampere, Pirkanmaa, Finland · Department of Mathematics and Statistics, University of Turku, Finland
Mapping the Fitness Landscape: A Structure-Guided Approach to Multi-Modal Optimization
May 18, 2026Meng Xiang, Pei YanEvolutionary Optimization Multi-Objective Evolutionary Optimization
University of Aizu Aizuwakamatsu, Japan
Multi-Party Multi-Objective Optimization as Consensus Search: Runtime Analysis of Cross-Party Recombination
May 17, 2026Xiaolei Fang, Peilan Xu, Wenjian LuoEvolutionary Optimization Multi-Objective Evolutionary Optimization
School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China · Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, Institute of Cyberspace Security, School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China.
General-Purpose Co-Evolutionary Construction of Parallel Algorithm Portfolios for Multi-Objective Binary Optimization
May 15, 2026Zhiyuan Wang, Shengcai Liu, Shaofeng Zhang +1Multi-Objective Evolutionary Optimization Synthetic Benchmark Generation
Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China · Zhongguancun Academy, Beijing 100094, China
First Mathematical Runtime Analyses of Multi-Objective Evolutionary Algorithms for Multi-Valued Decision Variables
May 14, 2026Mingfeng Li, Zheng Cheng, Weijie Zheng +1Evolutionary Optimization Multi-Objective Evolutionary Optimization
School of Computer Science and Technology, National Key Laboratory of Smart Farm Technologies and Systems, International Research Institute for Artiﬁcial Intelligence, Harbin Institute of Technology, Shenzhen · 2Pengcheng Laboratory, Shenzhen, China · 3Laboratoire d’Informatique (LIX), CNRS, ´Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau, France
TRUST-TAEA: A trustworthiness-guided two-archive evolutionary algorithm with variable-grouping sparse search for large-scale multi-objective optimization
May 13, 2026Junyi Cui, Chao Min, Stanisław Migórski +2Evolutionary Optimization Multi-Objective Evolutionary Optimization
School of Sciences, Southwest Petroleum University, Chengdu, 610500, China · Institute for Artificial Intelligence, Southwest Petroleum University, Chengdu, 610500, China · Jagiellonian University in Krakow, Faculty of Mathematics and Computer Science, Krakow, 30348, Poland +1
ToolMol: Evolutionary Agentic Framework for Multi-objective Drug Discovery
May 12, 2026Andrew Y. Zhou, Sharvaree Vadgama, Sumanth Varambally +3Molecular Optimization AI Agents for Scientific Discovery
Department of Computer Science and Engineering, UC San Diego, La Jolla, California, United States · Department of Computer Science, Stanford University, Stanford, California · Skaggs School of Pharmacy and Pharmaceutical Sciences, UC San Diego, La Jolla, California
On the Impact of Crossover in Many-Objective Optimization: A Runtime Analysis of NSGA-III
May 11, 2026Andre OprisEvolutionary Optimization Multi-Objective Evolutionary Optimization
Chair of Algorithms for Intelligent Systems, University of Passau, Passau, Germany
EvoPref: Multi-Objective Evolutionary Optimization Discovers Diverse LLM Alignments Beyond Gradient Descent
May 10, 2026Dongxin Guo, Jikun Wu, Siu Ming YiuMulti-Objective Evolutionary Optimization Multi-Objective Language Model Alignment
The University of Hong Kong, China · Stellaris AI Limited, China
QUIVER: Cost-Aware Adaptive Preference Querying in Surrogate-Assisted Evolutionary Multi-Objective Optimization
May 5, 2026Florian A. D. BurnatSurrogate-Assisted Optimization Multi-Objective Evolutionary Optimization
University of Warwick Warwick Business School Coventry, England, UK
Exact and Evolutionary Algorithms for Sequential Multi-Objective Transmission Topology Planning
May 5, 2026Job Groeneveld, Miguel Muñoz, Jan Viebahn +1Multi-Objective Evolutionary Optimization Combinatorial Optimization
TenneT TSO B.V., Arnhem, The Netherlands · Artelys, Paris, France · Department of Mathematics, Vrije Universiteit Amsterdam, The Netherlands
Benchmarking Stopping Criteria for Evolutionary Multi-objective Optimization
Apr 28, 2026Kenji Kitamura, Ryoji TanabeBenchmark Design Multi-Objective Evolutionary Optimization
Yokohama National University Yokohama, Kanagawa, Japan
MAEO: Multiobjective Animorphic Ensemble Optimization for Scalable Large-scale Engineering Applications
Apr 26, 2026Omer F. Erdem, Dean Price, Paul Seurin +1Evolutionary Optimization Multi-Objective Evolutionary Optimization
Department of Nuclear Engineering and Radiological Sciences, University of Michigan, Ann Arbor, MI 48109, United States · Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, 60 Vassar St., Cambridge, MA 02139 · Idaho National Laboratory, Idaho Falls, ID 83415, United States +1
On Scalability of Multi-Objective Evolutionary Algorithms on Combinatorial Optimisation Problems
Apr 20, 2026Menghao Tang, Zimin Liang, Miqing LiEvolutionary Optimization Multi-Objective Evolutionary Optimization
University of Birmingham Birmingham, United Kingdom
Optimising Urban Flood Resilience
Apr 17, 2026James Mckenna, Christos Iliadis, Vassilis GlenisMulti-Objective Evolutionary Optimization
School of Engineering, Newcastle University, Newcastle upon Tyne, United Kingdom
Analysis of Multitasking Pareto Optimization for Monotone Submodular Problems
Apr 16, 2026Liam Wigney, Frank NeumannSubmodular Optimization Evolutionary Optimization
Optimisation and Logistics School of Computer Science and Information Technology Adelaide University Adelaide, Australia
COEVO: Co-Evolutionary Framework for Joint Functional Correctness and PPA Optimization in LLM-Based RTL Generation
Apr 16, 2026Heng Ping, Peiyu Zhang, Shixuan Li +5Multi-Objective Evolutionary Optimization Code Generation
University of Southern California United States
On the use of evolutionary optimization for the dynamic chance constrained open-pit mine scheduling problem
Apr 15, 2026Ishara Hewa Pathiranage, Aneta NeumannStochastic Optimization Chance-Constrained Optimization
Machine Learning and Optimisation, School of Computer Science and Information Technology, Adelaide University, Adelaide, Australia
Decoupling Constraints from Two Directions for Evolutionary Constrained Multi-objective Optimization
Dec 30, 2025Ruiqing Sun, Dawei Feng, Xing Zhou +6Evolutionary Optimization Multi-Objective Evolutionary Optimization
College of Computer Science and Technology, National University of Defense Technology, Changsha 410000, P.R. China · College of Intelligence Science and Technology, National University of Defense Technology, Changsha, 410000, P.R. China · State Key Laboratory of Complex & Critical Software Environment, College of Information and Communication, National University of Defense Technology, Wuhan 430019, P.R. China +1

May 18, 2026, cs.AIJ/K move · Enter open · S save

When Fireflies Cluster; Enhancing Automatic Clustering via Centroid-Guided Firefly Optimization

MKA Ariyaratne, Azwirman Gusrialdi, Yury Nikulin, Jaakko Peltonen

Department of Computer Science, Faculty of Applied Sciences, University of Sri Jayewardenepura, Sri Lanka · Faculty of Engineering and Natural Sciences,Tampere University, Tampere, Pirkanmaa, Finland · Department of Mathematics and Statistics, University of Turku, Finland

Multi-Objective Evolutionary Optimization Metaheuristic Optimization Clustering Unsupervised Clustering

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This work presents a novel variant of the Firefly Algorithm (FA) for data clustering, addressing limitations of traditional methods like K-Means that struggle with non-uniform cluster shapes, densities, and the need for pre-defining the number of clusters. The proposed algorithm introduces a centroid movement strategy and a multi-objective fitness function that balances compactness, separation, and a novel TSP-based navigation penalty. It automatically estimates the optimal number of clusters and dynamically adjusts cluster boundaries. Application to robotic sensor networks highlights its practical value, with experiments showing improved clustering quality and reduced intra-cluster path distances compared to K-Means. These results confirm the algorithm's robustness in complex spatial clustering tasks, with potential for future extensions to higher-dimensional and adaptive scenarios.

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