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Historical Information-assisted Dynamic Response Integration and Adaptive Niche Methods for Dynamic Multimodal optimization

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IEEE Transactions on Evolutionary Computation 2025年

作者： Yu, Kunjie Zhang, Xuyang Zhang, Dezheng Liang, Jing Li, Yumeng Wang, Heshan Chen, Ke Yue, Caitong the School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China State Key Laboratory of Intelligent Agricultural Power Equipment Luoyang471039 China the School of Electrical Engineering and Automation Henan Institute of Technology Xinxiang453003 China School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China the State Key Laboratory of CNS/ATM Beijing100191 China

—Dynamic multimodal optimization problems (DMMOPs) represent the multimodal optimization problems that the optimal solution changes over time. Due to the wide application of DMMOPs in reality, some related algorithms have been proposed in recent years. Most existing algorithms employ a single dynamic response mechanism and embed it in existing multimodal evolutionary algorithms. However, these algorithms often perform limited when environmental change involves multiple types, and they fail to consider utilizing historical information to assist static multimodal optimizers. To solve these issues, this paper proposes historical information-assisted dynamic response integration and adaptive niche methods (HIA-DRI-AN) for dynamic multimodal optimization. In HIA-DRI-AN, an dynamic response integration method with adaptive adjustment mechanism is proposed for generating the initial population when the change happens. This method considers the change types of DMMOPs, and integrates targeted dynamic response mechanisms to respond to the different change types. Also, this method can adaptively self-adjust to balance the convergence and diversity of the initial population depending on the integrated response mechanism’s performance in historical environments. Furthermore, a niching adaptive division strategy is proposed to enhance the performance of the static optimizer. The strategy dynamically divides niches based on the integrated response mechanism’s performance and the current evolutionary stage, which can adjust the preference for diversity and convergence during evolution. The comprehensive experimental results on 24 test functions show that HIA-DRI-AN is superior compared to some state-of-the-art dynamic multimodal algorithms. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： optimization algorithms

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An Indicator-Based Evolutionary Algorithm for Large-Scale Constrained Multi-objective optimization

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IEEE Transactions on Evolutionary Computation 2025年

作者： Zhong, Xiaoyu Yao, Xiangjuan Qiao, Kangjia Gong, Dunwei Jin, Yaochu China University of Mining and Technology School of Mathematics Xuzhou221116 China Zhengzhou University School of Electrical and Information Engineering Zhengzhou450001 China Qingdao University of Science and Technology College of Automation and Electronic Engineering Qingdao266061 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Westlake University School of Engineering Hangzhou310030 China

Most existing constrained multi-objective evolutionary algorithms (CMOEAs) experience a dramatic performance degradation when solving large-scale constrained multi-objective optimization problems (LSCMOPs), since they converge very slowly and easily get trapped in local optima due to the loss of diversity. To enhance the efficiency of tackling LSCMOPs, this paper proposes an indicator-based evolutionary algorithm, referred to as ILCMO. In ILCMO, two complementary indicators are proposed to assess the contribution of each individual to feasibility, convergence, and diversity. The first is a feasibility-oriented indicator designed to drive the population towards the feasible regions. The second is an infeasibility-assisted dynamic indicator, which comprises two relaxed constraint boundaries. Theoretical studies demonstrate that this dynamic indicator can effectively guide the population to focus on evenly searching the infeasible regions around feasible solutions to enhance local diversity. In addition, a variable grouping-based differential evolution (VGDE) strategy, which includes a group-based intra-learning operator and a group-based inter-learning operator, is devised to improve the quality of reproduction in large-scale search spaces. The effectiveness of the proposed algorithm is validated through comprehensive experiments on four benchmarks and a microgrid dispatch problem against seven state-of-the-art algorithms. © 1997-2012 IEEE.

关键词： optimization algorithms

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Two novel combined systems for predicting the peak shear strength using RBFNN and meta-heuristic computing paradigms

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ENGINEERING WITH COMPUTERS 2022年第1期38卷 129-140页

作者： Gao, Juncheng Nait Amar, Menad Motahari, Mohammad Reza Hasanipanah, Mahdi Jahed Armaghani, Danial Dalian Univ Technol State Key Lab Coastal & Offshore Engn Dalian 116000 Peoples R China China Vanke Co Ltd Shenzhen 518000 Peoples R China Sonatrach Dept Etud Thermodynam Div Labs Boumerdes Algeria Arak Univ Dept Civil Engn Fac Engn Arak Iran Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Univ Malaya Dept Civil Engn Fac Engn Kuala Lumpur 50603 Malaysia

Effective prediction of the peak shear strength (PSS) is of crucial importance in evaluating the stability of a rock slope with interlayered rocks and has both theoretical and practical significance. This paper offers two novel prediction tools for the PSS prediction based on radial basis function neural network (RBFNN) and meta-heuristic computing paradigms. For this work, the gray wolf optimization (GWO) and ant colony optimization (ACO) algorithms were used to select the optimal parameters of RBFNN. Then, these two new models were compared with the gene expression programming (GEP) model. A total of 158 experimental data were used to train and test the proposed models using three input parameters, i.e., normal stress, compressive strength ratio of joint walls, and joint roughness coefficient. Finally, the computational result revealed that the RBFNN-GWO model, with the coefficient of determination (R-2) of 0.997, produced a better convergence speed and higher accuracy compared with RBFNN-ACO and GEP models, with the R-2 of 0.995 and 0.996, respectively. The RBFNN-GWO model was found an efficient predictive tool that can help rock engineers in the slopes design processes.

关键词： Peak shear strength RBFNN optimization algorithms Machine learning

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Fast Desynchronization algorithms for Decentralized Medium Access Control Based on Iterative Linear Equation Solvers

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2022年第11期67卷 6219-6226页

作者： Silvestre, Daniel Hespanha, Joao Silvestre, Carlos NOVA Univ Lisbon Sch Sci & Technol P-2829516 Caparica Portugal Lusdfona Univ Sch Sci & Technol COPELABS P-2829516 Caparica Portugal Univ Lisbon Inst Syst & Robot Inst Super Tecn P-2829516 Caparica Portugal Univ Calif Santa Barbara Dept Elect & Comp Engn Santa Barbara CA 93106 USA Univ Macau Fac Sci & Technol Dept Elect & Comp Engn Macau 999078 Peoples R China

We tackle the problem of having multiple transmitters cooperating to be desynchronized using a distributed algorithm. Although this problem can also be found in surveillance, it has the most impact in achieving a fair access to a wireless shared communication medium at the medium access control layer in the context of wireless sensor networks. In this article, we first theoretically investigate the convergence rate of various optimization algorithms, giving closed-form expressions for the parameters achieving the best worst-case convergence rate. We then show that a recently proposed time-varying parameters Nesterov algorithm applied to this problem has worse performance assuming one can determine the number of sensors in the network. In order to remove such an assumption, the problem is seen as the solution of a linear equation corresponding to the first optimality condition. Both theoretically and in simulation, we show that using the Gauss-Seidel method improves the speed of convergence, although its performance degrades for large network sizes. In simulations, it is shown the behavior for various number of wireless devices, emphasizing how the algorithms actually perform in comparison with their worst-case theoretical rates for different network sizes.

关键词： Convergence optimization Wireless sensor networks Eigenvalues and eigenfunctions Mathematical models Transmitters Closed-form solutions Communication networks distributed control optimization algorithms

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Genetic Algorithm Based on Predation Search Strategy 2

Genetic Algorithm Based on Predation Search Strategy

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2nd International Conference on Mechanics, Electronics, Automation and Automatic Control, MEAAC 2024

作者： Ji, Wanfeng Xie, Jinpeng Naval Aviation University China

ISBN: (纸本)9781643685403

Genetic algorithm has been widely used in route planning problems due to its strong global search ability by simulating material changes in nature, as well as biological activities and evolution processes. Predation search strategy is a spatial search strategy used to solve combinatorial optimization problems. By simulating the predatory behavior of animal populations, it can effectively balance global exploration ability and local development ability. This article proposes a genetic algorithm based on predation strategy, which optimizes the search efficiency of the genetic algorithm by dynamically adjusting the crossover probability and mutation probability. Through example verification, this algorithm has better comprehensive search ability compared to basic genetic algorithms and adaptive genetic algorithms, and has been successfully applied in route planning. Â© 2024 The Authors.

关键词： optimization algorithms

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Accelerating Gradient Slope Computation with K1 Fluu: A Novel Technique for Neural Network Training 4

Accelerating Gradient Slope Computation with K1 Fluu: A Nove...

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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Chaudhary, Naina Gupta, Astha Singh, Gurinder Thakur, Ayush Patil, Pratap Paraji Sharma, Vinita Amity University Tashkent Uzbekistan Amity International Business School Amity University Uttar Pradesh Noida India Amity Institute of Information Technology Amity University Uttar Pradesh Noida India

ISBN: (纸本)9798350387490

This report is all about the invention called the K1 Fluu, a new method in fast learning neural networks. K1 Fluu gets gradient slopes easier and yields hidden weights higher. It applies higher order derivatives, weight change, and a unique smoothing technique to improve the speed of the gradient slope. We demonstrate many difficult math operations that describe the essence of the functioning of K1 Fluu. I hope these equations make K1 Fluu understand how the partial derivatives to the parameters of the shapes of the loss function. We compared K1 Fluu on standard benchmarks and state-of-art neural networks. The testing confirms that K1 Fluu is more effective than other procedures. This makes the neural networks more precise, acquire knowledge within a faster time and also have a short training time. The performer K1 Fluu has the advantages in that it can change and smoothes things in order to make the learning process more stable and efficient. This report provides the field of neural network training with a powerful new tool. It enhances network performance and creates the need for other areas to be researched. © 2024 IEEE.

关键词： Dynamic Weighting Gradient Slope Acceleration Higher-Order Derivatives Neural Network Training optimization algorithms

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An Improved CPO Algorithm Based on Elite Preservation Strategy 10

An Improved CPO Algorithm Based on Elite Preservation Strate...

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10th International Conference on Systems and Informatics, ICSAI 2024

作者： Cui, Siyuan Li, Hao Qiu, Jianjie Xu, Zhenyu Early Warning Academy Wuhan China

ISBN: (纸本)9798331510138

This paper addresses the shortcomings of the novel metaheuristic algorithm, Crested Porcupine Optimizer (CPO), which tends to fall into local optima during optimization, exhibits slow convergence in the later stages of iteration, and faces performance improvement bottlenecks. An improved CPO algorithm based on an elitist preservation strategy is proposed. The core idea is to abandon the original method where each initial solution is generated randomly at initiation, and instead introduce an elitist preservation strategy that retains the best-performing individuals from the current population into the next generation after each iteration. This not only helps maintain population diversity but also prevents the loss of the current optimal solution. The paper verifies the effectiveness and efficiency of the improved algorithm using classic test sets, showing that the enhanced CPO algorithm improves the quality of solutions and convergence speed, further enhancing the algorithm's robustness and performance. © 2024 IEEE.

关键词： optimization algorithms

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Research on Index-based Octree Neighborhood Particle Search Algorithm 2

Research on Index-based Octree Neighborhood Particle Search ...

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2nd International Conference on Signal Processing and Intelligent Computing, SPIC 2024

作者： Su, Mo Liu, Chang Jiang, Hong Shenyang Ligong University School of Information Science and Engineering Shenyang China The First Liaoning Provincial Hospital for Veterans and Disabled Soldiers Shenyang China

ISBN: (纸本)9798350368888

This paper proposes an index-based octree neighborhood particle search algorithm to optimize the efficiency of neighborhood particle search in Smoothed Particle Hydrodynamics (SPH) simulations. Based on the octree search algorithm, the optimization uses an index-based octree to reduce the depth of search recursion. In view of the variable particle number and variable particle support radius in the jet fire scene, the adaptive particle volume algorithm is combined. Experimental results show that the proposed algorithm performs better than the octree search algorithm when the number of particles is large. © 2024 IEEE.

关键词： optimization algorithms

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Sudoku Solver algorithms: A Comparative Analysis 1

Sudoku Solver Algorithms: A Comparative Analysis

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Manyam, Yashaswini Sundaram, Srinidhi Sahay, Apurvan Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798331518943

The exploration of advanced algorithms for solving Sudoku puzzles reveals unique insights into their performance and effectiveness. The study analyzes and compares effectiveness in solving Sudoku puzzles with the classic backtracking method, an innovative approach based on ant colony optimization, and advanced constraint propagation algorithm. The project evaluates the performance of these algorithms about solution quality, computation time, and memory usage concerning the complexities of different puzzles: $9\times 9,\ 16\times 16$, and $25\times 25$ grid dimensions. Each individual of the different grid sizes tests time and space computations for each algorithm. These algorithms are putting up certain types of strengths and weaknesses in practical applications. The results, on average, produce that out of all, the ant colony optimization algorithm, which gives a good overall performance in terms of time and memory. © 2024 IEEE.

关键词： optimization algorithms

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Constrained Multi-objective optimization Based on Dynamic Priority and Cooperative Offspring Generation

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IEEE Transactions on Evolutionary Computation 2025年

作者： He, Zhihui Wang, Feng Li, Bingdong Zhou, Aimin Wuhan University School of Computer Science Hubei 430072 China East China Normal University Shanghai Frontiers Science Center of Molecule Intelligent Syntheses Shanghai Institute of AI for Education School of Computer Science and Technology Shanghai200062 China Ministry of Education Key Laboratory of Advanced Theory and Application in Statistics and Data Science China

As the number and complexity of constraints in constrained multi-objective optimization problems (CMOPs) increase, the performance of existing constrained multi-objective evolutionary algorithms (CMOEAs) declines significantly. A novel idea is to sequentially address each constraint based on priority, effectively reducing the complexity of CMOPs. However, in these algorithms, the constraint-handling priority is determined statically in the initial stage. This may lead to inappropriate determination of constraint-handling priority since accurately estimating the constraint landscape in the initial stage is quite challenging. Moreover, these algorithms tackle constraints separately, neglecting the potential for inter-constraint cooperation and thus compromising their efficiency in constraint handling. Thus, we propose a constrained multi-objective evolutionary algorithm based on dynamic priority and cooperative offspring generation called DPCMOEA. Firstly, the constraint-handling priority is determined dynamically by the estimated inconsistency degree (EID) between the Pareto fronts of the candidate constraints and the current population. Secondly, computational resources are automatically allocated to each constraint according to EID based constraint relationship analysis. Finally, a new offspring generation strategy based on constraint cooperation is designed to enhance the quality of new solutions. Experimental results on six CMOP test suites demonstrate that DPCMOEA outperforms six state-of-the-art algorithms. © 1997-2012 IEEE.

关键词： optimization algorithms

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