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An enhanced Kepler optimization algorithm with global attraction model and dynamic neighborhood search for global optimization and engineering problems

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Mathematics and Computers in Simulation 2025年 237卷 107-144页

作者： Liang, Ziyuan Wang, Zhenlei Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China

The Kepler optimization algorithm (KOA) is a recently proposed physics-based algorithm inspired by Kepler's laws. Despite the strong competitiveness of KOA relative to established algorithms, it faces challenges such as limited search capability, premature convergence, and low convergence accuracy in solving complex optimization problems. To address these shortcomings, we propose an enhanced KOA (EKOA) that integrates a global attraction model, a dynamic neighborhood search operator, and a local update strategy with multi-elite guided differential mutation. Firstly, EKOA introduces an innovative global attraction model to facilitate information exchange among individuals, aiming to extend the search space and improve search efficiency. Secondly, a dynamic neighborhood search operator is designed to weaken the influence of the best individual on the current position updates, thereby mitigating premature convergence. Finally, a local update strategy with multi-elite guided differential mutation is developed to provide new evolutionary opportunities for individuals, ensure evolution in a more favorable direction, and prevent stagnation of the optimal solution during the optimization process. The performance of EKOA is evaluated by comparing it with 12 state-of-the-art algorithms using the CEC2017, CEC2020, and CEC2022 benchmark test suites. Experimental results and statistical analysis substantiate the superiority of EKOA. Additionally, the practical applicability of EKOA is demonstrated through four real-world engineering problems. In conclusion, EKOA not only effectively enhances the performance of the original KOA but also emerges as a powerful and promising algorithm for solving complex engineering problems. © 2025 International Association for Mathematics and Computers in Simulation (IMACS)

关键词： optimization algorithms

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Secure Clustering and Routing Based on Modified Whale optimization Algorithm in MANET

Secure Clustering and Routing Based on Modified Whale Optimi...

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

作者： Ravi Kiran Varma, P. Kumar, Bura Vijay Hemalatha, K.L. Jabar, Muhammed Basim Manjula, B.M. Department of CSE Sagi Rama Krishnam Raju Engineering College Andhra Pradesh Bhimavaram India School of Computer Science and Artificial Intelligence SR University Warangal India Department of Information Science and Engineering Sri Krishna Institute of Technology Bangalore India National University of Science and Technology Dhi Qar Iraq Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (纸本)9789819778751

Mobile Ad Hoc Networks (MANETs) are a self-organizing and adaptive technique comprising several mobile nodes. These networks’ dynamic and changeable architecture creates substantial issues in terms of trust and security for wireless communication lines. To solve these concerns, the Modified Whale optimization Algorithm (MWOA) is used to provide a secure clustering and routing solution in MANET. The Modified K-means (MKM) method is used in the Cluster Head (CH) selection process to provide effective transmission to the target node without packet loss. MWOA, an optimized technique, is introduced to improve MANET routing security. Key parameters such as energy consumption (EC), packet delivery ratio (PDR), throughput, network Lifetime (NL), and end-to-end delay (EED) are used to evaluate the suggested methodology. Comparative investigation shows that the MWOA methodology outperforms present methods in terms of performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： optimization algorithms

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Research on the Application of Information Sharing Technology in Distributed Photovoltaic Aggregation optimization Control Strategy 5

Research on the Application of Information Sharing Technolog...

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5th IEEE International Conference on Power, Electronics and Computer Applications, ICPECA 2025

作者： Ling, Daiwei Huang, Kailang Wang, Kai Kong, Detian Zhou, Ran Wanning Power Supply Bureau of Hainan Power Grid Co. Ltd Wanning China

ISBN: (纸本)9798331533694

This paper studies the application of information sharing technology in distributed photovoltaic aggregation optimization control, and proposes an optimization control strategy based on distributed gradient descent algorithm. This strategy realizes collaborative control between each substation through information sharing technology to reduce power fluctuations and improve the overall efficiency of photovoltaic power generation. This paper constructs a mathematical model of distributed photovoltaic system and designs a corresponding distributed gradient descent algorithm, which can realize global optimization control under the premise of exchanging only a small amount of information between each substation. The effectiveness of the optimization control strategy is verified by modeling and testing the photovoltaic system data of a certain actual substation. The simulation results show that after applying the distributed gradient descent algorithm, the information sharing between each substation improves the overall power generation efficiency of the system and reduces power fluctuations. Specific data show that without increasing additional hardware costs, the power generation efficiency of the system is increased by about 8% and the power fluctuation is reduced by about 12%. These results fully demonstrate that the proposed distributed photovoltaic optimization control strategy based on information sharing has significant advantages in practical applications. © 2025 IEEE.

关键词： optimization algorithms

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An Adaptive Golden Jackal optimization Algorithm for Mobile Robotic Path Planning

An Adaptive Golden Jackal Optimization Algorithm for Mobile ...

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2024 International Conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Chen, Siwen Zheng, Xiujuan Shanghai United Imaging Healthcare Co. Ltd. Shanghai China Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan University of Science and Technology Wuhan China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Given the shortcomings of the traditional Golden Jackal optimization (GJO) algorithm, including limited accuracy and slow convergence speed in solving mobile robot path planning problems, an improved adaptive Golden Jackal optimization (IAGJO) method is proposed. Firstly, a nonlinear adaptive energy strategy is presented to adjust the energy decay pattern, balancing global and local search. Secondly, an enhanced position update mechanism based on Cauchy and Gaussian mutation increases population diversity and guide the search based on optimal individuals, thereby facilitating efficient exploration of unknown regions and avoiding local optima. Finally, the IAGJO algorithm is applied to mobile robot path planning (MRPP), demonstrating that the IAGJO achieves shorter path lengths and higher search efficiency, exhibiting significant advantages over existing methods. © 2025 SPIE.

关键词： 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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The Three-Point optimization Algorithm: A Novel Physics-Based Metaheuristic Approach 1st

The Three-Point Optimization Algorithm: A Novel Physics-Base...

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1st International Symposium on Software Fault Prevention, Verification, and Validation, SFPVV 2024

作者： Deng, Xiong Liu, Shaoying Liu, Yanli Hiroshima University Hiroshima Higashi-Hiroshima City739-8511 Japan Anyang Normal University Henan Anyang City455000 China

ISBN: (纸本)9789819616206

In this paper, a novel metaheuristic approach called The Three-point optimization Algorithm (TPOA) is proposed. TPOA is inspired by the principle of three point positioning. Three point positioning is a geometric based positioning method that determines the position of a target by measuring the distance between the target point and a known point. This method has a wide range of applications in multiple fields, including geological surveying, satellite navigation systems, and WeChat positioning. TPOAs performance is demonstrated by benchmarking with 10 well-known test functions (including unimodal, multimodal, fixed-dimension multimodal, and composite functions). Moreover, TPOAs results are verified by comparison to 5 other metaheuristics. In this study, the experimental results for the above 6 methods on the 10 testing functions in terms of the Mean (mean value), Std (standard deviation), the Best(the best value), the Worst(the worst value), and Burden(cost time), and it suggest that TPOAs results are competitive and, in many instances, outperform the aforementioned well-known metaheuristics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： optimization algorithms

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Logistics Distribution Path Intelligent optimization System Based on Floyd optimization Algorithm 14th

Logistics Distribution Path Intelligent Optimization System ...

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14th International Conference on Frontier Computing, FC 2024

作者： Shi, Danni Jilin Agricultural Science and Technology University Jilin China

ISBN: (纸本)9789819623907

The article analyzed the construction of an intelligent optimization system for rural logistics distribution paths. The overall design concept and technical framework of the intelligent optimization system for rural logistics distribution paths were proposed, and the specific functions and implementation methods of the intelligent optimization system for rural logistics distribution paths were introduced. Finally, through experiments on specific examples, the average delivery cost of A Express Company was reduced to 59000 yuan, and the delivery time of B Express Company was shortened to 14.8 h. This verified that the system has significant advantages in reducing rural logistics delivery costs and improving delivery efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

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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