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检索条件"主题词=Optimization algorithms"

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Improved Tyrannosaurus optimization Algorithm with Mixed Strategies 24

Improved Tyrannosaurus Optimization Algorithm with Mixed Str...

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3rd International Conference on Artificial Intelligence and Intelligent Information Processing, AIIIP 2024

作者： Zheng, Longbiao Feng, Xiangsheng Wu, Meiyan He, Menghui Nanchang Institute of Technology Jiangxi Nanchang China

ISBN: (纸本)9798400707308

This paper addresses the problems of easy local optimum trapping, low convergence precision and slow convergence speed in the basic Tyrannosaurus (T-Rex) optimization algorithm (TROA) by proposing an improved Tyrannosaurus (T-Rex) optimization algorithm (ITROA). By using the logistic chaotic mapping method, ITROA enhances the population’s quality and variety while improving convergence precision and global optimization performance. In order to optimize the Tyrannosaurus Rex hunting method, it simultaneously incorporates the golden sine algorithm, which successfully reduces the search space, shortens the convergence time, and improves local development capabilities. The algorithm’s search ability is evaluated by simulating experiments on multiple single-modal and multi-modal benchmark test functions. The experimental results show that the improved algorithm outperforms the Particle Swarm optimization (PSO), Grey Wolf optimization (GWO), Whale optimization Algorithm (WOA), and Dung Beetle optimization (DBO), and its performance is significantly improved compared to the original TROA algorithm, verifying the effectiveness of the improved algorithm. © 2024 Copyright held by the owner/author(s).

关键词： optimization algorithms

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A Distributed Technique of optimization Problems Solving Based on Efficient Workload Assignment 27th

A Distributed Technique of Optimization Problems Solving Ba...

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27th International Conference on Distributed Computer and Communication Networks, DCCN 2024

作者： Klimenko, Anna Institute of IT and Security Technologies Russian State University for Humanities Moscow Russia

ISBN: (纸本)9783031808524

Currently, the efficiency improvement of complex technical systems is an urgent scientific task, which in numerous cases is formalized via optimization problem and solved by some well-known optimization methods. Metaheuristics are used intensively in this field due to their possibility to generate acceptable solutions in restricted time periods. However, it is known that with the decrease of performing time the solution accuracy of metaheuristics degrades, as well as algorithm’s convergence depends on its exploitation/exploration features and preliminary set parameters. Parallel metaheuristics implementations are used to improve algorithms performance. In this paper a distributed technique of computationally-hard optimization problems solving based on efficient workload assignment is presented and described. The novelty of the technique proposed is that the additional procedure of metaheuristics instances forming and distribution is added, which creates metaheuristic instances of a various computational complexity and assigns them to the most suited computing resources to perform parallel independent runs within the operation time restriction. The workload generated by metaheuristics blocks is distributed efficiently by means of metaheuristics portfolio usage. The latter makes it possible to improve the metaheuristics instances distribution and computational complexities forming so as to put the largest metaheuristic block to the node with the highest performance with the iterative improvement of the distribution, considering the full set of computing resources constraints and criteria. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： optimization algorithms

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Comparative Analysis of Indicators for Multi-objective Diversity optimization 13th

Comparative Analysis of Indicators for Multi-objective Div...

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

作者： Pereverdieva, Ksenia Deutz, André Ezendam, Tessa Bäck, Thomas Hofmeyer, Hèrm Emmerich, Michael Leiden Institute of Advanced Computer Science Leiden University Leiden Netherlands Eindhoven University of Technology Eindhoven Netherlands Faculty of Information Technology University of Jyvaskyla Jyvaskyla Finland

ISBN: (纸本)9789819635375

Indicator-based (multi-objective) diversity optimization aims at finding a set of near (Pareto)optimal solutions that maximizes a diversity indicator, where diversity is typically interpreted as the number of essentially different solutions. Whereas, in the first diversity-oriented evolutionary multi-objective optimization algorithm, the NOAH algorithm by Ulrich and Thiele, the Solow Polasky Diversity (SP Diversity, also known as Magnitude [1]) served as a metric, other diversity indicators could be considered. We examine the parameter-free Max-Min Diversity and the Riesz s-Energy, which features uniformly distributed solution sets. Focusing on multi-objective diversity optimization, we discuss different diversity indicators from the perspective of indicator-based evolutionary algorithms with multiple objectives. We examine theoretical, computational, and practical properties of these indicators, such as monotonicity in species, twinning, monotonicity in distance, strict monotonicity in distance, uniformity of maximizing point sets, computational effort for a set of size n, single-point contributions, subset selection, and submodularity. We present new theorems—including a proof of the NP-hardness of the Riesz s-Energy Subset Selection Problem—and consolidate existing results from the literature. In the experiments, we apply these indicators in the NOAH algorithm to analyze search dynamics via an example. We study how optimizing one indicator impacts others and propose NOAH-specific modifications for the Max-Min indicator. © The Author(s) 2025.

关键词： optimization algorithms

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Feature-Based Evolutionary Diversity optimization of Discriminating Instances for Chance-Constrained optimization Problems 25th

Feature-Based Evolutionary Diversity Optimization of Discri...

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25th European Conference on Evolutionary Computation in Combinatorial optimization, EvoCOP 2025 Held as Part of EvoStar 2025

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

ISBN: (纸本)9783031868481

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. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： optimization algorithms

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Research on the Application System of Multi-Objective optimization Algorithm in the Allocation of Educational Resources in Colleges and Universities

Research on the Application System of Multi-Objective Optimi...

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2025 Asia-Europe Conference on Cybersecurity, Internet of Things and Soft Computing, CITSC 2025

作者： Qian, Feng Zhang, Lei School of Mathematics and Computer Science Tongling University Tongling China

ISBN: (纸本)9798331504205

This article examines the application system of multi-objective optimization algorithm in resource allocation, to enhance the fairness and efficiency of resource allocation. Resource allocation encompasses multiple objectives, such as maximizing resource utilization, minimizing costs and guaranteeing fairness, thus it is extremely crucial to construct a rational optimization model. This paper first designs a resource allocation algorithm based on multi-objective optimization, combining the weight allocation method and the Pareto optimal solution to adapt to the diversified needs of resources. Then a simulation platform is constructed to simulate the actual resource allocation scenario through the operation of the algorithm, and the allocation results are accurately analyzed. The experimental outcomes demonstrate that the algorithm can effectively enhance the efficiency and fairness of resource allocation within different resource limitations. In comparison with the traditional allocation approach, the resource utilization ratio is raised by over 15%, the cost is decreased by approximately 10%, and the fairness index is notably optimized. This system not only provides a reference for the reasonable allocation of resources, but also lays the foundation for the dynamic optimization of resource allocation. © 2025 IEEE.

关键词： optimization algorithms

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Vertical Protection Level optimization Method and Performance Analysis of ARAIM Based on PSA Algorithm 24

Vertical Protection Level Optimization Method and Performanc...

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4th Asia-Pacific Artificial Intelligence and Big Data Forum, AIBDF 2024

作者： Li, Mengjiao Han, Dan Tianjin Key Laboratory for Advanced Signal Processing Civil Aviation University of China Tianjin China Tianjin Air Traffic Control Sub-Branch North China Regional Air Traffic Management Bureau CAAC Tianjin China

ISBN: (纸本)9798400710865

Advanced Receiver Autonomous Integrity Monitoring (ARAIM) algorithm is one of the satellite navigation integrity enhancement technologies to assist civil aviation approach. The basic ARAIM algorithm distributes the integrity risk (IR) and continuity risk (CR) equally, leading to a conservative vertical protection level (VPL) and limiting the availability of ARAM. In this paper, a dynamic risk optimization allocation method PID-based search algorithm (PSA) is proposed. Using the minimum VPL as the optimization objective, iteratively search for the best risk allocation method assigned to each subset of faults while satisfying the constraints. And different optimization variables and objective functions are constructed to analyze the extent to which optimizing the allocation of both IR and CR risks versus optimizing the allocation of a single risk affects the level of protection. Experiments are conducted with static data from multi-GNSS experiment (MGEX) stations and vehicle-mounted dynamic data, and the experimental results show that under GPS and BDS dual-constellation observation, the ARAIM risk optimization allocation method based on the PSA algorithm can effectively reduce the VPL. The smallest VPL is obtained by optimizing the allocation of two risks at the same time, with an average optimization rate of up to 16.1%. And optimizing IR plays a more critical role in the overall protection level optimization, the magnitude of optimizing IR accounts for 68% of the complete optimization. © 2024 Copyright held by the owner/author(s).

关键词： optimization algorithms

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Knowledge Gradient for Multi-objective Bayesian optimization with Decoupled Evaluations 13th

Knowledge Gradient for Multi-objective Bayesian Optimizatio...

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

作者： Buckingham, Jack M. Rojas-Gonzalez, Sebastian Branke, Juergen University of Warwick Coventry United Kingdom Surrogate Modeling Lab UGent-imec Ghent Belgium

ISBN: (纸本)9789819635375

Multi-objective Bayesian optimization aims to find the Pareto front of trade-offs between a set of expensive objectives while collecting as few samples as possible. In some cases, it is possible to evaluate the objectives separately, and a different latency or evaluation cost can be associated with each objective. This decoupling of the objectives presents an opportunity to learn the Pareto front faster by avoiding unnecessary, expensive evaluations. We propose a scalarization based knowledge gradient acquisition function which accounts for the different evaluation costs of the objectives. We prove asymptotic consistency of the estimator of the optimum for an arbitrary, D-dimensional, real, compact search space and show empirically that the algorithm performs comparably with the state of the art and significantly outperforms versions which always evaluate both objectives. © The Author(s) 2025.

关键词： optimization algorithms

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A Backtracking based Combinatorial optimization Algorithm for Target Matching in Account Reconciliation

A Backtracking based Combinatorial Optimization Algorithm fo...

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2024 International Conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Peng, Lei Vincent Mary School of Science and Technology Assumption University Bangkok10240 Thailand Library and Information Science Center Chongqing Three Gorges Medical College Chongqing404120 China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

This paper addresses the issue of matching two columns of data during account reconciliation by designing an appropriate combinatorial optimization algorithm. In the core computing region, we employ a backtracking search strategy to reduce both space and time complexity. The algorithm evaluates all possible combinations to identify the optimal match. Experiments demonstrate that the program exhibits robust anti-interference performance, making it suitable for real-world applications and thereby reducing the workload of reconciliation personnel. © 2025 SPIE.

关键词： optimization algorithms

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Improved Whale optimization Algorithm Based Energy Efficient Secure Routing in Mobile Wireless Sensor Networks

Improved Whale Optimization Algorithm Based Energy Efficient...

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International Conference on 6G Communications Networking and Signal Processing, SGCNSP 2023

作者： Naga Raju, M. Sunil, G. Almoussawi, Zainab Abed Ravi Kiran Varma, P. Chakraborty, Subhra Department of CSE GST GITAM Bengaluru Karnataka Nagadenahalli561203 India School of Computer Science and Artificial Intelligence SR University Warangal India College of Islamic Science Ahl Al Bayt University Karbala Iraq Department of CSE Sagi Rama Krishnam Raju Engineering College Andhra Pradesh Bhimavaram India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (纸本)9789819778751

Mobile Wireless Sensor Networks (MWSN) have gathered significant attention due to their diverse applications across military, industrial infrastructure, automation, health, traffic, and various consumer domains. The primary challenges in MWSN encompass energy consumption, connectivity, scalability, and security. Introducing mobility further amplifies the complexity, necessitating the identification of efficient routing protocols to enhance the overall performance of MWSNs. In this research, Improved Whale optimization Algorithm (IWOA) is proposed for obtaining the energy efficient secure routing in MWSN. The consumption of energy in WSN is achieved by the optimal routing method by utilizing the optimization algorithm. The optimization-based routing approach is developed for designing the optimal transmission path by Cluster Head to destination. The performance of proposed method achieves better results by utilizing the performance metrices of First Node Dead (FND) of 2070, Half Node Dead (HND) of 14,568 and Last Node Dead (LND) of 19,894 when compared to the existing methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： optimization algorithms

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Seismic-inspired simplified earthquake optimization 4th

Seismic-inspired simplified earthquake optimization

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4th International Conference on Computational Methods in Science and Technology, ICCMST 2024

作者： Panchal, Aneesh Kumar, Vivek Department of Applied Mathematics Delhi Technological University Delhi India

ISBN: (纸本)9781032911571

This paper introduces a seismic-inspired simplified earthquake optimization algorithm inspired by seismic wave propagation principles for solving optimization problems. Proposed algorithm combines exploration with local search and gradient descent to efficiently travel throughout the search spaces. The proposed algorithm in this paper is applied to the rastrigin and rosenbrock functions. Through iterative updation of agent positions and adaptive step size adjustments, proposed algorithm demonstrates its capability in finding optimal solutions within search spaces. Comparison study with other nature-inspired algorithms along with statistical analysis of Initial step size parameter is also done in this paper. Future research directions may focus on fine-tuning algorithm parameters, and exploring its applicability across diverse problem domains. Proposed algorithm shows promise as an optimization tool and require further investigation to enhance its performance for practical applications. © 2025 the Author(s).

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