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A Knee-Guided evolutionary Algorithm Based Navigation Approach for Mobile Robots in Intelligent Manufacturing Scenarios

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 582-593页

作者： Xia, Yunhao Li, Zhijun Yen, Gary G. Xia, Haisheng Univ Sci & Technol China Dept Automat Hefei 230026 Peoples R China Tongji Univ Sch Mech Engn Shanghai 201804 Peoples R China Tongji Univ Shanghai Sunshine Rehabil Ctr Shanghai Yangzhi Rehabil Hosp Sch Mech EngnTranslat Res Ctr Shanghai 201804 Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei 230026 Peoples R China Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA

This paper proposes a novel navigation and optimization approach for mobile robots operating in intelligent manufacturing (IM) scenarios with unknown obstacles. First, we introduce a set of evaluation functions that simultaneously consider multiple metrics, including speed, security, sampling step, and obstacle avoidance ability, for scenarios with unknown dynamic obstacles. We then adopt a knee-guided multi-objective evolutionary algorithm capable of balancing parameter size and performance to optimize these conflicting objectives. Finally, we present a local navigation framework that integrates navigation and multi-objective optimization to enable obstacle avoidance in unstructured environments. The proposed algorithm is validated through simulations and practical scenarios, demonstrating its effectiveness in both static and dynamic scenarios. Note to Practitioners-The Human-cyber-physical System (HCPS) is the basic principle of the new generation of intelligent manufacturing (IM). It presents new requirements for the production method, and robot operate completely autonomously is expected to be the future solution for some complex tasks. However, traditional navigation methods often fail in these scenarios due to environmental changes and restricted workspaces. To address this challenge, this paper proposes a multi-objective optimization-based local navigation approach (MONA) that transforms the local navigation problem into a multi-objective optimization problem. The approach uses the knee-guided multi-objective evolutionary algorithm to optimize the control input for the mobile robot, ensuring safe and fast navigation in complex environments. The proposed approach has been validated through simulation and real scenario experiments, demonstrating its effectiveness.

关键词： Mobile robots navigation multi-objective optimization evolutionary computation

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Characterization of Constrained Continuous Multiobjective Optimization Problems: A Performance Space Perspective

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IEEE TRANSACTIONS ON evolutionary computation 2025年第1期29卷 275-285页

作者： Vodopija, Aljosa Tusar, Tea Filipic, Bogdan Jozef Stefan Inst Dept Intelligent Syst Ljubljana 1000 Slovenia Jozef Stefan Int Postgrad Sch Ljubljana 1000 Slovenia

Constrained multiobjective optimization has gained much interest in the past few years. However, constrained multiobjective optimization problems (CMOPs) are still unsatisfactorily understood. Consequently, the choice of adequate CMOPs for benchmarking is difficult and lacks a formal background. This article takes a step toward addressing this issue by exploring CMOPs from a performance space perspective. First, it presents a novel performance assessment approach designed explicitly for constrained multiobjective optimization. This methodology offers a first attempt at simultaneously measuring the performance in approximating the Pareto front and constraint satisfaction. Second, it proposes an approach to measure the capability of the given optimization problem to differentiate among algorithm performances. Finally, this approach is used to compare eight frequently used artificial test suites of CMOPs. The experimental results reveal which suites are more efficient in discerning between four well-known multiobjective optimization algorithms.

关键词： Optimization evolutionary computation Benchmark testing Linear programming Convergence Behavioral sciences Runtime Benchmarking constrained multiobjective optimization constraint violation hypervolume multiobjective evolutionary algorithm (MOEA) performance measurement test problem

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Bayesian Optimization for Quality Diversity Search With Coupled Descriptor Functions

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IEEE TRANSACTIONS ON evolutionary computation 2025年第2期29卷 302-316页

作者： Kent, Paul Gaier, Adam Mouret, Jean-Baptiste Branke, Juergen Univ Warwick Coventry CV4 7AL England Autodesk Res AI Lab D-53111 Bonn Germany Univ Lorraine CNRS Inria Loria F-54000 Nancy France Univ Warwick Warwick Business Sch Coventry CV4 7AL England

Quality diversity (QD) algorithms, such as the multidimensional archive of phenotypic elites (MAP-Elites), are a class of optimization techniques that attempt to find many high-performing points that all behave differently according to a user-defined behavioral metric. In this article we propose the Bayesian optimization of elites (BOP-Elites) algorithm. Designed for problems with expensive fitness functions and coupled behavior descriptors, it is able to return a QD solution-set with excellent performance already after a relatively small number of samples. BOP-Elites models both fitness and behavioral descriptors with Gaussian Process surrogate models and uses Bayesian optimization strategies for choosing points to evaluate in order to solve the quality-diversity problem. In addition, BOP-Elites produces high-quality surrogate models which can be used after convergence to predict solutions with any behavior in a continuous range. An empirical comparison shows that BOP-Elites significantly outperforms other state-of-the-art algorithms without the need for problem-specific parameter tuning.

关键词： Optimization Bayes methods Conductivity Linear programming Search problems evolutionary computation Task analysis Bayesian optimization (BO) quality diversity

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A Constrained Many-Objective Mobile Crowdsensing Task Allocation Method Considering Latent Workers

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IEEE INTERNET OF THINGS JOURNAL 2025年第4期12卷 4065-4077页

作者： Cai, Xingjuan Ji, Chen Zhao, Tianhao Taiyuan Univ Sci & Technol Shanxi Key Lab Big Data Anal & Parallel Comp Taiyuan 030024 Shanxi Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing 210093 Jiangsu Peoples R China

The concept of mobile crowd sensing (MCS) represents a powerful model within the Internet of Things. It perceives different phenomena in the urban environment by taking advantage of the mobility of individuals and the sensors embedded in smartphones. Existing MCS task allocation systems typically assume that workers are in an idle state without any current tasks. However, this overlooks workers who are already engaged in other tasks but are closer to the task location, leading to potential resource wastage. In this article, we refer to such workers as "the latent workers." To address this issue, we propose a novel model called the constrained many-objective MCS task allocation model considering latent workers (CMaO-MCSTA-LW). This model aims to find a set of compromise solutions under multiple uncontrollable objective environments. To effectively solve this model, we developed a new algorithm called the dual-population constrained many-objective evolutionary algorithm based on the easing strategy and the elite parent strategy (dCMaOEA-E-P), which builds upon the original dCMaOEA-RAE. Experimental results demonstrate that, compared to several other representative algorithms, the proposed algorithm shows superior performance across multiple perspectives, significantly improving task allocation efficiency.

关键词： Sensors Resource management Mobile computing evolutionary computation Costs Internet of Things Encoding Software Scheduling Schedules evolutionary algorithm (EA) latent workers many-objective optimization mobile crowdsensing task allocation

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Coloring Dynamic Graphs With a Similarity and Pool-Based evolutionary Algorithm

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IEEE ACCESS 2025年 13卷 38054-38075页

作者： Terci, Gizem Sungu Boz, Betul Gebze Tech Univ Inst Informat Technol TR-41400 Kocaeli Turkiye Marmara Univ Comp Engn Dept TR-34854 Istanbul Turkiye

The graph coloring problem is a well-known optimization challenge, particularly relevant in dynamic environments where the graph undergoes continuous changes over time. evolutionary algorithms, known for their adaptability and effectiveness in handling NP-hard problems, are well-suited for tackling the issues related to coloring dynamic graphs. In this paper, we present a novel Similarity and Pool-Based evolutionary Algorithm designed to address the graph coloring problem on dynamic graphs. Our approach employs a partition-based representation that adapts to dynamic graph changes while preserving valuable historical information. The algorithm introduces an innovative similarity and conflict-based crossover operator aimed at minimizing the number of colors used, alongside a local search method to enhance solution diversity. We evaluated the performance of the proposed algorithm against a well-known heuristic for the graph coloring problem and a genetic algorithm with a dynamic population across a diverse set of dynamic graphs. Experimental results demonstrate that our algorithm consistently outperforms these alternatives by reducing the number of colors required in the majority of test cases.

关键词： Heuristic algorithms Color evolutionary computation Social networking (online) Real-time systems Partitioning algorithms Adaptation models Metaheuristics Genetic operators Communication networks Dynamic graphs edge-dynamic graphs node-dynamic graphs graph coloring problem crossover operator evolutionary algorithms

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AIEA: An Asynchronous Influence-Based evolutionary Algorithm for Expensive Many-Objective Optimization

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IEEE TRANSACTIONS ON CYBERNETICS 2025年第2期55卷 786-799页

作者： Wei, Feng-Feng Chen, Wei-Neng Zhang, Jun South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China South China Univ Technol State Key Lab Subtrop Buildingand Urban Sci Guangzhou 510006 Peoples R China Nankai Univ Coll Artificial Intelligence Tianjin 30071 Peoples R China Zhejiang Normal Univ Sch Comp Sci & Technol Jinhua 321004 Peoples R China Hanyang Univ Dept Elect & Elect Engn Ansan 15588 South Korea

In expensive multi/many-objective optimization problems (EMOPs), the expensive objectives are generally accessed through different simulation tools, leading to different evaluation latencies and unbearable computational time for serial optimization. One promising approach to improve efficiency is to perform simulation and build surrogates separately for each objective in parallel. However, how to improve the model accuracy and select promising candidates without global information are big challenges. To alleviate these problems, this article proposes an asynchronous influence-based SAEA (AIEA) based on the client-server model. Each client approximates an objective and the server takes charge for evolution. To adaptively select promising candidates, the influence degree is introduced in candidate selection, which is calculated in the objective space to judge which candidate has more beneficial influence for evolution. With the selected candidate, the most-uncertain-first strategy is devised in objective selection for asynchronous evaluations and model improvement. To handle incomplete objective values, the nearest neighbor inheritance is adopted for unevaluated objectives. Comprehensive experiments compared with five surrogate-assisted EAs demonstrate the global optimization and scalability of AIEA.

关键词： Optimization computational modeling Predictive models Training data Data models Vectors Servers Program processors evolutionary computation Distributed databases Expensive optimization many-objective optimization problems parallel and distributed evolutionary computation surrogate-assisted evolutionary algorithm

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Benchmarking Derivative-Free Global Optimization Algorithms Under Limited Dimensions and Large Evaluation Budgets

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IEEE TRANSACTIONS ON evolutionary computation 2025年第1期29卷 187-204页

作者： Stripinis, Linas Kudela, Jakub Paulavicius, Remigijus Vilnius Univ Inst Data Sci & Digital Technol LT-08663 Vilnius Lithuania Brno Univ Technol Inst Automat & Comp Sci Brno 61600 Czech Republic

This article addresses the challenge of selecting the most suitable optimization algorithm by presenting a comprehensive computational comparison between stochastic and deterministic methods. The complexity of algorithm selection arises from the absence of a universal algorithm and the abundance of available options. Manual selection without comprehensive studies can lead to suboptimal or incorrect results. In order to address this issue, we carefully selected 25 promising and representative state-of-the-art algorithms from both aforementioned classes. The evaluation with up to the 20 dimensions and large evaluation budgets $(10<^>{5}{\times }n)$ was carried out in a significantly expanded and improved version of the DIRECTGOLib v2.0 library, which included ten distinct collections of primarily continuous test functions. The evaluation covered various aspects, such as solution quality, time complexity, and function evaluation usage. The rankings were determined using statistical tests and performance profiles. When it comes to the problems and algorithms examined in this study, EA4eig, EBOwithCMAR, APGSK-IMODE, 1-DTC-GL, OQNLP, and DIRMIN stand out as superior to other derivative-free solvers in terms of solution quality. While deterministic algorithms can locate reasonable solutions with comparatively fewer function evaluations, most stochastic algorithms require more extensive evaluation budgets to deliver comparable results. However, the performance of stochastic algorithms tends to excel in more complex and higher-dimensional problems. These research findings offer valuable insights for practitioners and researchers, enabling them to tackle diverse optimization problems effectively.

关键词： Optimization Partitioning algorithms evolutionary computation Benchmark testing Stochastic processes Linear programming Closed box Derivative-free global optimization deterministic algorithms evolutionary computation (EC) algorithms nature-inspired meta-heuristics numerical benchmarking

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Gradient-Guided Local Search for Large-Scale Hypervolume Subset Selection

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IEEE TRANSACTIONS ON evolutionary computation 2025年第2期29卷 519-533页

作者： Nan, Yang Shu, Tianye Ishibuchi, Hisao Shang, Ke Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Peoples R China Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen 518060 Peoples R China

The use of an unbounded archive (UA) has attracted much attention in the filed of evolutionary multiobjective optimization (EMO) since a solution set selected from the UA is often better than the final population. The size of the UA is very large (e.g., 1 000 000) since it is unbounded and it stores all the examined nondominated solutions during the execution of an EMO algorithm. Thus, an algorithm which can efficiently select a high-quality subset from a large-scale candidate set (e.g., UA) is needed. In this article, we propose a gradient-guided local search hypervolume subset selection (GL-HSS) algorithm to efficiently select a high-quality subset from a large-scale candidate set. In each iteration of GL-HSS, the gradient of the hypervolume (HV) contribution of each selected solution is used to guide the local search. As a result, the proposed algorithm can quickly improve the HV of the selected subset. Experimental results show that, compared to the existing subset selection algorithms, the proposed GL-HSS algorithm can efficiently select high-quality subsets from various large-scale candidate sets.

关键词： Approximation algorithms Classification algorithms Search problems Optimization Vectors evolutionary computation Minimization Time complexity Size measurement Indexes evolutionary multiobjective optimization (EMO) hypervolume subset selection (HSS) local search (LS) many-objective optimization

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evolutionary Algorithm for the Traveling Salesman Problem With Innovative Encoding on Hybrid Quantum-Classical Machines

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IEEE ACCESS 2025年 13卷 54223-54239页

作者： Saini, Ravi Mani, Ashish Prasad, M. S. Bhattacharyya, Siddhartha Platos, Jan Amity Univ Dept Comp Sci & Engn ASET Noida 201301 Uttar Pradesh India Amity Univ Amity Innovat & Design Ctr Noida 201301 Uttar Pradesh India Amity Univ Amity Inst Space Sci & Technol Noida 201301 Uttar Pradesh India VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Ostrava 70800 Czech Republic

The Traveling Salesman Problem (TSP) is a widely studied NP-complete optimization challenge with significant theoretical and practical implications. This study proposes a hybrid quantum-classical framework using a Quantum-Inspired evolutionary Algorithm (QEA) with Sort Gray Binary Encoding to solve the TSP. The proposed method guarantees the generation of valid TSP tours by eliminating invalid solutions. It employs quantum superposition with intrinsic randomness to enhance computational efficiency and scalability. The framework was implemented on cloud-based NISQ platforms, including IBM Quantum and AWS Braket, demonstrating its practicality and effectiveness. Experimental evaluations revealed that the proposed framework successfully solved TSP instances with up to 15 cities, achieving superior performance compared to classical methods and showcasing its ability to scale under NISQ constraints. The results also highlight the potential of hybrid quantum-classical approaches to overcome hardware limitations in current quantum systems. This study establishes a robust hybrid methodology for solving combinatorial optimization problems. It also sets a benchmark for leveraging the capabilities of NISQ-era quantum devices in real-world applications, thereby providing a foundation for future research in hybrid quantum-classical optimization techniques.

关键词： Urban areas Quantum computing Logic gates Encoding evolutionary computation Traveling salesman problems Qubit Mathematical models Quantum system Hardware evolutionary algorithm gray encoding hybrid algorithms noisy intermediate-scale quantum quantum computing traveling salesman problem

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Single-stage filter-based local feature selection using an immune algorithm for high-dimensional microarray data

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APPLIED SOFT COMPUTING 2025年 172卷

作者： Wang, Yi Li, Wenshan Li, Tao Tian, Hao Hubei Univ Econ Sch Informat Engn Wuhan 430205 Peoples R China Chengdu Univ Informat Technol Sch Cybersecur Chengdu 610225 Peoples R China Sichuan Univ Coll Cybersecur Chengdu 610065 Peoples R China

Feature selection is crucial for disease classification and prognosis in high-dimensional microarray data, as it reduces dimensionality, enhances model accuracy, and improves computational efficiency. However, most existing methods rely on a global set of features and overlook the relationships between features and sample subspaces. To address this limitation, this study proposes a filter-based local feature selection method based on an immune algorithm (IA-FLFS). This method dynamically assigns a unique feature subset to each sample neighborhood, replacing reliance on a global feature subset. It incorporates three key innovations: (1) a single- stage, filter-based approach that considers feature interactions and is independent of any learning model, ensuring high effectiveness and efficiency for high-dimensional datasets;(2) an enhanced clonal selection algorithm is utilized to identify feature subsets, enhancing search capabilities through filter-based initialization, adaptive differential evolution-based mutation, and symmetric uncertainty-based local search. (3) thread-level parallelism is applied to each feature subset, significantly reducing computation time. Experimental results on twelve datasets demonstrate IA-FLFS's superior accuracy, efficiency, and ability to produce smaller feature subsets, outperforming fourteen state-of-the-art feature selection methods on most datasets. Notably, compared to other local feature selection algorithms, it achieves significant accuracy improvements on over eight datasets, highlighting its potential as a powerful and efficient tool for high-dimensional microarray analysis.

关键词： Local feature selection Immune algorithm evolutionary computation Gene selection

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