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Period regulated particle swarm optimization algorithm

Soft Computing 2025年第2期29卷 839-860页

作者： Liu, Zhilong Jiang, Huhai Southwest Institute of Technical Physics Renmin South Road Sichuan Chengdu610041 China

Population cycles refer to the regular fluctuations in population size over time. Inspired by this phenomenon, we introduce a period-regulating factor into the particle swarm optimization (PSO) algorithm and propose a modified period-regulated particle swarm optimization (PPSO) algorithm. This algorithm allows for dynamic parameter adjustments in each particle, maintaining a balance between exploitation and exploration. To enhance the diversity of the swarm, we introduce a selection mechanism that enables particles to choose between the global optimum and a new learning object called the mean optimum. Additionally, we incorporate a multi-particle mutation mechanism to improve the particles’ ability to escape local optima. A set of benchmark functions and classical engineering problems are used to verify the superiority of the PPSO algorithm. The results show that the PPSO can provide a very competitive performance compared to some popular PSO variants and meta-heuristic algorithms. Furthermore, this algorithm retains the advantages of simplicity of implementation inherent in PSO. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： optimization algorithms

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Learned Approximate Computing: Algorithm Hardware Co-optimization

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2025年第3期44卷 858-866页

作者： Glukhov, E. Li, T. Gupta, V. Gupta, P. University of California at Los Angeles Department of Electrical and Computer Engineering Los AngelesCA90095 United States

Approximate hardware trades acceptable error for improved performance and previous literature focuses on optimizing this tradeoff in the hardware. We show in this article that the application and the hardware can be co-optimized to achieve the best-quality-performance tradeoff. We propose LAC: learned approximate computing to optimize the algorithm and approximate hardware at the same time to maximize quality of output. Our approach allows automatic selection of approximate computing hardware while achieving similar quality as dedicated training for a single hardware configuration. Our improved training algorithm allows simultaneous hardware selection and application optimization without additional runtime overhead. Multihardware setup chooses a separate approximate hardware for each part of an application which allows for more hardware configurations and further improves quality. © 1982-2012 IEEE.

关键词： optimization algorithms

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Field Data Retrieval for Electric Vehicles and Estimating Equivalent Circuit Model Parameters via Particle Swarm optimization

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TECHNOLOGIES 2025年第3期13卷 91-91页

作者： Sardar, Syed Adil Iqbal, Shahzad Park, Jeongju Han, Sekyung Kim, Woo Young Jeju Natl Univ Fac Appl Energy Syst Dept Elect Engn Jeju 63243 South Korea Kyungpook Natl Univ Dept Elect & Elect Engn Daegu 41566 South Korea

Data retrieval techniques are crucial for developing an effective battery management system for an electric vehicle to accurately assess the battery's health and performance by monitoring operating conditions such as voltage, current, time, temperature, and state of charge. This paper proposes an efficient approach to retrieve real-world field data (voltage, current, and time) under running vehicle conditions. In the first step, noise is removed from the field data using a moving-average filter. Then, first- and second-order derivations are applied to the filtered data to determine specific data set conditions. After that, a new approach based on zero-crossing is implemented to retrieve the field data. A second-order Randle circuit (SORC) is utilized in this study to analyze the selected field data. Further, a particle swarm optimization algorithm is adapted to estimate the parameters of the SORC. Our experiments indicate that the relative errors of the equivalent circuit model (ECM) are less than 2% compared to the model voltage and real voltage, which is consistent with the stable parameters of ECM.

关键词： electric vehicles data retrieval techniques equivalent circuit model battery modeling optimization algorithms

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Measures of stochastic non-dominance in portfolio optimization

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European Journal of Operational Research 2025年第1期321卷 269-283页

作者： Junová, Jana Kopa, Miloš Department of Probability and Mathematical Statistics Faculty of Mathematics and Physics Charles University Sokolovská 83 Prague186 75 Czech Republic

Stochastic dominance rules are well-characterized and widely used. This work aims to describe and better understand the situations when they do not hold by developing measures of stochastic non-dominance. They quantify the error caused by assuming that one random variable dominates another one when it does not. To calculate them, we search for a hypothetical random variable that satisfies the stochastic dominance relationship and is as close to the original one as possible. The Wasserstein distance between the optimal hypothetical random variable and the original one is considered as the measure of stochastic non-dominance. Depending on the conditions imposed on the probability distribution of the hypothetical random variable, we distinguish between general and specific measures of stochastic non-dominance. We derive their exact values for random variables with uniform, normal, and exponential distributions. We present relations to almost first-order stochastic dominance and to tractable almost stochastic dominance. Using monthly returns of twelve assets captured by the German stock index DAX, we solve portfolio optimization problems with the first-order and second-order stochastic dominance constraints. The measures of stochastic non-dominance allow us to compare the optimal portfolios with respect to different orders of stochastic dominance from a new angle. We also defined the closest dominating and closest approximately dominating portfolios. They brought a better understanding of the relationship between the two types of optimal portfolios. Using moving window analysis, the relationship of the in-sample measure of stochastic non-dominance to out-of-sample performance was studied, too. © 2024 Elsevier B.V.

关键词： optimization algorithms

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Leveraging ChatGPT for enhanced stock selection and portfolio optimization

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Neural Computing and Applications 2025年第8期37卷 6163-6179页

作者： Huang, Zhendai Liao, Bolin Hua, Cheng Cao, Xinwei Li, Shuai College of Computer Science and Engineering Jishou University Jishou416000 China School of Business Jiangnan University Wuxi214122 China Faculty of Information Technology and Electrical Engineering University of Oulu Oulu90570 Finland VTT-Technology Research Center of Finland Kaitovayla 1 Oulu90570 Finland

The public release of ChatGPT represents a significant milestone in generative AI technology, enabling the autonomous generation of content based on pre-training. This breakthrough presents new opportunities for advancements in the field of portfolio selection. This paper aims to introduce a comprehensive portfolio selection method by applying ChatGPT for stock selection and combining it with optimization algorithms to jointly optimize portfolio selection. Compared to randomly selected stocks, the portfolios optimized using ChatGPT-selected stocks and solved with the egret swarm optimization algorithm (ESOA) demonstrate higher diversification and lower volatility, leading to superior portfolio optimization results. Additionally, to validate ESOA’s superiority, its performance is compared against genetic algorithm (GA) and particle swarm optimization (PSO) on five metrics: risk, expected return, Sharpe ratio, objective value, and penalty term. Under equivalent experimental setting, ESOA exhibits a better ability to balance the relationship between risk and return. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： optimization algorithms

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New buzzard optimization algorithm for multi-level thresholding and comparison with multi-agent based fuzzy particle swarm optimization algorithm

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Multimedia Tools and Applications 2025年 1-18页

作者： Arshaghi, Ali Department of Electrical Engineering Central Tehran Branch Islamic Azad University Tehran Iran

In this article, a new optimal multi-level thresholding algorithm for histogram-based image segmentation present. The proposed algorithm is an improved type of particle optimization algorithm. In this paper, the selection of image Thresholding done using a new method called Buzzard optimization Algorithm (BUZO). Each particle (Buzzard) has two vectors in the search space, one is the ability vector, and the other is the position vector. The new position depends on these two vectors. The proposed BUZO algorithm has also solve these weaknesses of being stuck in local optimal points and early convergence and compensates for the failings of other algorithms. PSNR compares the proposed BUZO algorithm with PSO and MAFPSO algorithms as follows: In the image of the tree for the PSNR test of the BUZO algorithm in 5-level thresholding, it is 29.8, which is more different from the PSNR of the PSO and MAFPSO algorithms, which are 25 and 28, respectively. In addition, in another pirate test image, the PSNR of the BUZO algorithm in 5-level thresholding is 28.3, compared to the PSNR of PSO and MAFPSO algorithms, which are 25 and 26, respectively. In addition, in the pirate test image, the PSNR value of the BUZO algorithm in 2-level thresholding is 22.9, which compared to the PSNR of PSO algorithm 18 and MAFPSO algorithm 22.3. These results show the superiority of this algorithm with other methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： optimization algorithms

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LOGISTICS PATH PLANNING BASED ON IMPROVED PARTICLE SWARM optimization ALGORITHM

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Scalable Computing 2025年第3期26卷 1276-1283页

作者： Tang, Longjiao ChongQing College Of Finance And Economics Chongqing402160 China

In order to solve the problem of vehicle path scheduling management more reasonably, the author proposes a logistics path planning research based on improved particle swarm optimization algorithm. The author introduces a particle swarm optimization algorithm that incorporates a dynamic monkey jumping mechanism. Initially, dynamic population grouping is used to assign varying dynamic inertia weights, enhancing the algorithm’s speed. Subsequently, the monkey jumping mechanism is added to ensure global convergence. This enhanced algorithm was then tested on two logistics distribution path optimization scenarios. In a consistent environment, the improved algorithm outperformed the standard particle swarm optimization algorithm by achieving a better optimal path fitness value, shorter average operation time, and a higher number of successful attempts to find the optimal solution. The experimental results show that out of 10 instances solved using the improved algorithm, 5 times obtained the optimal solution of 67.1km, and the optimal delivery path corresponding to the optimal solution was 0-4-7-6-0;0-2-8-5-3-1-0, with an average calculation time of 1.26s, indicating high computational efficiency. The total delivery distance and average calculation time of the particle swarm algorithm, as well as the number of times to obtain the optimal solution, are 69.01, 2.7, and 3, respectively. It is evident that the enhanced particle swarm optimization algorithm significantly outperforms the conventional particle swarm algorithm. The improvements not only accelerate the optimization process but also enhance the algorithm’s convergence, ensuring high-quality optimization results. Consequently, this improved algorithm holds substantial application value. © 2025 SCPE.

关键词： optimization algorithms

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Enhanced genetic algorithm for indoor low-illumination stereo matching energy function optimization

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Alexandria Engineering Journal 2025年 121卷 1-17页

作者： Hongjin, Zhang Hui, Wei Laboratory of Algorithms for Cognitive Models School of Computer Science Fudan University Handan Road 220 ShangHai China College of Engineering Science and Technology Shanghai Ocean University Huchenghuan Road 999 ShangHai China

This study presents an enhanced genetic algorithm to generate high-quality, dense disparity maps in low-illumination indoor stereo matching scenarios. The algorithm utilizes a novel biomimetic mutation strategy, dynamically adjusted through common allelic theory, to effectively reduce the search space and improve optimization efficiency. A multi-objective genetic approach is employed, optimizing both data fidelity and smoothness to achieve balanced and accurate results. The Integration of biological evolution principles, including a hierarchical performance index system, ensures the algorithm balances local and global features during optimization. Experimental results demonstrate that the proposed algorithm consistently converges within 10 iterations, reducing average disparity errors by 33.7% compared to state-of-the-art methods and by 20% compared to a control group. Additionally, the algorithm achieves a lower computational complexity of O(H), offering significant improvements in resource efficiency compared to standard algorithms. These findings validate the algorithm's reliability, advancement, and effectiveness for stereo matching under challenging conditions. © 2025 The Authors

关键词： optimization algorithms

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New comparative approach to multi-level thresholding: chaotically initialized adaptive meta-heuristic optimization methods

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Neural Computing and Applications 2025年第14期37卷 8371-8396页

作者： Serbet, Fatmanur Kaya, Turgay Department of Electrical and Energy Pertek Sakine Genc Vocational School Munzur University Tunceli62000 Turkey Department of Electrical-Electronics Engineering Faculty of Engineering Firat University Elazig23119 Turkey

One method aimed at enhancing the performance of meta-heuristic optimization techniques is the incorporation of chaotic systems. Instead of irregular distributions in the search space, chaotic distributions are employed in the initial population of optimization algorithms to improve the efficiency of the search process. This approach enables search agents distributed in a chaotic manner to effectively explore the search space. The initial populations of both the well-established PSO algorithm and the enhanced WSO algorithm, which incorporates advanced search techniques, are distributed in the search space according to the characteristics of Logistic, Chebyshev, Circle, Sine, and Piecewise chaotic maps in this study. The original PSO and WSO algorithms, as well as the resulting chaotically initialized PSO and chaotically initialized WSO algorithms, were tested using 23 benchmark functions. Subsequently, the Otsu method was integrated into the tested optimization algorithms to obtain multi-level thresholding values. These algorithms were applied to five different test images with a manually determined number of thresholds. The results obtained were presented in the study and evaluated using statistical tests. © The Author(s) 2025.

关键词： optimization algorithms

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WHALE optimization ALGORITHM FOR EFFICIENT TASK ALLOCATION IN THE INTERNET OF THINGS

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Scalable Computing 2025年第3期26卷 1412-1421页

作者： Shu, Wanchang Jiangxi Teachers College Jiangxi Yingtan335000 China

In order to solve the problem of reducing worker costs and improving worker efficiency, the author proposes a title whale optimization algorithm for efficient task allocation in the Internet of Things. This algorithm adopts the fuzzy chance constrained programming method to model the online time of workers, and introduces delay costs and idle costs based on whether there is delay or not, Due to the fact that the corresponding problem is a combinatorial optimization problem and belongs to the NP hard problem category, a two-stage task allocation algorithm is designed to solve it in combination with the whale optimization algorithm. The experimental results show that after being simulated by the algorithm, half of the workers reached the highest efficiency of 1, and the expected online time of the workers was less than 30, and the task execution time of the workers was less than 35. The task allocation algorithm designed by the author has higher worker efficiency compared to other algorithms and has broad application prospects. © 2025 SCPE.

关键词： optimization algorithms

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