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5th Asia Conference on Information Engineering, ACIE 2025

作者： Beskirli, Ayse Karamanoǧlu Mehmetbey University Faculty of Medicine Department of Medical Education and Informatics Karaman Turkey

ISBN: (纸本)9798331528409

There are many meta-heuristic algorithms inspired by nature for solving optimization problems. One of these algorithms is the Gorilla Troop optimization (GTO) algorithm, which has been recently proposed for solving continuous optimization problems and is inspired by the behavior of gorilla troops in nature. GTO includes various mechanisms to utilize the exploration and exploitation processes efficiently. Due to this feature, although the success varies according to the problem size, it is generally known that GTO is a successful algorithm. In this study, levy flight and roulette fitness distance balance (LRFDB) are added to the basic GTO to explore the search space more efficiently. With this method added to GTO, the algorithm is named LRFDB-GTO. Thus, it is aimed to strengthen the global search capability of the algorithm. The performance of LRFDBGTO was analyzed on test functions. Then it was applied to real world problems. The results of LRFDB-GTO and the original GTO are compared in tables. At the same time, the convergence curves and box plots of the algorithms are presented in figures. The experimental results show that the LRFDB-GTO algorithm performs better than the original GTO. © 2025 IEEE.

关键词： optimization algorithms

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Spatiotemporal assessment of landslide susceptibility in Southern Sichuan, China using SA-DBN, PSO-DBN and SSA-DBN models compared with DBN model

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ADVANCES IN SPACE RESEARCH 2022年第8期69卷 3071-3087页

作者： Li, Jiaying Wang, Weidong Chen, Guangqi Han, Zheng Cent South Univ Sch Civil Engn Changsha 410075 Hunan Peoples R China Cent South Univ MOE Key Lab Engn Struct Heavy Haul Railway Changsha 410075 Hunan Peoples R China Kyushu Univ Dept Civil & Struct Engn Fukuoka 8190395 Japan Chengdu Univ Technol State Key Lab Geohazard Prevent & Geoenvironm Pro Chengdu 610000 Peoples R China

Landslide is one of the most serious geo-hazards, and the landslide susceptibility assessment (LSA) is an existing effective method to efficiently mitigate the loss caused by landslides. This study develops the improved deep belief network (DBN) models by selecting the optimal model hyperparameter to improve the accuracy of LSA. Evaluation factors of the LSA are selected from fifteen influencing fac-tors by chi-square test, multicollinearity test and out-of-bag error. 30% data of the study area are selected randomly as the training data to assess the landslide susceptibility of each grid in the study area. The spatial LSA is then obtained by integrating the DBN models with three optimization algorithms, namely the simulated annealing (SA), particle swarm optimization (PSO) and sparrow search algorithm (SSA). The assessment results obtained using DBN and improved DBN models are thus compared and verified using the receiver operating characteristic (ROC) curve and seed cell area index. It shows that the three improved DBN models outperform the DBN model, which demonstrates the ability of optimization algorithms to improve model performance, and the SSA-DBN model achieves the highest assessment accuracy, followed by the PSO-DBN and SA-DBN models. Meanwhile, the effective rainfall model and peak ground acceleration are respectively employed to evaluate the impact of two inducing factors, namely the rainfall and earthquake, and the temporal LSA is thus obtained. The spatiotemporal LSA map is then generated by coupling the optimal spatial LSA map and temporal LSA map. Therefore, the present study further explores the proposed improved methods and offers instructions for spatiotemporal LSA. (c) 2022 COSPAR. Published by Elsevier B.V. All rights reserved.

关键词： DBN model optimization algorithms SA-DBN PSO-DBN SSA-DBN Spatiotemporal LSA

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Local Optimisation Based Detection of Weak Targets in Isolated Forests 24

Local Optimisation Based Detection of Weak Targets in Isolat...

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8th International Conference on Electronic Information Technology and Computer Engineering, EITCE 2024

作者： Hu, Jurong Xing, Yanxiao Li, Yuxiao School of Information Science and Engineering Hohai University Jiangsu Nanjing China School of Computer and Software Hohai University Jiangsu Nanjing China

ISBN: (纸本)9798400710094

Aiming at the problem that the iForest algorithm is not sensitive enough to local anomalies and produces a large number of false alarms in the detection results on some low sea state datasets, this paper proposes the iForest algorithm based on an improved splitting strategy. The algorithm introduces the Gini impurity, incorporates the Gini impurity parameter into the splitting strategy, and is more targeted in the selection of splitting attributes and splitting points, thus better adapting to the problem of detecting weak targets under different sea states, and has an excellent performance of high accuracy, low time duration, and low false alarm rate compared with the traditional methods in anomaly detection. © 2024 Copyright held by the owner/author(s).

关键词： optimization algorithms

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Forest Covers and Bounded Forest Covers 50th

Forest Covers and Bounded Forest Covers

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50th International Conference on Current Trends in Theory and Practice of Computer Science, SOFSEM 2025

作者： Gaur, Daya Ram Gorain, Barun Patra, Shaswati Singh, Rishi Ranjan University of Lethbridge Alberta Canada Indian Institute of Technology Bhilai Chattisgarh Raipur India

ISBN: (纸本)9783031826696

We study approximation algorithms for the forest cover and bounded forest cover problems. A probabilistic 2+ϵ approximation algorithm for the forest cover problem is given using the method of dual fitting. A deterministic algorithm with a 2-approximation ratio that rounds the optimal solution to a linear program is given next. The 2-approximation for the forest cover is then used to give a 6-approximation for the bounded forest cover problem. The use of the probabilistic method to develop the 2+ϵ approximation algorithm may be of independent interest. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： optimization algorithms

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PEbfs: Implement High-Performance Breadth-First Search on PEZY-SC3s 24th

PEbfs: Implement High-Performance Breadth-First Search on P...

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24th International Conference on algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Guo, Weihao Wang, Qinglin Liu, Xiaodong Peng, Muchun Yang, Shun Liang, Yaling Shi, Yongzhen Cao, Ligang Liu, Jie Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha410073 China Engineering Research Center for National Fundamental Software National University of Defense Technology Changsha410073 China

ISBN: (纸本)9789819615506

The breadth-first search (BFS) algorithm is a fundamental algorithm in graph theory, and it’s parallelization can significantly improve performance. Therefore, there have been numerous efforts to leverage the powerful parallel computing capabilities of hardware like GPGPU to implement high-performance BFS algorithms. However, the energy efficiency is relatively low due to the high power consumption of the platforms on which the algorithm is adapted to. To deal with these challenges, this paper introduces PEbfs that is a high-performance BFS algorithm based on the PEZY-SC3s efficient processor. We integrated three search algorithms, two algorithm optimization strategies, and a directional optimization scheme into PEbfs. Through multiple evaluations of the performance of PEbfs on the public SNAP dataset, the results demonstrate that the average energy efficiency ratio of PEbfs is higher than that of Enterprise and Tigr, the two most advanced implementations on Nvidia’s GPGPU: It achieves 3.08× the average energy efficiency ratio of Enterprise and 4.53× that of Tigr. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： optimization algorithms

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Hybrid Binary SGO-GA for solving MAX-SAT problem 4

Hybrid Binary SGO-GA for solving MAX-SAT problem

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Das, Rhiddhi Prasad Paul, Anuruddha Jena, Junali Jasmine Dash, Bibhuti Bhusan De, Utpal Chandra Gourisaria, Mahendra Kumar School of Computer Engineering KIIT Deemed to Be University Odisha Bhubaneswar India

The Maximum Satisfiability Problem (MAX-SAT) is a crucial NP-hard optimization problem with applications in artificial intelligence, circuit design, scheduling, and combinatorial optimization. In this work, we provide a unique hybrid strategy that blends Genetic algorithms (GA) with Social Group optimization (SGO) algorithm to effectively solve the MAX-SAT problem. The SGO algorithm, inspired by the social behavior of groups, excels in exploring diverse regions of the search space. w used a binary variant of SGO i.e. Binary-SGO which is defined specifically for binary search spaces, while GA leverages evolutionary principles to exploit local optima through selection, crossover, and mutation. By integrating the exploration capabilities of SGO with the exploitation strengths of GA, the hybrid approach strikes an optimal balance between global and local search. Extensive experimental evaluations conducted on standard MAX-SAT benchmarks demonstrate that our hybrid algorithm outperforms several existing state-of-the-art meta-heuristic algorithms. Hybrid BSGO-GA achieved the highest average fitness values, with an average accuracy of 99.7% in Experiment 1, 99.61% in Experiment 2, and 99.21% in Experiment 3 and achieved complete satisfiability in 55 out of 75 cases in Experiment 1, 42 out of 75 cases in Experiment 2, and 7 out of 75 cases in Experiment 3. This approach demonstrates the potential of hybrid metaheuristics in addressing complex optimization problems and offers a robust framework for tackling other NP-hard problems. © 2025 The Authors. Published by Elsevier B.V.

关键词： optimization algorithms

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A Stochastic Gradient Tracking Algorithm for Decentralized optimization With Inexact Communication

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IEEE Transactions on Automatic Control 2025年

作者： Shah, Suhail M. Bollapragada, Raghu University of Texas at Austin Operations Research and Industrial Engineering Program TX78705 United States

Decentralized optimization is typically studied under the assumption of noise-free transmission. However, real-world scenarios often involve the presence of noise due to factors such as additive white Gaussian noise channels or probabilistic quantization of transmitted data. These sources of noise have the potential to degrade the performance of decentralized optimization algorithms if not effectively addressed. In this paper, we focus on the noisy communication setting and propose an algorithm that bridges the performance gap caused by communication noise while also mitigating other challenges like data heterogeneity. We establish theoretical results of the proposed algorithm that quantify the effect of communication noise and gradient noise on the performance of the algorithm. Notably, our algorithm achieves the optimal convergence rate for minimizing strongly convex, smooth functions in the context of inexact communication and stochastic gradients. Finally, we illustrate the superior performance of the proposed algorithm compared to its state-of-the-art counterparts on machine learning problems using MNIST and CIFAR-10 datasets. © 2025 IEEE.

关键词： optimization algorithms

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Research on the Comprehensive Performance Evaluation Method of Genetic Algorithm Based on Euclidean Distance Minimization 8

Research on the Comprehensive Performance Evaluation Method ...

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8th International Workshop on Control Engineering and Advanced algorithms, IWCEAA 2024

作者： Gao, Donglin Qin, Honglei Li, Fuhe School of Electronics and Information Engineering Bei Hang University China Ship Research and Development Academe Beijing China School of Electronics and Information Engineering Bei Hang University Beijing China China Ship Research and Development Academe Beijing China

ISBN: (纸本)9798331528881

In order to solve the problem of selecting optimal control parameters for various genetic operators in genetic algorithms, this article propose a comprehensive performance evaluation function based on minimizing Euclidean distance in three-dimensional space, Quantitatively evaluate the performance of algorithms with different control parameters by using comprehensive optimization instead of one or two aspects of optimization. The correctness of this method was verified through experiments of directly selecting proportional parameters through selection operators and determining the probability of crossover operators. © 2024 IEEE.

关键词： optimization algorithms

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Visual Explanations of Some Problematic Search Behaviors of Frequently-Used EMO algorithms 13th

Visual Explanations of Some Problematic Search Behaviors of ...

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

作者： Ishibuchi, Hisao Pang, Lie Meng Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9789819635375

Recently, evolutionary multi-objective optimization (EMO) algorithms have been used in various application fields. Whereas many new EMO algorithms are proposed every year, well-known EMO algorithms such as NSGA-II, MOEA/D, SMS-EMOA and NSGA-III have been still frequently used in application papers as shown by their number of citations. Since most EMO algorithms have been designed for and evaluated by artificial test problems such as DTLZ and WFG, they do not always work well on real-world problems. Main difficulties of real-world problems are as follows: (i) their Pareto front shapes are irregular, (ii) some objectives have much larger values than others, (iii) some decision variables have much larger values than others, (iv) some objectives are highly correlated, and (v) solution evaluation is expensive. In this paper, we focus on the first four difficulties (except for expensive solution evaluation). Our goal is to clearly explain problematic search behaviors of NSGA-II, MOEA/D, SMS-EMOA and NSGA-III caused by these difficulties using simple distance minimization problems. We formulate various distance minimization problems with the above-mentioned four difficulties by changing the location of each target point on the map as well as the size and shape of the map. We also suggest simple remedies for performance improvement of each algorithm for real-world applications without changing the algorithm itself. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： optimization algorithms

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An Improved Sand Cat Swarm Algorithm Optimizing Support Vector Machines 9

An Improved Sand Cat Swarm Algorithm Optimizing Support Vect...

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9th International Conference on Intelligent Computing and Signal Processing, ICSP 2024

作者： Wu, Jiajun Wang, Hongxian Xidian University National Key Laboratory of Radar Signal Processing Xi'an China

ISBN: (纸本)9798350376548

Support vector machines (SVM) are commonly used to solve classification and regression problems, however a suitable kernel function needs to be selected to achieve an effective solution. To solve this problem, we propose an improved sand cat swarm optimization algorithm (SSCSO) to optimize the hyperparameters of SVM. First, Logistic- Icmic mapping is used to generate the initial population to improve the convergence speed. Then, the spiral search strategy is used to balance the global and local search capabilities. Finally, the alert mechanism in the Sparrow search algorithm is integrated to further improve the convergence speed. Simulation experiments of 12 benchmark test functions show that SSCSO has faster convergence speed and more accurate search capabilities than SCSO, SCSO1, SCSO2, SCSO3, and Sparrow Search Algorithm (SSA). In the experiment of modulated signal classification, the internal characteristics of the signal were characterized by extracting high-order cumulants and instantaneous characteristic parameters, and a multi-dimensional feature matrix was constructed for data analysis. The classification accuracy of the SSCSO-SVM algorithm is 4. 3%, 1. 6% and 2. 3% higher than that of the SVM, SCSO-SVM and SSA-SVM algorithms respectively, indicating that the SSCSO-SVM algorithm shows better performance in optimizing the SVM parameters. © 2024 IEEE.

关键词： optimization algorithms

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