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arXiv 2025年

作者： Ahouei, Saba Sadeghi Antipov, Denis Neumann, Aneta Neumann, Frank Optimisation and Logistics School of Computer and Mathematical Sciences The University of Adelaide Adelaide Australia LIP6 Sorbonne University Paris France

Algorithm selection is crucial in the field of optimization, as no single algorithm performs perfectly across all types of optimization problems. Finding the best algorithm among a given set of algorithms for a given problem requires a detailed analysis of the problem’s features. To do so, it is important to have a diverse set of benchmarking instances highlighting the difference in algorithms’ performance. In this paper, we evolve diverse benchmarking instances for chance-constrained optimization problems that contain stochastic components characterized by their expected values and variances. These instances clearly differentiate the performance of two given algorithms, meaning they are easy to solve by one algorithm and hard to solve by the other. We introduce a (µ+1) EA for feature-based diversity optimization to evolve such differentiating instances. We study the chance-constrained maximum coverage problem with stochastic weights on the vertices as an example of chance-constrained optimization problems. The experimental results demonstrate that our method successfully generates diverse instances based on different features while effectively distinguishing the performance between a pair of algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： optimization algorithms

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An Adaptive Dropout Approach for High-Dimensional Bayesian optimization

arXiv

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arXiv 2025年

作者： Huang, Jundi Zhan, Dawei School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China

Bayesian optimization (BO) is a widely used algorithm for solving expensive black-box optimization problems. However, its performance decreases significantly on high-dimensional problems due to the inherent high-dimensionality of the acquisition function. In the proposed algorithm, we adaptively dropout the variables of the acquisition function along the iterations. By gradually reducing the dimension of the acquisition function, the proposed approach has less and less difficulty to optimize the acquisition function. Numerical experiments demonstrate that AdaDropout effectively tackle high-dimensional challenges and improve solution quality where standard Bayesian optimization methods often struggle. Moreover, it achieves superior results when compared with state-of-the-art high-dimensional Bayesian optimization approaches. This work provides a simple yet efficient solution for high-dimensional expensive optimization. Copyright © 2025, The Authors. All rights reserved.

关键词： optimization algorithms

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Design optimization of cam-follower mechanisms using Rao algorithms and their variants

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EVOLUTIONARY INTELLIGENCE 2024年第2期17卷 745-770页

作者： Rao, R., V Pawar, R. B. SV Natl Inst Technol Dept Mech Engn Surat 395007 Gujarat India

This paper presents the design optimization of cam-follower mechanisms (CFM) with eccentric roller type follower using recently developed advanced optimization algorithms, namely Rao, SAMP-Rao, and QO-Rao algorithms. Four types of follower motion law i.e., cycloidal, modified harmonic, 3-4-5 degree polynomial, and 4-5-6-7 degree polynomial motion, are considered. The CFM is optimized to minimize three objectives such as the input torque needed to rotate the cam, the radius of the pitch circle of the cam, and the maximum contact stress. The problem has five continuous design variables, namely the roller radius (R-g), the radius of cam base circle (R-b), the distance between the follower bearing and the center of the cam (q), the eccentricity of the follower (e), and the length of follower bearing (b). Eight design constraints related to the geometry of the cam mechanism, the pressure angle, the efficiency of the mechanism, the curvature radius of the pitch curve, and the maximum contact stress, are considered. The computational results obtained using Rao algorithms and their variants are compared with other advanced optimization algorithms such as the salp swarm algorithm (SSA), ant lion optimizer (ALO), moth-flame optimization (MFO), multi verse optimizer (MVO), evaporation rate water cycle algorithm (ER-WCA), grey wolf optimizer (GWO), and mine blast algorithm (MBA). The comparison of optimization results reveals that the optimum value of a fitness function obtained using Rao algorithms and their variants is superior to GWO, ALO, MFO, SSA, ER-WCA, MBA, and MVO for all four cases considered. The optimum fitness function value of the CFM with case III is reduced by 3.14%, 4.13%, and 7.61% compared to the CFM's fitness function value with the case I, case II, and case IV, respectively. Hence, the 3-4-5 degree polynomial motion of the follower is effective for better performance of the CFM. Also, the average time required for Rao algorithms and their variants is compar

关键词： Rao algorithms optimization algorithms Metaheuristics Engineering design problems Cam-follower mechanism

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Maximum Independent Set Using Hummingbird optimization 4th

Maximum Independent Set Using Hummingbird Optimization

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4th International Conference on Advanced Network Technologies and Intelligent Computing, ANTIC 2024

作者： Verma, Ritika Mahato, Dharamendra Prasad Department of Computer Science and Engineering National Institute of Technology Hamirpur HP Hamirpur177 005 India

ISBN: (纸本)9783031837821

The Maximum Independent Set problem in distributed systems is an NP-hard problem. This paper uses a soft computing approach to solve this problem. In this paper, we use Hummingbird optimization to solve the problem. We also compare the proposed algorithm with deterministic algorithm, non-deterministic algorithms and other soft computing algorithms. We tested the algorithms on DIMACS benchmark datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： optimization algorithms

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Research on Firefly Algorithm Enhancement by Diversifying Swarm 8th

Research on Firefly Algorithm Enhancement by Diversifying Sw...

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8th International Conference on Cognitive Computing, ICCC 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Pan, Xiuqin Ren, Shuangqing School of Information Engineering Minzu University of China Beijing100081 China

ISBN: (纸本)9783031779534

In response to the issues faced by the traditional Firefly Algorithm (FA), particularly its tendency to become trapped in local optima and slow convergence during the global optimization process, especially for high-dimensional optimization problems, an improved version of the algorithm is proposed—Diverse Swarm Firefly Algorithm (DSFA). Enhancements to the algorithm are twofold: firstly, a new adaptive randomization parameter strategy is designed to meet the search requirements at different stages;secondly, a novel random search mechanism is introduced to enhance the diversity and quality of solutions during the iterative process. To validate the effectiveness of DSFA, simulation experiments were conducted on eight widely recognized benchmark test functions. The experimental results demonstrate that, compared to traditional intelligent optimization algorithms and another version of the improved Firefly Algorithm, DSFA exhibits significant advantages in both convergence speed and solution precision. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： optimization algorithms

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Theoretical Approach on Assessing the Accuracy of the Shortest Path Non-Optimal Algorithm for 2-Dimensional Grids with Obstacles 24

Theoretical Approach on Assessing the Accuracy of the Shorte...

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8th International Conference on algorithms, Computing and Systems, ICACS 2024

作者： Mo, Chenghao Oyster River High School DurhamNH United States

ISBN: (纸本)9798400718304

In many applications such as urban navigation and robotics, finding the shortest path in a 2D grid is crucial but computationally expensive using traditional optimal algorithms like Floyd-Warshall or Dijkstra. These traditional algorithms guarantee to find the shortest path at the cost of time complexity, leading to a time-consuming computation, particularly for large-scale grids. Non-optimal algorithms that trade accuracy for speed have emerged to address the issue. However, the impact of grid obstacle density on the accuracy of the algorithms has not been well understood. This paper presents a theoretical framework for evaluating the accuracy of two non-optimal algorithms. By integrating theoretical analysis with extensive experimental data, this paper demonstrates how obstacle density influences algorithm performance, and proposes a methodology to select the best non-optimal algorithms based on the grid obstacle density. The theoretical framework has practical implications for applications requiring rapid path finding in complex environments. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： optimization algorithms

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An iterated greedy algorithm based on NSGA-II for distributed hybrid flow shop scheduling problem 3

An iterated greedy algorithm based on NSGA-II for distribute...

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3rd International Conference on Communications, Information System, and Data Science, CISDS 2024

作者： Liu, Dandan Liang, Xu Zou, Zhiyuan School of Computing Beijing Information Science and Technology University Beijing China

ISBN: (数字)9781510688322

ISBN: (纸本)9781510688315

The distributed hybrid flow shop scheduling problems (DHFSP) widely exist in various industrial production processes, and thus have received widespread attention. However, studies on HFSP considering green objective in distributed production environment are quite limited. Therefore, this paper investigated a distributed hybrid flow shop scheduling problem with objectives of minimization the makespan and total energy consumption (TEC). To solve it, an iterated greedy algorithm based on NSGA-II (NSGAIG) is developed. In the proposed algorithm, a random initialization strategy is used to generate the initial solution. Then, starting from the population initialization, a multi-objective local search is carried out for the current optimal solution in the population to obtain the global optimal solution. Next, random variation method is used to increase the exploration space of the algorithm. Finally, the proposed NSGAIG algorithm is compared with other multi-objective optimization algorithms. Experimental results indicate that the proposed NSGAIG outperforms its compared algorithms in solving this problem. © 2025 SPIE.

关键词： optimization algorithms

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A New Dual-Population Evolutionary Algorithm Leveraging Objective-Constraint Relationships for Constrained Multi-Objective optimization

SSRN

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SSRN 2025年

作者： Ye, Jialu Tan, Chaogui Xia, Yizhang Hou, Zhanglu Liu, Yuan Zou, Juan Hunan Engineering Research Center of intelligent System Optimization and Security Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Hunan National Applied Mathematics Center China Xiangtan University China

Population co-evolution strategies are widely used to handle constrained multi-objective optimization problems (CMOPs). However, existing coevolutionary algorithms oversimplify population collaboration and are rigid in evolving the auxiliary population, ignoring the importance of maintaining population diversity, especially for CMOPs with irregular feasible regions. To tackle this issue, we propose a new dual-population evolutionary algorithm, denoted as AACMO, which attempts to mine the relational features between the unconstrained Pareto front (UPF) and the constrained Pareto front (CPF) to better adapt to CMOPs with various constraints. Specifically, the method is categorized into two phases: a learning phase and an evolving phase. First, the main population (MP) aims to quickly learn the relationships between the UPF and the CPF by leveraging the advantages of different operators, namely, genetic algorithm operators and differential evolution operators, during the learning phase. Secondly, two auxiliary strategies are designed to collaborate with the MP during the evolving phase. These strategies are capable of employing a more effective co-evolution method to assist the MP in maintaining diversity, based on the degree of alignment between the learned UPF and CPF at the end of the learning phase. The effectiveness of AACMO is confirmed through comparative analysis with eight state-of-the-art algorithms across four CMOP benchmark suites. © 2025, The Authors. All rights reserved.

关键词： optimization algorithms

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An exponentially stable discrete-time primal-dual algorithm for distributed constrained optimization

arXiv

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arXiv 2025年

作者： Ren, Xiaoxing Bin, Michelangelo Notarnicola, Ivano Parisini, Thomas Department of Electrical and Electronic Engineering Imperial College London London United Kingdom Department of Electrical Electronic and Information Engineering University of Bologna Italy Department of Electronic Systems Aalborg University Denmark Department of Engineering and Architecture University of Trieste Trieste Italy

This paper studies a distributed algorithm for constrained consensus optimization that is obtained by fusing the Arrow-Hurwicz-Uzawa primal-dual gradient method for centralized constrained optimization and the Wang-Elia method for distributed unconstrained optimization. It is shown that the optimal primal-dual point is a semiglobally exponentially stable equilibrium for the algorithm, which implies linear convergence. The analysis is based on the separation between a slow centralized optimization dynamics describing the evolution of the average estimate toward the optimum, and a fast dynamics describing the evolution of the consensus error over the network. These two dynamics are mutually coupled, and the stability analysis builds on control theoretic tools such as time-scale separation, Lyapunov theory, and the small-gain principle. Our analysis approach highlights that the consensus dynamics can be seen as a fast, parasite one, and that stability of the distributed algorithm is obtained as a robustness consequence of the semiglobal exponential stability properties of the centralized method. This perspective can be used to enable other significant extensions, such as time-varying networks or delayed communication, that can be seen as "perturbations" of the centralized *** Codes 65K05, 93A14, 93A16, 90C25, 90C30, 90C35, 93D05, 93D23 © 2025, CC BY-NC-ND.

关键词： optimization algorithms

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Causal Bayesian optimization with Unknown Graphs

arXiv

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arXiv 2025年

作者： Durand, Jean Annadani, Yashas Bauer, Stefan Parbhoo, Sonali Imperial College London United Kingdom Germany

Causal Bayesian optimization (CBO) is a methodology designed to optimize an outcome variable by leveraging known causal relationships through targeted interventions. Traditional CBO methods require a fully and accurately specified causal graph, which is a limitation in many real-world scenarios where such graphs are unknown. To address this, we propose a new method for the CBO framework that operates without prior knowledge of the causal graph. Consistent with causal bandit theory, we demonstrate through theoretical analysis and that focusing on the direct causal parents of the target variable is sufficient for optimization, and provide empirical validation in the context of CBO. Furthermore we introduce a new method that learns a Bayesian posterior over the direct parents of the target variable. This allows us to optimize the outcome variable while simultaneously learning the causal structure. Our contributions include a derivation of the closed-form posterior distribution for the linear case. In the nonlinear case where the posterior is not tractable, we present a Gaussian Process (GP) approximation that still enables CBO by inferring the parents of the outcome variable. The proposed method performs competitively with existing benchmarks and scales well to larger graphs, making it a practical tool for real-world applications where causal information is incomplete. © 2025, CC BY-SA.

关键词： optimization algorithms

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