Swarm and Evolutionary Computation最新文献_第3页

Multi-objective optimization in autonomous foraging using swarm robots 群机器人自主觅食的多目标优化

IF 8.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation

Pub Date : 2026-01-16 DOI: 10.1016/j.swevo.2026.102294

Erick J. Ordáz-Rivas, Angel Rodríguez-Liñan, Luis M. Torres-Treviño

Swarm robotics is an innovative field focused on developing collective behaviors through local interactions among simple robots, enabling scalability and flexibility across a wide range of tasks. This study presents a behavioral model for collective foraging based on RAOI (repulsion, attraction, orientation, and influence) parameters, and investigates how their tuning affects multi-objective performance in robot swarms. Our approach explores the relationship between RAOI parameter configurations and task-level performance metrics, allowing systematic analysis of emergent swarm behaviors in dynamic environments.

In this work, the tuning of RAOI parameters is formulated as a multi-objective optimization problem guided by established evolutionary algorithms (MOEA/D and NSGA-III), yielding Pareto-optimal trade-offs among competing objectives. The obtained solutions illustrate improvements across multiple criteria, including task completion time, energy consumption, workload distribution, and swarm size efficiency, highlighting inherent trade-offs rather than a single optimal configuration.

The results provide insights into how RAOI-based interaction parameters influence collective foraging dynamics and overall swarm performance. The study focuses on simulation-based evaluation, offering a structured framework for analyzing and tuning swarm behaviors in foraging tasks and related collective robotics scenarios.

群机器人是一个创新领域，专注于通过简单机器人之间的局部交互来发展集体行为，从而在广泛的任务范围内实现可扩展性和灵活性。本文提出了一种基于RAOI（斥力、吸引力、方向和影响）参数的集体觅食行为模型，并研究了它们的调整如何影响机器人群体的多目标性能。我们的方法探索了RAOI参数配置和任务级性能指标之间的关系，允许系统分析动态环境中的突发群体行为。在这项工作中，RAOI参数的调整被制定为由已建立的进化算法（MOEA/D和NSGA-III）指导的多目标优化问题，在竞争目标之间产生帕累托最优权衡。获得的解决方案说明了跨多个标准的改进，包括任务完成时间、能耗、工作负载分布和群大小效率，突出了固有的权衡，而不是单一的最优配置。研究结果揭示了基于raoi的交互参数如何影响群体觅食动态和整体群体表现。该研究侧重于基于仿真的评估，为分析和调整觅食任务和相关集体机器人场景中的群体行为提供了结构化框架。

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引用次数: 0

A survey of features used for representing black-box single-objective continuous optimization 黑箱单目标连续优化的特征描述

IF 8.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation

Pub Date : 2026-01-14 DOI: 10.1016/j.swevo.2026.102288

Gjorgjina Cenikj , Ana Nikolikj , Gašper Petelin , Niki van Stein , Carola Doerr , Tome Eftimov

This survey examines key advancements in designing features to represent optimization problem instances, algorithm instances, and their interactions within the context of single-objective continuous black-box optimization. These features support machine learning tasks such as algorithm selection, algorithm configuration, and problem classification, and they are also used to evaluate the complementarity of benchmark problem sets. We provide a comprehensive overview of problem landscape features, algorithm features, high-level problem-algorithm interaction features, and trajectory features, including the latest works from the past five years. We also point out limitations of the current state-of-the-art and suggest directions for future research.

本调查研究了在单目标连续黑盒优化环境中，设计特征以表示优化问题实例、算法实例及其相互作用方面的关键进展。这些特征支持机器学习任务，如算法选择、算法配置和问题分类，它们也用于评估基准问题集的互补性。我们提供了问题景观特征、算法特征、高级问题-算法交互特征和轨迹特征的全面概述，包括过去五年的最新研究成果。本文还指出了目前研究水平的局限性，并提出了未来研究的方向。

引用次数: 0

River-land multi-modal bulk cargo transportation problem with containerization 河陆多式联运散货运输的集装箱化问题

IF 8.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation

Pub Date : 2026-01-14 DOI: 10.1016/j.swevo.2025.102261

Wei Wu , Lijun Fan , Ruiyou Zhang , Qianli Ma , Peng Jia

This research addresses a river-land multi-modal bulk cargo transportation problem with containerization. It involves three transportation modes: inland waterway, railway, and road transportation. While heterogeneous vessels are commonly employed in inland waterway transportation, few studies have focused on the allocation of these vessels within the context of river-based multimodal transportation. Consequently, introducing decisions on container usage for bulk shipments, identifying containerization locations, and assigning heterogeneous ships to riverine channel in multimodal transportation presents significant challenges. An integer nonlinear programming model based on a directed graph, which incorporates constraints such as water depth, the availability of road and railway vehicles, and the capacity of containerization equipment throughout the planning horizon, is formulated and subsequently linearized. The objective is to minimize the total cost, including transportation, containerization, and cargo damage costs. A multiple ant colony algorithm embedded by a mathematical model is developed to solve the problem. Experiments conducted on numerous near-practical instances demonstrate the effectiveness of the solution methods. The results indicate that for medium- and large-scale instances, the methodology can achieve optimal or high-quality feasible solutions within a reasonable computation time.

本文研究了河陆多式联运散货的集装箱化问题。它涉及三种运输方式：内河运输、铁路运输和公路运输。虽然异质船舶通常用于内河运输，但很少有研究关注这些船舶在河流多式联运背景下的分配。因此，在多式联运中，引入关于散装货物集装箱使用的决定，确定集装箱化地点，并将异质船舶分配到内河航道，都是重大挑战。建立了一个基于有向图的整数非线性规划模型，该模型包含了诸如水深、公路和铁路车辆的可用性以及整个规划范围内集装箱化设备的容量等约束条件，并随后进行了线性化。目标是使总成本最小化，包括运输、集装箱化和货物损坏成本。提出了一种嵌入数学模型的多蚁群算法来解决这一问题。在大量接近实际的实例上进行的实验证明了求解方法的有效性。结果表明，对于大中型实例，该方法能在合理的计算时间内得到最优或高质量的可行解。

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引用次数: 0

Adaptive pattern learning particle swarm optimization for large-scale optimization 大规模优化的自适应模式学习粒子群算法

IF 8.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation

Pub Date : 2026-01-13 DOI: 10.1016/j.swevo.2025.102268

Zhi-Tao Lai , Zi-Jia Wang , Shuai Liu , Zong-Gan Chen , Zhi-Hui Zhan , Sam Kwong , Jun Zhang

Large scale optimization problems (LSOPs) are an important topic in the field of evolutionary computing (EC), and many researchers have designed various learning strategies to try to solve LSOPs more effectively. However, most of the learning strategies are with the fixed learning pattern during the whole evolution process and lack the adaptive adjustment mechanism according to individual property. In fact, different individuals are with different exploitation or exploration abilities, and are suitable for different learning patterns. Therefore, in this paper, we propose adaptive pattern learning particle swarm optimization (APLPSO) to solve LSOPs. In APLPSO, several learning patterns based on different numbers of learning exemplars are first generated to enrich the learning diversity of population. Then, each individual will evaluate the learning patterns and adaptively select its own appropriate learning pattern for updating. The experimental results on two widely used large-scale optimization test suites, CEC2010 and CEC2013, show that APLPSO significantly outperforms other state-of-the-art large-scale optimization algorithms, including the winners of the CEC2010 and CEC2012 competitions. Moreover, we apply APLPSO to a real-world large-scale portfolio optimization application to show its practical applicability.

大规模优化问题（Large scale optimization problems, lsop）是进化计算领域的一个重要课题，许多研究者设计了各种学习策略来尝试更有效地解决lsop问题。然而，大多数学习策略在整个进化过程中都是固定的学习模式，缺乏针对个体属性的适应性调整机制。事实上，不同的个体具有不同的开发或探索能力，适合不同的学习模式。因此，本文提出了自适应模式学习粒子群优化（APLPSO）来解决lsop问题。在APLPSO中，首先基于不同数量的学习范例生成多种学习模式，以丰富群体的学习多样性。然后，每个个体将评估学习模式，并自适应地选择适合自己的学习模式进行更新。在CEC2010和CEC2013两个广泛使用的大规模优化测试套件上的实验结果表明，APLPSO显著优于其他最先进的大规模优化算法，包括CEC2010和CEC2012比赛的获奖者。最后，将该算法应用于一个实际的大规模投资组合优化应用中，以验证其实用性。

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引用次数: 0

An efficient binary ant colony evolutionary algorithm for feature selection 一种高效的二元蚁群特征选择进化算法

IF 8.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation

Pub Date : 2026-01-10 DOI: 10.1016/j.swevo.2025.102258

Peichen Xiong , Zhen Liu , Weiqing Xiong

Feature selection is an optimization problem of finding the optimal feature subset with high computational complexity and belongs to the NP-hard problem. Efficient direct solutions are typically unavailable, necessitating the design of algorithms to tackle such issues. To address this, we propose an efficient binary ant colony evolutionary algorithm to solve the feature selection problem, called EBACEA. Drawing on nucleic acid coding principles and the double helix structure of DNA, a binary network for ant traversal is developed, simplified into a one-dimensional chain by adding complement operators, and a corresponding pheromone-update operator was designed. Compared to the complete graph space, this algorithm requires low intelligence of individual agents, and the set of feasible solution nodes need not be explicitly recorded. Moreover, based on binary coding and inspired by DNA genetic principles, the algorithm incorporates three genetic operators: crossover, mutation and selection, to yield richer sets of solutions, can mitigate the risks of premature convergence and overcome the limitations of evolutionary computation in local search. To validate the performance of the proposed algorithm, we evaluated twelve public datasets and compared the results with those of eight swarm intelligence algorithms. The findings demonstrate that the proposed algorithm exhibits favorable execution time and achieves superior accuracy. Additionally, further comparative analysis confirms the proposed algorithm’s effectiveness.

特征选择是一个计算复杂度很高的寻找最优特征子集的优化问题，属于np困难问题。通常没有有效的直接解决方案，因此需要设计算法来解决此类问题。为了解决这个问题，我们提出了一种有效的二元蚁群进化算法来解决特征选择问题，称为EBACEA。利用核酸编码原理和DNA的双螺旋结构，建立了蚂蚁遍历的二元网络，通过添加补算子将其简化为一维链，并设计了相应的信息素更新算子。与完全图空间相比，该算法对个体智能体的要求较低，且不需要明确记录可行解节点集。该算法以二进制编码为基础，受DNA遗传原理的启发，结合交叉、突变和选择三种遗传算子，产生更丰富的解集，减轻了早熟收敛的风险，克服了进化计算在局部搜索中的局限性。为了验证所提出算法的性能，我们评估了12个公共数据集，并将结果与8种群体智能算法的结果进行了比较。结果表明，该算法具有较好的执行时间和较高的精度。进一步的对比分析验证了算法的有效性。

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引用次数: 0

Evolutionary algorithm with domain-specific operators for UAV path planning 基于特定域算子的无人机路径规划进化算法

IF 8.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation

Pub Date : 2026-01-08 DOI: 10.1016/j.swevo.2025.102267

Daison Darlan , Oladayo S. Ajani , Rammohan Mallipeddi

Multi-objective UAV path planning is critical in practical applications such as surveillance, search-and-rescue missions, and environmental monitoring. However, the inherent complexity stemming from conflicting objectives, dynamic operational environments, and stringent mission constraints severely limits the efficacy of conventional evolutionary algorithms. Standard evolutionary operators typically fail to adequately respect domain-specific constraints, leading to infeasible or inefficient flight trajectories. Motivated by these limitations, this paper proposes specialized evolutionary operators tailored explicitly for multi-objective UAV path planning. We introduce a novel crossover operator that strategically employs the A* algorithm to generate feasible offspring paths between selected waypoints from parent solutions. Additionally, we present an adaptive polynomial mutation mechanism that dynamically controls exploration and exploitation by adjusting the mutation factor progressively across generations. Complementing this, we propose a secondary mutation operator utilizing A* to refine path segments effectively. Comprehensive ablation studies demonstrate the synergistic advantage of these innovations. Extensive evaluations on a realistic benchmark environment illustrate that our approach achieves significant enhancements, validated through substantial improvements in the hypervolume metric. Our findings confirm that embedding domain-specific intelligence into evolutionary operators markedly advances the state-of-the-art in multi-objective UAV path planning.

多目标无人机路径规划在监视、搜救任务和环境监测等实际应用中至关重要。然而，冲突的目标、动态的作战环境和严格的任务约束所产生的内在复杂性严重限制了传统进化算法的有效性。标准进化算子通常不能充分尊重特定领域的约束，导致不可行或低效的飞行轨迹。基于这些局限性，本文提出了专门针对多目标无人机路径规划的进化算子。我们引入了一种新的交叉算子，该算子策略性地使用a *算法在父解的选定路径点之间生成可行的子代路径。此外，我们提出了一种自适应多项式突变机制，通过逐步调整突变因子来动态控制探索和开发。作为补充，我们提出了一个利用a *的二次变异算子来有效地细化路径段。综合消融研究证明了这些创新的协同优势。在现实基准测试环境中进行的广泛评估表明，我们的方法实现了显著的增强，并通过对hypervolume度量的实质性改进进行了验证。我们的研究结果证实，在进化算子中嵌入特定领域的智能显著提高了多目标无人机路径规划的水平。

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引用次数: 0

Evaluation-based multi-objective optimization for responsive-resilient supply chain network under hybrid uncertainty 混合不确定性下响应弹性供应链网络基于评价的多目标优化

IF 8.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation

Pub Date : 2026-01-08 DOI: 10.1016/j.swevo.2025.102274

Yurong Guo, Keping Zhou

Designing supply chains that remain responsive and resilient under hybrid uncertainty constitutes a complex multi-objective optimization challenge. This study introduces an evaluation-driven evolutionary framework that integrates metaheuristic search with performance feedback learning. A multi-objective mixed-integer programming model is formulated to simultaneously minimize total cost and maximize responsiveness and resilience. To effectively manage stochastic and fuzzy uncertainty, an improved fuzzy robust stochastic programming (IFRSP) model is developed, incorporating adaptive penalty terms to enhance feasibility robustness. A novel hybrid optimization–evaluation mechanism couples multi-objective optimization framework with a CRITIC–TOPSIS evaluator, where real-time performance feedback dynamically guides the evolutionary process toward high-quality Pareto solutions. Four representative multi-objective meta-heuristic algorithms are embedded and benchmarked. Computational results reveal that the proposed framework exhibits superior convergence diversity, stability, and decision robustness compared to classical approaches. The proposed methodology establishes a self-adaptive learning mechanism between optimization and evaluation, advancing the integration of metaheuristic evolution, performance assessment, and hybrid uncertainty modeling. This work offers a generalizable paradigm for intelligent decision-making in large-scale, uncertainty-driven optimization environments.

设计在混合不确定性下保持响应和弹性的供应链是一个复杂的多目标优化挑战。本研究引入了一个评估驱动的进化框架，将元启发式搜索与绩效反馈学习相结合。建立了一个多目标混合整数规划模型，以使总成本最小化，同时使响应能力和弹性最大化。为了有效地管理随机和模糊不确定性，提出了一种改进的模糊鲁棒随机规划（IFRSP）模型，并引入自适应惩罚项来增强可行性鲁棒性。一种新的混合优化-评估机制将多目标优化框架与critical - topsis评估器相结合，其中实时性能反馈动态引导进化过程走向高质量的Pareto解。嵌入了四种具有代表性的多目标元启发式算法并对其进行了基准测试。计算结果表明，与经典方法相比，该框架具有更好的收敛多样性、稳定性和决策鲁棒性。该方法在优化与评价之间建立了一种自适应学习机制，促进了元启发式进化、绩效评估和混合不确定性建模的融合。这项工作为大规模、不确定性驱动的优化环境中的智能决策提供了一个可推广的范例。

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引用次数: 0

A multi-objective evolutionary algorithm with clustering-based archiving and adaptive search mechanism 基于聚类归档和自适应搜索机制的多目标进化算法

IF 8.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation

Pub Date : 2026-01-08 DOI: 10.1016/j.swevo.2025.102277

Yiting Zeng, Peng Shao, Shaoping Zhang

To address the challenges of unstable archiving mechanisms and the difficulty of balancing diversity and convergence in multi-objective evolutionary algorithms, this paper proposes a multi-objective evolutionary algorithm with clustering-based archiving and an adaptive search mechanism based on Harris Hawks optimization (MOCAS/HHO). Building on the framework of the Harris Hawks Optimization (HHO), MOCAS/HHO employs k-medoids clustering to update the archive, where representative solutions at the cluster centers are preserved to improve solution diversity. Subsequently, MOCAS/HHO identifies ‘valuable solutions’ from the archive to guide the population toward the correct search direction. Based on the proportion and saturation of the ‘valuable solutions’, a regulatory factor is introduced to perturb the escape energy E, enabling the algorithm to adaptively adjust its search direction. Moreover, leaders are randomly selected from the valuable solutions to enhance stability and the global search capability of MOCAS/HHO. For the performance evaluation, MOCAS/HHO is compared with 9 algorithms on 25 benchmark functions, using IGD and HV metrics and statistical analysis. MOCAS/HHO outperforms MOHHO on approximately 88 % of the selected 2–3 objective functions, while achieving superior performance on all chosen 4-objective high-dimensional functions. For the Car side impact design problem, MOCAS/HHO improves IGD by 24.3 % over MOEDO; for the Liquid-rocket single element injector design, it improves IGD by 65.95 % over MOGWO; and for Conceptual marine design, it ranks second in IGD to MOEA/D. Overall, these results indicate that MOCAS/HHO achieves a good balance between convergence and diversity across both benchmark test functions and practical engineering applications.

针对多目标进化算法中归档机制不稳定以及难以平衡多样性和收敛性的问题，提出了一种基于聚类归档和基于Harris Hawks优化的自适应搜索机制的多目标进化算法（MOCAS/HHO）。在Harris Hawks Optimization （HHO）框架的基础上，MOCAS/HHO采用k- medioids聚类更新存档，其中保留了集群中心的代表性解决方案，以提高解决方案的多样性。随后，MOCAS/HHO从档案中识别出“有价值的解决方案”，以引导人们朝着正确的搜索方向前进。根据“有价解”的比例和饱和度，引入调节因子扰动逃逸能E，使算法能够自适应调整搜索方向。此外，从有价值的解决方案中随机选择领导者，以增强MOCAS/HHO的稳定性和全局搜索能力。为了进行性能评价，利用IGD和HV指标和统计分析，在25个基准函数上比较了MOCAS/HHO与9种算法的性能。在所选的2-3个目标函数中，MOCAS/HHO在约88%的目标函数上优于MOHHO，而在所有所选的4-目标高维函数上均取得了优异的性能。对于汽车侧面碰撞设计问题，moas /HHO比MOEDO提高了24.3%的IGD；对于液体火箭单元件喷射器设计，IGD比MOGWO提高65.95%；船舶概念设计IGD排名第二，仅次于MOEA/D。总体而言，这些结果表明，在基准测试功能和实际工程应用中，MOCAS/HHO在收敛性和多样性之间取得了良好的平衡。

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引用次数: 0

A hybrid surrogate-assisted evolutionary algorithm for technician routing and scheduling problems 技术人员路由调度问题的混合代理辅助进化算法

IF 8.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation

Pub Date : 2026-01-01 DOI: 10.1016/j.swevo.2025.102280

Engin PEKEL

Technician routing and scheduling problems (TRSP) that involve time windows and skill-based requirements represent complex optimization tasks frequently encountered in areas such as maintenance and field service operations. Because these problems belong to the class of NP-hard combinatorial challenges, obtaining exact solutions within realistic scales is computationally prohibitive. To address this challenge, the present study proposes a hybrid surrogate-assisted evolutionary algorithm (HSAEA) that combines several artificial intelligence (AI) strategies to obtain high-quality solutions more efficiently. At its core, the framework employs a Genetic Algorithm (GA) that is specifically designed to allocate and schedule service jobs among available technicians. To enhance this GA, the paper incorporates: a surrogate model – a Multi-Layer Perceptron (MLP) regressor trained on domain-specific features – to approximate the expensive fitness evaluation of candidate schedules; a reinforcement learning (RL) based adaptive mutation operator selection mechanism that dynamically chooses mutation operators based on their past performance, aiming to balance exploration and exploitation; and a simulated annealing (SA) procedure applied as a local search for intensification, refining individual solutions by exploiting neighborhood improvements. The proposed method is formalized and each component’s role is detailed. To sum up, the proposed hybrid framework combines several complementary ideas: surrogate models are used to cut down computational effort, reinforcement learning helps direct the search process more intelligently, and simulated annealing refines the solutions at a local level. When tested on standard benchmark datasets, the method consistently produced better-quality solutions than the baseline algorithms, and it did so within practical computation times.

技术人员路线和调度问题（TRSP）涉及时间窗口和基于技能的要求，是维护和现场服务操作等领域经常遇到的复杂优化任务。由于这些问题属于NP-hard组合挑战的范畴，在实际尺度内获得精确解在计算上是禁止的。为了应对这一挑战，本研究提出了一种混合代理辅助进化算法（HSAEA），该算法结合了几种人工智能（AI）策略，以更有效地获得高质量的解决方案。该框架的核心是采用遗传算法（GA），该算法专门用于在可用的技术人员之间分配和调度服务工作。为了增强这种遗传算法，本文采用了：一个代理模型-一个基于特定领域特征训练的多层感知器（MLP）回归器-来近似候选时间表的昂贵适应度评估；基于强化学习（RL）的自适应突变算子选择机制，该机制根据突变算子过去的表现动态选择突变算子，旨在平衡探索和利用；模拟退火（SA）程序应用于局部搜索强化，通过利用邻域改进来改进单个解决方案。对所提出的方法进行了形式化描述，并详细说明了每个组件的作用。综上所述，所提出的混合框架结合了几个互补的思想：使用代理模型来减少计算工作量，强化学习有助于更智能地指导搜索过程，模拟退火在局部级别改进解决方案。当在标准基准数据集上进行测试时，该方法始终产生比基线算法质量更好的解决方案，并且在实际计算时间内做到了这一点。

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引用次数: 0

Opposition based learning for metaheuristic algorithms: Theory, variants, applications, and performance evaluation 基于对立学习的元启发式算法：理论、变体、应用和性能评估

IF 8.5 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Swarm and Evolutionary Computation

Pub Date : 2026-01-01 DOI: 10.1016/j.swevo.2025.102271

Rebika Rai , Buddhadev Sasmal , Arunita Das , Totan Bharasa , Krishna Gopal Dhal , Prabir Kumar Naskar

Despite the widespread popularity of Metaheuristic Algorithms (MAs) in solving complex optimization problems, several challenges persist. These challenges include slow convergence rates, limited exploration capabilities, and premature convergence. Opposition-Based Learning (OBL) is a moderately novel and promising method that has the potential to boost the effectiveness of different soft computing algorithms, including Fuzzy Logic, Artificial Neural Networks, and Evolutionary Algorithms. OBL has demonstrated its ability to enhance the performance of these algorithms effectively. OBL has shown that it can effectively improve the performance of these algorithms. The process of OBL entails producing a group of solutions that are opposite in nature to each proposed solution, which helps to broadens the range of population and makes it easier to explore the search area. In this article, we give a thorough analysis of the OBL technique and how it is used in MAs. We talk about the theoretical underpinnings of OBL and the various variations that have been put forth in the literature. Additionally, we provide an overview of recent research studies that have applied OBL to a broad spectrum of optimization problems. We conclude by exploring into the possible future research paths for OBL and its potential applications in diverse fields. To accomplish this, a total of 168 research papers on MAs with OBL, published in various journals and conferences between 2021 and 2025, were reviewed. From the study, it is clear that OBL is an effective and efficient strategy for enhancing the performance of MAs and has the potential to advance the field of optimization further.

尽管元启发式算法（MAs）在解决复杂优化问题方面广泛流行，但仍存在一些挑战。这些挑战包括收敛速度慢、勘探能力有限和过早收敛。基于对立的学习（OBL）是一种比较新颖和有前途的方法，有可能提高不同软计算算法的有效性，包括模糊逻辑、人工神经网络和进化算法。OBL已经证明了它能够有效地提高这些算法的性能。实验表明，OBL可以有效地提高这些算法的性能。OBL的过程需要产生一组解决方案，这些解决方案在性质上与每个提出的解决方案相反，这有助于扩大人口范围，并使探索搜索区域变得更容易。在本文中，我们对OBL技术及其在MAs中的应用进行了全面的分析。我们讨论了OBL的理论基础以及文献中提出的各种变体。此外，我们还概述了最近将OBL应用于广泛的优化问题的研究。最后，我们探讨了OBL未来可能的研究路径及其在不同领域的潜在应用。为了实现这一目标，共审查了2021年至2025年期间在各种期刊和会议上发表的168篇关于具有OBL的MAs的研究论文。从研究中可以清楚地看出，OBL是一种有效和高效的策略，可以提高MAs的性能，并有可能进一步推动优化领域的发展。

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引用次数: 0