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DANCE: Distributed co-evolutionary design of velocity controllers for swarm intelligence robots in flocking and entrapping tasks

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SWARM AND evolutionary computation 2025年 94卷

作者： Wang, Chen Zhu, Cheng Zhu, Xianqiang Lei, Hongtao Zhang, Weiming Wu, Meng Natl Univ Def Technol Deya Rd Changsha 410073 Hunan Peoples R China Natl Key Lab informat Syst Engn Deya Rd Changsha 410073 Hunan Peoples R China

This study combined evolutionary algorithm and reinforcement learning to propose anew automated design method for generating swarm robots velocity controller model. It alternately evolves heterogeneous swarm and homogeneous swarm through a gene expression programming method that introduces reinforcement learning, and assembles function nodes and leaf nodes into new mathematical formulas during the evolution process. The method enable to realize the effect of swarm robots emerging to perform swarm tasks such as flocking and entrapping. What is more, anew swarm rule was discovered during the evolution process, which is used to realize the flocking of swarm robots at any angle. The experimental results show that the swarm motion controller automatically generated by the model has high task completion efficiency and strong generalization.

关键词： Swarm intelligence Multi-agent systems Robots evolutionary computation Reinforcement learning Gene expression programming

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Text clustering with a hybrid multi-objective optimization approach: The multi-objective firefly differential Jaya Algorithm

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SWARM AND evolutionary computation 2025年 93卷

作者： Naderi, Muhammad Amiri, Maryam Arak Univ Fac Engn Dept Comp Engn Arak *** Iran

The exponential growth of unstructured text data generated by internet users has created an urgent need for efficient organization methods to uncover valuable insights. Text clustering, a widely used data mining approach, often relies on single-objective optimization, which can struggle to deliver optimal results for datasets with diverse clustering criteria. To address these challenges, we propose the Multi-objective Firefly Differential Jaya (MFDJ) algorithm, a novel nature-inspired optimization method designed to enhance text clustering. MFDJ integrates the strengths of NSGA-II, a well-established multi-objective optimization framework, with three complementary algorithms: the Firefly algorithm for swarm intelligence-based optimization, Differential Evolution for robust exploration through mutation, and the Jaya algorithm for parameter-free improvement leveraging both the best and worst solutions. This synergy significantly enhances the algorithm's ability to balance exploration and exploitation, yielding superior clustering performance. We evaluated MFDJ on eight benchmark text datasets, where it demonstrated consistent superiority over state-of-the-art methods, including NSGA-II and MOMDE. On average, MFDJ achieved a 67.89% improvement in F-measure over NSGA-II and a 5.87% improvement over MOMDE, while also exhibiting better convergence properties for the majority of datasets. These results underscore the capability of MFDJ to generate high-quality clusters, making it a versatile tool for tackling complex text clustering and broader optimization challenges.

关键词： evolutionary computation Text clustering Multi-objective optimization Optimization algorithms

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Leveraging Grammarware for Active Video Game Development

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APPLIED SCIENCES-BASEL 2025年第8期15卷 4253-4253页

作者： Crepinsek, Matej Kosar, Tomaz Moravec, Matej Ravber, Miha Mernik, Marjan Univ Maribor Fac Elect Engn & Comp Sci Koroska Cesta 46 Maribor 2000 Slovenia

This paper presents a grammarware-based approach to developing active video games (AVGs) for sensor-driven training systems. The GCGame domain-specific language (DSL) is introduced to define game logic, sensor interactions, and timing behavior formally. This approach ensures cross-platform consistency, supports real-time configurability, and simplifies the integration of optimization and visualization tools. The presented system, called GCBLE, serves as a case study, demonstrating how grammarware enhances modularity, maintainability, and adaptability in real-world physical interaction applications. The results highlight the potential of a DSL-driven design to bridge the gap between developers and domain experts in embedded interactive systems.

关键词： active video games grammarware internet of things DSL procedural level generation evolutionary computation game controllers

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Micro Many-Objective evolutionary Algorithm With Knowledge Transfer

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IEEE TRANSACTIONS ON EMERGING TOPICS IN computationAL INTELLIGENCE 2025年第1期9卷 43-56页

作者： Peng, Hu Luo, Zhongtian Fang, Tian Zhang, Qingfu Jiujiang Univ Sch Comp & Big Data Sci Jiujiang 332005 Peoples R China Jiujiang Key Lab Digital Technol Jiujiang 332005 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong 999077 Peoples R China

computational effectiveness and limited resources in evolutionary algorithms are interdependently handled during the working of low-power microprocessors for real-world problems, particularly in many-objective evolutionary algorithms (MaOEAs). In this respect, the balance between them will be broken by evolutionary algorithms with a normal-sized population, but which doesn't include a micro population. To tackle this issue, this paper proposes a micro many-objective evolutionary algorithm with knowledge transfer (mu MaOEA). To address the oversight that knowledge is often not considered enough between niches, the knowledge-transfer strategy is proposed to bolster each unoptimized niche through optimizing adjacent niches, which enables niches to generate better individuals. Meanwhile, a two-stage mechanism based on fuzzy logic is designed to settle the conflict between convergence and diversity in many-objective optimization problems. Through efficient fuzzy logic decision-making, the mechanism maintains different properties of the population at different stages. Different MaOEAs and micro multi-objective evolutionary algorithms were compared on benchmark test problems DTLZ, MaF, and WFG, and the results showed that mu MaOEA has an excellent performance. In addition, it also conducted simulation on two real-world problems, MPDMP and MLDMP, based on a low-power microprocessor. The results indicated the applicability of mu MaOEA for low-power microprocessor optimization.

关键词： Optimization evolutionary computation Knowledge transfer Vectors Convergence Microprocessors Fuzzy logic Micro many-objective evolutionary algorithm many-objective optimization problems knowledge transfer low-power microprocessors

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Multi-objective genetic programming based on decomposition for feature learning in image classification

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SWARM AND evolutionary computation 2025年 94卷

作者： Zhang, Tuo Bi, Ying Liang, Jing Xue, Bing Zhang, Mengjie Zhengzhou Univ Sch Elect & Informat Engn 100 KeXue Rd Zhengzhou 450001 Henan Peoples R China State Key Lab Intelligent Agr Power Equipment 39 Xiyuan Rd Luoyang 471039 Henan Peoples R China Henan Inst Technol Sch Elect Engn & Automat 699 Pingyuan Rd Xinxiang 453003 Henan Peoples R China Victoria Univ Wellington Ctr Data Sci & Artificial Intelligence Sch Engn & Comp Sci POB 600 Wellington 6140 New Zealand

Image classification presents a challenge due to its high dimensionality and extensive variations. Feature learning is a powerful method in addressing this challenge, constituting a multi-objective problem aimed at maximizing classification accuracy and minimizing the number of learned features. A few multi-objective genetic programming (MOGP) methods have been proposed to optimize these two objectives, simultaneously. However, existing MOGP methods ignore the characteristics of feature learning tasks. Therefore, this work proposes a decomposition-based MOGP approach with a global replacement strategy for feature learning in data-efficient image classification. To handle the different value ranges of the two objectives, a transformation function is designed to uniform the range of the number of learned features. In addition, a preference-based decomposition strategy is proposed to address the preference for the objective of classification accuracy. The proposed approach is compared with existing MOGP methods for feature learning on five different image classification datasets with different numbers of training images. The experimental results demonstrate the effectiveness of the proposed approach by achieving better HVs than or comparable to the existing MOGP methods in at least 13 out of 20 cases and classification accuracy significantly better than a popular neural architecture search method in all cases.

关键词： Genetic programming Multi-objective optimization Feature learning Image classification evolutionary computation

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Multi-Objective Optimization for Multimodal Multi-Objective Multi-Point Shortest Path Problem Considering Unforeseeable Road Eventualities

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第6期26卷 8622-8640页

作者： Xu, Zhiwei Zhang, Kai Del Ser, Javier Li, Miqing Xu, Xin He, Juanjuan Wu, Ni Wuhan Univ Sci & Technol Sch Comp Sci & Technol Hubei Prov Key Lab Intelligent Informat Proc andRe Wuhan 430065 Hubei Peoples R China Tecnalia Basque Res & Technol Alliance BRTA Bizkaia 48160 Bilbao Spain Univ Basque Country Dept Commun Engn UPV EHU Bizkaia 48013 Bilbao Spain Univ Birmingham Sch Comp Sci Birmingham B15 2TT England

Multi-objective multi-point shortest path planning problems are commonly encountered in real-world applications. Numerous path planning algorithms have been proposed to accommodate different model assumptions. However, most existing algorithms can only identify a subset of the Pareto optimal paths and overlook equivalent Pareto optimal paths. Relying solely on a subset of Pareto optimal solutions is insufficient to effectively respond to unforeseeable road eventualities in the real-world traffic environment. In this paper, multi-objective multi-point shortest path planning problem is modeled as a multimodal multi-objective optimization problem with necessary points constrains. A multimodal multi-objective evolutionary algorithm using constraint dominance principle-based path comparison strategy and path similarity-based multimodal solutions selection strategy is proposed to address this problem. The proposed constraint dominance principle-based path comparison strategy can effectively navigate through large infeasible regions by relaxing necessary point constraints, thereby obtaining a true constrained Pareto front. The proposed path similarity-based multimodal solutions selection strategy can effectively balance the distribution of solutions in the decision space, thereby preserving multiple equivalent optimal solutions. The proposed algorithm is compared with five state-of-the-art path planning algorithms from the benchmark test suite derived from the 2021 IEEE CEC path planning competition, where city maps are adapted from real transportation networks in Chinese cities, in our experiments. The exceptional performance is demonstrated through thirty independent runs, yielding experimental results that showcase the superiority of the proposed algorithm on the test problem set. This superior performance highlights the potential for designing more resilient path planners suitable for scenarios affected by unpredictable road eventualities.

关键词： Path planning Optimization Classification algorithms Shortest path problem Pareto optimization Search problems Heuristic algorithms Roads evolutionary computation Convergence Multi-objective shortest path planning constrained multi-objective optimization multimodal multi-objective optimization multi-objective evolutionary algorithm

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Multiple Tasks for Multiple Objectives: A New Multiobjective Optimization Method via Multitask Optimization

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IEEE TRANSACTIONS ON evolutionary computation 2025年第1期29卷 172-186页

作者： Li, Jian-Yu Zhan, Zhi-Hui Li, Yun Zhang, Jun Nankai Univ Coll Artificial Intelligence Tianjin 300350 Peoples R China Univ Elect Sci & Technol China Shenzhen Inst Adv Study Ind Artificial Intelligence Ctr Shenzhen 518110 Peoples R China I4AI Ltd London WCIN 3AX England Nankai Univ Coll Artificial Intelligence Tianjin 300350 Peoples R China Hanyang Univ ERICA Ansan 15588 South Korea

Handling conflicting objectives and finding multiple Pareto optimal solutions are two challenging issues in solving multiobjective optimization problems (MOPs). Inspired by the efficiency of multitask optimization (MTO) in finding multiple optimal solutions of MTO problem (MTOP), we propose to treat MOP as a MTOP and solve it by using MTO. By transforming the MOP into a MTOP, not only that the difficulty in handling conflicting objectives can be avoided, but also that MTO can help efficiently find well-distributed multiple optimal solutions for MOP. With the above idea, this article proposes a new multiobjective optimization method via MTO, with the following three contributions: 1) a theorem is proposed to theoretically show the relationship between MOP and MTOP and how MOP can be transformed into a MTOP;2) based on the theoretical analysis, a multiple tasks for multiple objectives (MTMOs) framework is proposed for solving MOP efficiently;and 3) a MTMO-based evolutionary algorithm is developed to solve MOP, together with two novel strategies. One is a target point estimation strategy for transforming the MOP into a MTOP automatically and accurately. The other is an archive-based implicit knowledge transfer strategy for efficiently transferring knowledge across multiple tasks to enhance the optimization results of multiple tasks together. The superiority of the proposed algorithm is validated in extensive experiments on 15 MOPs with objective numbers varying from 3 to 20 and with six state-of-the-art algorithms as competitors. Therefore, solving MOP and even many-objective optimization problem via MTO is a new, promising, and efficient method.

关键词： Task analysis Optimization Pareto optimization evolutionary computation Knowledge transfer Minimization Transforms evolutionary computation (EC) knowledge transfer multiobjective optimization problem (MOP) multiple tasks for multiple objectives (MTMOs) multitask optimization problem (MTOP) transforming

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Deep Learning-Driven Analysis of a Six-Bar Mechanism for Personalized Gait Rehabilitation

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JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING 2025年第1期25卷 011001页

作者： Khan, Naveed Ahmad Hussain, Shahid Spratford, Wayne Goecke, Roland Kotecha, Ketan Jamwal, Prashant K. Univ Canberra Sch Informat Technol & Syst Canberra ACT 2617 Australia Univ Canberra Res Inst Sport & Exercise Canberra ACT 2617 Australia Univ New South Wales Sch Syst & Comp Canberra ACT 2612 Australia Symbiosis Int Univ Symbiosis Inst Technol Symbiosis Ctr Appl Artificial Intelligence Pune 411045 Maharashtra India Nazarbayev Univ Dept Elect & Comp Engn Astana 010000 Kazakhstan

Recent advances in robotics and artificial intelligence have highlighted the potential for the integration of computational intelligence in enhancing the functionality and adaptability of robotic systems, particularly in rehabilitation. Designing robotic exoskeletons for the lower limb rehabilitation of post-stroke patients requires frequent adjustments to accommodate individual differences in leg anatomy. This complex engineering challenge necessitates a deep understanding of human physiology, robotics, and optimization to develop adaptive robotic systems and also to swiftly quantify the required adjustments and implement them for each patient. The conventional approaches, which mostly rely on heuristics and manual tuning, often struggle to achieve optimal results. This paper presents a novel method that integrates a genetic algorithm with a deep learning approach to generate a gait trajectory of the ankle joint from a six-bar linkage mechanism of fixed dimensions. Later, using the same approach, the inverse kinematics solution for this mechanism is also devised whereby, the set of the link dimensions of the six-bar linkage mechanism is obtained for the given gait trajectory of an individual to achieve customization. We simulated the kinematic behavior of the six-bar linkage mechanism within defined mechanical constraints and utilized the generated data for training a feedforward neural network and long short-term memory models. The proposed model, when trained, can produce accurate lengths for the desired gait trajectories in the sagittal plane and vice versa, which further validates our proposed approach for inverse kinematics solution. Moreover, to evaluate the efficiency of deep learning models, we have conducted an extensive error-based, comparative, and sensitivity analysis using different performance indices. The results highlight the potential of the proposed deep-learning-driven approach in the design analysis of gait rehabilitation robots.

关键词： stroke gait rehabilitation six-bar linkage mechanism data-driven simulation deep learning for engineering applications evolutionary computation artificial intelligence data-driven engineering machine learning for engineering applications model-based systems engineering

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Bi-Level Model Management Strategy for Solving Expensive Multi-Objective Optimization Problems

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IEEE TRANSACTIONS ON EMERGING TOPICS IN computationAL INTELLIGENCE 2025年第1期9卷 332-346页

作者： Li, Fei Yang, Yujie Liu, Yuhao Liu, Yuanchao Qian, Muyun Anhui Univ Technol Dept Elect & Informat Engn Anhui Prov Key Lab Met Engn & Resources Recycling Anhui Prov Engn Lab Intelligent D/emolit Equipment Maanshan 243002 Anhui Peoples R China Anhui Univ Technol Sch Elect & Informat Engn Maanshan 243032 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Anhui Univ Technol Dept Elect & Informat Engn Maanshan 243002 Anhui Peoples R China

Model management strategy is the main component of surrogate-assisted evolutionary algorithms for solving expensive multi-objective optimization problems(EMOPs). In such problems, evaluating the true fitness function requires significant computational resources, which necessitates effective determination of which individuals should be selected for evaluation. However, existing model management strategies often struggle to effectively balance exploration and exploitation when selecting individuals. To mitigate this issue, a bi-level model management strategy is proposed. The selection procedure not only considers exploring the objective space by balancing predicted values and uncertainty in the lower-level but also considers convergence and diversity in the upper-level selection. Specifically, we first divide the objective space into some sub-spaces through a set of direction vectors. Then, we select the first rank individuals via adopting the non-dominated sorting to balance the predicted objective values and the uncertainty in each subspace. The intersection individuals of the selected candidates can be denoted as the lower-level solutions. In the upper-level selection, we combine the modified inverted generational distance (IGD(+)) and shift-based density estimation (SDE) indicators to select the most promising individuals from the lower-level to update the model. Experimental results on benchmark instances show that the proposed algorithm is competitive compared with some representative algorithms.

关键词： Optimization Uncertainty evolutionary computation Vectors Predictive models Convergence Load modeling Surrogate-assisted evolutionary algorithm expensive multi-objective optimization bi-level model management strategy performance indicators Kriging models

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evolutionary induced survival trees for medical prognosis assessment

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APPLIED SOFT COMPUTING 2025年 170卷

作者： Kretowska, Malgorzata Kretowski, Marek Bialystok Tech Univ Fac Comp Sci Wiejska 45A PL-15351 Bialystok Poland

Survival analysis focuses on predicting the time of a specific event, known as failure. In the analysis of survival data, it is crucial to fully leverage censored observations for which we do not have precise event time information. Decision trees are among the most frequently applied machine learning techniques for survival analysis, but to adequately address this issue, it is necessary to transform them into survival trees. This involves equipping the leaves with, for instance, local Kaplan-Meier estimators. Until now, survival trees have predominantly been generated using a greedy approach through classical top-down induction that uses local optimization. Recently, one of the most promising directions in decision tree approach is global learning. The paper proposes an evolutionary algorithm for survival tree induction, which concurrently searches for the tree structure, univariate tests in internal nodes, and Kaplan-Meier estimators in leaves. The fitness function is based on an integrated Brier score, and by introducing a penalty term related to the tree size, it becomes possible to control the interpretability of the obtained predictor. The work investigated, among other aspects, the impact of censoring, and the results obtained from both synthetic and real-life medical datasets are encouraging. The comparison of the predictive ability of the proposed method with already-known univariate survival trees shows statistically significant differences.

关键词： Survival analysis Decision tree evolutionary computation Integrated Brier score

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