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8th IFIP TC 12 International Conference on Computer, Communication and Signal Processing, ICCCSP 2024

作者： Baskar, A. Panimalar Institute of Technology Tamil Nadu Poonamallee 600123 India

ISBN: (纸本)9783031736162

Population-based optimization algorithms start with initializing a random population and iteratively move towards the optimal solution. Most of these algorithms use the expression, "Lower Bound + random*(Upper Bound – Lower Bound)" to initialize the random solutions. This paper analyses four initialization strategies;the widely used one, the strategy used by "Jellfish Optimizer" and two newly proposed strategies. Among the newly proposed strategies, the first one is a modified version of the strategy used in the "Jelly Fish Optimizer" and the other one is based on a larger initial population combined with the concept of "Opposite Number". Fifty-five benchmark functions including real-world industrial problems are considered in this work for the performance analyses. The obtained results demonstrate the better performance of the larger initial population-based strategy that uses the "Opposite Number" concept in both constrained and unconstrained benchmark instances. For fair analysis, the number of function evaluations and population size are kept the same. © IFIP International Federation for Information Processing 2025.

关键词： optimization algorithms

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Typical optimization algorithms: A Survey 16th

Typical Optimization Algorithms: A Survey

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16th International Conference on Modelling, Identification and Control, ICMIC 2024

作者： Jia, Yongnan Luan, Xiaoyu Dong, Linjie Gao, Xiandong University of Science and Technology Beijing Beijing100083 China Wudi Runyu Water Group Co. Ltd. Shandong251900 China

ISBN: (纸本)9789819617760

optimization algorithms can provide practical solutions to enhance efficiency and performance in complex systems that involve multiple variables, constraints, and objectives. Therefore, relative research contents are increasingly inadequate for addressing the rising complexity of contemporary tasks. Consequently, researchers have developed numerous novel optimization algorithms in recent years, yielding promising experimental results. Firstly, this paper presents the fundamental concepts, developmental background, classification, and classic algorithms in the field of optimization. And then, this paper conducts an in-depth analysis of the current research status on classic optimization algorithms, including the genetic algorithm, ant colony optimization algorithm, simulated annealing algorithm, particle swarm optimization algorithm, and non-dominated sorting genetic algorithm II. The paper will summarize the performance characteristics, and potential applications of the above algorithms in the future. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： optimization algorithms

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Performance Analysis of Constrained Evolutionary Multi-objective optimization algorithms on Artificial and Real-World Problems 13th

Performance Analysis of Constrained Evolutionary Multi-obje...

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13th International Conference on Evolutionary Multi-Criterion optimization, EMO 2025

作者： Nan, Yang Ishibuchi, Hisao Pang, Lie Meng Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9789819635375

Most real-world optimization problems have multiple objectives and constraints. To address constrained multi-objective optimization problems (CMOPs), researchers have proposed many constrained evolutionary multi-objective optimization (EMO) algorithms. They usually evaluate the proposed constrained EMO algorithms on artificial CMOPs in the literature. As a result, some newly specialized constrained EMO algorithms are designed for those artificial problems. Our previous work demonstrated that an old EMO algorithm NSGA-II with a simple constraint handling mechanism can outperform some newly proposed algorithms on real-world CMOPs. In this paper, we further examine the performance of constrained EMO algorithms on artificial and real-world CMOPs. We also analyze the relationship between algorithms’ performance and problems’ properties. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： optimization algorithms

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The Unreasonable Effectiveness of Optimal Transport Distance in the Design of Multi-Objective Evolutionary optimization algorithms 4th

The Unreasonable Effectiveness of Optimal Transport Distance...

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4th International Conference on Numerical Computations: Theory and algorithms, NUMTA 2023

作者： Ponti, Andrea Archetti, Francesco Department of Economics Management and Statistics University of Milano-Bicocca Milan Italy Department of Computer Science Systems and Communication University of Milano-Bicocca Milan Italy

ISBN: (纸本)9783031812408

Most Multi-Objective Evolutionary algorithms (MOEAs) face significant challenges in many-objective optimization. MOEAs have random elements in the selection and crossover operators which endow them with global convergence properties. The key contribution of this paper is to integrate discrete probability distributions instead of just randomization in the design of the algorithm. This allows us to use the Wasserstein distance to drive the selection operator, instead of the Euclidean distance. Achieving balance between convergence and diversity is a key issue in evolutionary multi-objective optimization. The use of the Wasserstein distance constitutes a novel approach to the diversity management mechanism. This approach can be applied to the two main strategies: Pareto sampling, as in NSGA-II, and decomposition, as in MOEA/D, resulting in new algorithms NSGA-II/W and MOEA/D/W. The performance of the proposed algorithms is validated on a number of unconstrained and constrained benchmark problems with up to 10 objectives. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： optimization algorithms

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optimization algorithms FOR CARLESON AND SPARSE COLLECTIONS OF SETS

arXiv

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arXiv 2025年

作者： Honig, Eline A. Lorist, Emiel Delft Institute of Applied Mathematics Delft University of Technology P.O. Box 5031 Delft2600 GA Netherlands

Carleson and sparse collections of sets play a central role in dyadic harmonic analysis. We employ methods from optimization theory to study such collections. First, we present a strongly polynomial algorithm to compute the Carleson constant of a collection of sets, improving on the recent approximation algorithm of Rey [Rey24]. Our algorithm is based on submodular function minimization. Second, we provide an algorithm showing that any Carleson collection is sparse, achieving optimal dependence of the respective constants and thus providing a constructive proof of a result of Hänninen [Hän18]. Our key insight is a reformulation of the duality between the Carleson condition and sparseness in terms of the duality between the maximum flow and the minimum cut in a weighted directed *** Codes Primary: 42B25, 90C27, Secondary: 05C21 © 2025, CC BY.

关键词： optimization algorithms

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optimization algorithms based on double-integral revolutionary neurodynamics in deep learning

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IEEE/CAA Journal of Automatica Sinica 2025年

作者： Su, Dan Han, Jie Yang, Chunhua Gui, Weihua School of Automation Central South University Changsha410083 China

Deep neural networks are increasingly exposed to attack threats, and at the same time, the need for privacy protection is growing. As a result, the challenge of developing neural networks that are both robust and capable of strong generalization while maintaining privacy becomes pressing. Training neural networks under privacy constraints is one way to minimize privacy leakage, and one way to do this is to add noise to the data or model. However, noise may cause gradient directions to deviate from the optimal trajectory during training, leading to unstable parameter updates, slow convergence, and reduced model generalization capability. To overcome these challenges, we propose an optimization algorithm based on double-integral coevolutionary neurodynamics (DICND), designed to accelerate convergence and improve generalization in noisy conditions. Theoretical analysis proves the global convergence of the DICND algorithm and demonstrates its ability to converge to near-global minima efficiently under noisy conditions. Numerical simulations and image classification experiments further confirm the DICND algorithm's significant advantages in enhancing generalization performance. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： optimization algorithms

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A causal framework for stochastic local search optimization algorithms

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Computers and Operations Research 2025年 180卷

作者： Franzin, Alberto Stützle, Thomas Belgium

Despite the multitude of optimization algorithms available in the literature and the various approaches that study them, understanding the behaviour of an optimization algorithm and explaining its results are fundamental open questions in artificial intelligence and operations research. We argue that the body of available literature is already very rich, and the main obstacle to advancements towards answering those questions is its fragmentation. In this work, we focus on stochastic local search algorithms, a broad class of methods to compute good quality suboptimal solutions in a short time. We propose a causal framework that relates the entities involved in the solution of an optimization problem. We demonstrate how this conceptual framework can be used to relate many approaches aimed at understanding how stochastic local search algorithms work, and how it can be utilized to address open problems, both theoretical and practical. © 2025 Elsevier Ltd

关键词： optimization algorithms

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Performance Assessment of Population-Based Multiobjective optimization algorithms Using Composite Indicators

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IEEE Transactions on Evolutionary Computation 2025年

作者： Saborido, Rubén Ruiz, Ana B. González-Gallardo, Sandra Luque, Mariano Borrego, Antonio University of Málaga ITIS Software Calle Arquitecto Francisco Peñalosa 18 Málaga Spain Calle Ejido 6 Málaga29071 Spain Universidad de Málaga Department of Mathematical Analysis Statistics and Operations Research and Applied Mathematics Blvr. Louis Pasteur 31 Málaga Spain

The performance of population-based multiobjective optimization algorithms is usually evaluated using indicators assessing the quality of the approximation set generated according to convergence, cardinality, spread, and uniformity (the combination of the last two known as diversity). Since not all quality indicators can capture all these properties, we propose to aggregate already-existing indicators into a single measure informing about the algorithm's performance from a general perspective. To synthesize the desired quality indicators, we build three composite quality indicators (weak, strong, and mixed) based on the reference point approach. This approach enables the use of desirable value ranges for the aggregated quality indicators, defined by aspiration and reservation levels, that allow knowing which algorithms perform better, within, or worse than the desired limits. Each of the composite quality indicators proposed enables a different compensation degree among the aggregated indicators, and their joint use permits a deep insight into the algorithms' performance. In addition, we show that the weak and mixed composite indicators are Pareto-compliant, and the strong one is weakly Pareto-compliant if at least one of the aggregated indicators is Pareto-compliant. Finally, we demonstrate the benefits of our proposal when comparing many population-based algorithms on three-, five-, and eight-objective optimization problems. © 1997-2012 IEEE.

关键词： optimization algorithms

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Scalable Derivative-Free optimization algorithms with Low-Dimensional Subspace Techniques

arXiv

引用

arXiv 2025年

作者： Zhang, Zaikun

We re-introduce a derivative-free subspace optimization framework originating from Chapter 5 of the Ph.D. thesis [Z. Zhang, On Derivative-Free optimization Methods, Ph.D. thesis, Chinese Academy of Sciences, Beijing, 2012] of the author under the supervision of Ya-xiang Yuan. At each iteration, the framework defines a (low-dimensional) subspace based on an approximate gradient, and then solves a subproblem in this subspace to generate a new iterate. We sketch the global convergence and worst-case complexity analysis of the framework, elaborate on its implementation, and present some numerical results on solving problems with dimension as high as 104 using only inaccurate function values. © 2025, CC BY-NC-ND.

关键词： optimization algorithms

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Evolving Hard Maximum Cut Instances for Quantum Approximate optimization algorithms

arXiv

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arXiv 2025年

作者： Pan, Shuaiqun Patel, Yash J. Neumann, Aneta Neumann, Frank Bäck, Thomas Wang, Hao LIACS Leiden University Leiden Netherlands The University of Adelaide Adelaide Australia

Variational quantum algorithms, such as the Recursive Quantum Approximate optimization Algorithm (RQAOA), have become increasingly popular, offering promising avenues for employing Noisy Intermediate-Scale Quantum devices to address challenging combinatorial optimization tasks like the maximum cut problem. In this study, we utilize an evolutionary algorithm equipped with a unique fitness function. This approach targets hard maximum cut instances within the latent space of a Graph Autoencoder, identifying those that pose significant challenges or are particularly tractable for RQAOA, in contrast to the classic Goemans and Williamson algorithm. Our findings not only delineate the distinct capabilities and limitations of each algorithm but also expand our understanding of RQAOA’s operational limits. Furthermore, the diverse set of graphs we have generated serves as a crucial benchmarking asset, emphasizing the need for more advanced algorithms to tackle combinatorial optimization challenges. Additionally, our results pave the way for new avenues in graph generation research, offering exciting opportunities for future explorations. © 2025, CC BY.

关键词： optimization algorithms

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