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multi-objective particle swarm optimization with Integrated Fireworks Algorithm and Size Double Archiving

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2025年第1期18卷 1-37页

作者： Zhang, Yansong Liu, Yanmin Zhang, Xiaoyan Song, Qian Ouyang, Aijia Yang, Jie Guizhou Univ Sch Math & Stat Guiyang 550025 Peoples R China Zunyi Normal Coll Zunyi 563002 Peoples R China Guizhou Minzu Univ Sch Data Sci & Informat Engn Guiyang 550025 Peoples R China

The multi-objective particle swarm optimization (MOPSO) is an optimization technique that mimics the foraging behavior of birds to solve difficult optimization problems. MOPSO is well known for its strong global search capability, which efficiently locates solutions that are close to the global optimum across a wide search domain. However, similar to many other optimization algorithms, the fast convergence property of MOPSO can occasionally lead to the population entering the local optimum too soon, obstructing researchers from investigating more efficient solutions. To address this challenge, the study proposes a novel framework that integrates the fireworks algorithm (FA) into MOPSO and establishes a size-double archiving mechanism to maintain population diversity. By preventing population homogenization, this mechanism promotes the retention of better solutions. Additionally, by fusing evolutionary data analysis with particle information, the study offers new individual optimal choices and adaptive parameter tuning to improve the algorithm's robustness and adaptability and better manage the complexity of multi-objective optimization problems (MOPs). The suggested algorithm is compared with several existing MOPSOs and multi-objective evolutionary algorithms (MOEAs) in simulation experiments. Standard test problems like ZDT, UF, and DTLZ are used in the experiments. The new algorithm performs exceptionally well in terms of improving convergence and population diversity, as well as demonstrating significant competitiveness for solving MOPs.

关键词： multi-objective particle swarm optimization Fireworks algorithm Double archiving Adaptive parameters

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optimization of microchannel heat sink based on multi-objective particle swarm optimization algorithm for integrated circuit chips cooling

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NUMERICAL HEAT TRANSFER PART B-FUNDAMENTALS 2025年第4期86卷 840-858页

作者： Jiang, Meixia Pan, Zhongliang South China Normal Univ Sch Phys & Telecommun Engn Guangzhou Peoples R China Guangzhou City Polytech Sch Informat Engn Guangzhou Peoples R China

The microchannel heat sink is an effective way to solve the heat dissipation problem in integrated circuit chips. In order to obtain the high efficiency and low resistance microchannel heat sink, the three variables (oh, f, g) were selected as the design variables, and the thermal resistance and pumping power were selected as objective functions. Then, the RSM, multi-objective particle swarm optimization algorithm and K-means clustering method were comprehensively applied to the multi-objective optimization of the microchannel heat sink with turbulence to obtain the five representative solutions (Case A-Case E), and the results were verified by CFD. The research shows that the maximum relative errors between Pareto optimization values and numerical simulation values of thermal resistance and pumping power of the five groups representative solutions are 0.637% and 1.187%, respectively. The Pareto optimization values are in good agreement with the numerical simulation values, and there is an effective tradeoff point between the five representative solutions of the Pareto optimal solution to make both the pumping power and thermal resistance within the optimal range. Compared with before optimization and the research results of Han Wang et al. [Appl. Therm. Eng. 2022;215:118849], the comprehensive heat transfer performance of the optimized microchannel heat sink has been improved, where the comprehensive heat transfer performance of the Case B (Delta h=0.0400 mm, f = 0.0979 mm, g = 0.5000 mm) is the best. The maximum temperature and pressure drop of the optimized microchannel heat sink can be reduced by 3.045% and 7.659%, respectively. Compared with the research results of Han Wang et al. [Appl. Therm. Eng. 2022;215:118849], the temperature distribution more uniform and the pressure drop of the microchannel heat sink after optimization (Case B) decreased by 7.058%. This optimization method can provide a reference for the optimization design of the microchannel heat sin

关键词： Microchannel heat sink multi-objective particle swarm optimization pumping power RSM thermal resistance

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A multi-objective particle swarm optimization Algorithm Based on Decomposition and multi-Selection Strategy

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Computers, Materials & Continua 2025年第1期82卷 997-1026页

作者： Li Ma Cai Dai Xingsi Xue Cheng Peng School of Computer Science Shaanxi Normal UniversityXi’an710119China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhou350118China Information Construction and Management Center and Institute of Artificial Intelligence and Educational New Productivity Ningxia Normal UniversityGuyuan756099China

The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization *** the article,amulti-objective particle swarm optimization algorithmbased on decomposition and multi-selection strategy is proposed to improve the search ***,two update strategies based on decomposition are used to update the evolving population and external archive,***,a multiselection strategy is *** first strategy is for the subspace without a non-dominated *** the neighbor particles,the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle far away fromthe search particle and the global optimal solution is selected as the personal optimal solution to enhance global *** second strategy is for the subspace with a non-dominated *** the neighbor particles,two particles are randomly selected,one as the global optimal solution and the other as the personal optimal solution,to enhance local *** third strategy is for Pareto optimal front(PF)discontinuity,which is identified by the cumulative number of iterations of the subspace without non-dominated *** the subsequent iteration,a new probability distribution is used to select from the remaining subspaces to ***,an adaptive inertia weight update strategy based on the dominated degree is designed to further improve the search ***,the proposed algorithmis compared with fivemulti-objective particle swarm optimization algorithms and five multi-objective evolutionary algorithms on 22 test *** results show that the proposed algorithm has better performance.

关键词： multi-objective optimization multi-objective particle swarm optimization decomposition multi-selection strategy

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Ensemble learning training strategy based on multi-objective particle swarm optimization and chasing method

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 281卷

作者： Li, Xinyue Zhang, Yu Hu, Wang Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 611731 Peoples R China

Ensemble learning (EL) is a widely used approach with remarkable achievements in real-world applications. However, the quality of ensemble models constructed through the common practice, such as multiple data divisions or training processes, cannot be guaranteed due to the low accuracy or high complexity of base models. Achieving a balance between prediction accuracy and model complexity is challenging, as these are two conflicting aspects that affect generalization ability in existing works. Inspired by the ability of multi-objective optimization to generate a set of Pareto solutions involving conflicting objectives, an ensemble learning algorithm is proposed based on multi-objective particle swarm optimization (MOPSO) and a chasing method that combines local search with the MOPSO. This algorithm, termed EL-MOPSO, aims to balance diversity, prediction accuracy, and model complexity simultaneously during the training process. Specifically, MOPSO is introduced to EL to generate various Pareto models, which are on the approximate Pareto front (PF) of the MOPSO in terms of accuracy and complexity, to construct a model pool. A chasing method is designed, where solutions in the MOPSO archive and those from the local search method chase each other to imporve the accuracy of the models generated by EL-MOPSO. Additionally, the adaptive reference vector is introduced to select suitable models from the model pool for the local search model ensemble process. Experimental results on 42 test functions demonstrate the superiority of EL-MOPSO in terms of prediction accuracy compared to state-of-the-art methods, with EL-MOPSO achieving the best accuracy in 28 test cases. Furthermore, the proposed method is applied to a realworld material design problem, further evidencing the competence of the EL-MOPSO algorithm. As a result, the ensemble model trained through EL-MOPSO exhibited an average error of 0.021, compared to MSEs of 0.033, 0.041, 0.023, and 0.025 from SWA-based EL, DREML,

关键词： Ensemble learning Evolutionary algorithm multi-objective particle swarm optimization Local search

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Research on Economic Environment Scheduling optimization of Power System Based on multi-objective particle swarm optimization

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PROCESS INTEGRATION AND optimization FOR SUSTAINABILITY 2025年第1期9卷 275-290页

作者： Ge, Jiajia Yiwu Ind & Commercial Coll Sch Econ & Management Yiwu 322000 Peoples R China China Yiwu Individual Econ Dev Res Ctr Yiwu 322000 Peoples R China

With the progress of time and the development of technology, human society has more and more demands on the power system. The optimization of economic emission dispatching in power systems is an important issue. Therefore, a multi-objective particle swarm optimization was proposed to achieve the optimization of economic emission dispatching in power systems. The test results confirmed that the proposed method found the lowest cost that satisfied the constraint conditions and considered the pollution emissions when the iterations reached 41, thus reaching a convergence state. The particle swarm optimization reached a convergence state after 60 iterations. The artificial bee colony algorithm reached a convergence state after 69 iterations. In sample one, the optimal compromise solution calculated by the multi-objective particle swarm optimization had a cost reduction of 50,235.156 yuan compared to the particle swarm optimization and a savings of 105,138.384 yuan compared to the artificial bee colony method. Its pollution emissions were reduced by 6831.594 lb compared to the particle swarm algorithm and 9624.269 lb compared to the artificial bee colony method. As a result, the proposed multi-objective particle swarm optimization algorithm has obvious advantages in solving the economic environment scheduling optimization of power system. Compared with the traditional particle swarm optimization algorithm and artificial bee colony algorithm, the multi-objective particle swarm optimization algorithm significantly improves the iterative efficiency. Meanwhile, the method achieves a better balance between the dual goals of economic cost and environmental protection. Specifically, the cost reduction and significant reduction of pollution emissions effectively support the sustainable development goals of the power system. This study can provide effective solutions for the economic and environmental scheduling optimization of the power system.

关键词： Power system Economic emission dispatching particle swarm optimization multi-objective particle swarm optimization Equilibrium constraint

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Encoding local label correlations in multi-instance multi-label learning with an improved multi-objective particle swarm optimization

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COMPLEX & INTELLIGENT SYSTEMS 2025年第6期11卷 1-25页

作者： Bao, Xiang Han, Fei Ling, Qinghua Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang 212013 Peoples R China Jiangsu Engn Res Ctr Big Data Ubiquitous Percept & Zhenjiang 212013 Peoples R China Jiangsu Univ Inst Sci & Technol Informat Zhenjiang 212013 Peoples R China Jiangsu Univ Sci & Technol Sch Comp Sci & Engn Zhenjiang 212003 Peoples R China

Label correlations, as important prior information, are essential to enhance the classification performance in multi-Instance multi-Label (MIML) algorithms, but existing models always leverage global label correlations which are less informative. Furthermore, classifier optimization is also crucial for MIML classification results, previous works do not frequently seek to optimize multi objectives simultaneously which may easily result in poor performance on some important metrics. In this paper, a MIML algorithm encoded with local label correlations with an improved multi-objective particle swarm optimization (MIML-MOPSO-LLC) is proposed to address the above problems. Specifically, a framework is proposed by taking consideration into both global discrimination fitting and local label correlation sensitivity in the bag level simultaneously in the standard MIML. Subsequently, the loss function of the framework is solved by an alternating optimization process where Support Vector Machine (SVM) classifiers are constructed for optimization. Ultimately, an improved MOPSO is employed to optimize the SVM classifiers by searching for more reliable Pareto front solutions. The experimental results demonstrate that the proposed method achieves competitive performance compared with the classical and state-of-the-art MIML models from the perspective of several classification indicators. Notably, the proposed method which explores the local label correlations exhibits superiority over methods relying on global ones. Furthermore, the study reveals that proposed methods demonstrates enhanced effectiveness in MIML classification and optimizing SVM classifiers compared to conventional single and multi objective optimization approaches.

关键词： multi-instance multi-label Local label correlations multi-objective particle swarm optimization Pareto front

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Optimizing managed artificial recharge backwash using a multi-objective particle swarm optimization coupled with a clogging simulation model

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COMPUTERS & GEOSCIENCES 2025年 196卷

作者： Zhang, Tianjiao Zhu, Qi Wen, Zhang China Univ Geosci Sch Environm Studies Hubei Key Lab Yangtze Catchment Environm Aquat Sci Wuhan 430074 Hubei Peoples R China

Artificial recharge (AR) plays an important role in the management of groundwater resources and the mitigation of hydrogeological problems. However, challenges related to clogging inevitably arise during groundwater recharge. Although the clogging mechanism during groundwater recharge has been intensively studied in the past decades, there is a relative scarcity of studies focused on strategies for preventing clogging through artificial interventions. This study introduces an optimization framework that integrates a clogging model with two objective functions to obtain an optimized backwashing strategy aimed at minimizing clogging during groundwater recharge. The proposed clogging model for the groundwater recharge process considers both physical clogging (attachment of suspended solids) and chemical clogging (iron oxide clogging) by coupling COMSOL and PHREEQC models. The multi-objective particle swarm optimization (MOPSO) algorithm was used to obtain the Pareto-optimal solutions, by evaluating the clogging conditions and recharge efficiencies of different strategies, which enables stakeholders to determine suitable backwashing frequency and duration among various groundwater backwashing strategies. The results indicate that optimized backwashing strategy can significantly reduce clogging in groundwater recharge projects. With the highest backwashing frequency and duration, clogging near the recharge wells would be reduced to 90% of that observed during normal recharge without strategies, and the time spent on backwashing would only constitute 4.8% of the recharge time.

关键词： Well clogging Artificial groundwater recharge Backwashing multi-objective particle swarm optimization

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Energy-efficient trajectory optimization for planetary surface manipulators via heuristic multi-objective particle swarm optimization algorithm

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ROBOTICA 2025年

作者： Xiao, Guangzhou Ma, Yuting Li, Yunpeng Jiangxi Hongdu Aviat Ind Grp Nanchang Peoples R China Harbin Inst Technol Sch Astronaut Harbin Peoples R China

This paper addresses the issue of energy-efficient trajectory optimization for planetary surface manipulators under kinematic and dynamic constraints. To mitigate the inefficiency of existing algorithms, an adaptive boundary adjustment strategy for the multi-dimensional decision space is proposed, which modifies the time intervals between neighboring configuration nodes, enabling precise adaptation of the decision space boundaries. Additionally, a complementary dual-archive guided boundary exploration strategy is introduced to connect the feasible and infeasible regions, allowing for the effective utilization of information from infeasible solutions near the constraint boundaries. This heuristic approach guides the particle swarm in efficiently exploring areas close to the constraints, significantly enhancing the evolutionary optimization capability of the swarm. Furthermore, a swarm optimal position updating strategy based on sparsity sorting is developed. This guides the particle swarm to concentrate on exploring positions where non-dominated solutions on the Pareto front are more sparsely distributed, ensuring uniformity and completeness in the final Pareto front. Finally, the aforementioned strategies are integrated into a heuristic multi-objective particle swarm optimization (HMOPSO) algorithm for the trajectory optimization of manipulators. Comparative experiments are conducted with HMOPSO and existing advanced algorithms in the field of multi-objective optimization. Experimental results demonstrate that HMOPSO exhibits superior evolutionary optimization capabilities and faster convergence rates. Moreover, performance metrics such as inverse generation distance and dominant area during the iterative process of HMOPSO significantly outperform those of existing optimization algorithms.

关键词： planetary surface manipulators trajectory optimization multi-objective particle swarm optimization

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multi-objective particle swarm optimization algorithm for task allocation and archived guided mutation strategies

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Scientific reports 2025年第1期15卷 15821页

作者： Jianjie Chen Yanmin Liu Yi Luo Aijia Ouyang Jie Yang Wuer Bai School of Data Science and Information Engineering Guizhou Minzu University Guiyang China. School of Mathematics Zunyi Normal College Zunyi China. yanmin7813@***. School of Mathematics and Statistics Guizhou University Guiyang China. School of Mathematics Zunyi Normal College Zunyi China.

In this paper, we propose a novel multi-objective particle swarm optimization algorithm with a task allocation and archive-guided mutation strategy (TAMOPSO), which effectively solves the problem of inefficient search in traditional algorithms by assigning different evolutionary tasks to particles with different characteristics. First, TAMOPSO divides multiple subpopulations according to the particle distribution status of each iteration of the population and designs a new task allocation mechanism to improve the evolutionary search efficiency. Second, TAMOPSO adopts an adaptive Lévy flight strategy according to the population growth rate, automatically increasing the global variation probability to expand the search range when the population converges and enhancing the local variation to conduct fine search when the population disperses to realize the dynamics of global and local variations. Finally, TAMOPSO measures the contribution of particles to the population optimization through the particle evolution contribution rate index and filters out valuable historical solutions for subsequent reuse to accelerate the convergence speed; in addition, TAMOPSO improves the individual optimal particle selection mechanism, changes the bias of the traditional algorithm, ensures that each particle has an equal opportunity, and enhances the fairness of the selection process. The fairness of the selection process is enhanced at the same time. The performance of TAMOPSO is compared with ten existing algorithms on 22 standard test problems, and the experimental results show that TAMOPSO outperforms the other algorithms in several standard test problems and has better performance in solving multi-objective problems.

关键词： Individual optimal selection Lévy flight strategy multi-objective particle swarm optimization Subpopulation partitioning Task allocation

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multi-objective particle swarm optimization on ultra-thin silicon solar cells

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JOURNAL OF OPTICS-INDIA 2020年第4期49卷 446-454页

作者： Atalay, Ipek Anil Gunes, Hasan Alper Alpkilic, Ahmet Mesut Kurt, Hamza TOBB Univ Econ & Technol Dept Elect & Elect Engn TR-06560 Ankara Turkey

Finding optimized parameters for any photonic device is a challenging problem, because as the search space enlarges the computation time and design complexity increase. For higher performance solar cells, various studies have been carried out to procure optimized parameters, to attain better performance and low cost as well. In this study, we used a multi-objective particle swarm optimization approach to search design space effectively and obtain fixed parameters for enhanced solar spectrum absorption. Numerical investigations are conducted for pyramid surface pattern, to find proper solar cell parameters for minimum reflection and maximum light trapping which give rise to enhanced absorption of photons. For the ultra-thin-film silicon solar cell having a thickness of 1 mu m, a designed double-sided pyramid structure provides an ideal short-circuit photocurrent of 34.23 mA/cm(2). In this regard, the proposed approach can be applied to different film thicknesses of semiconductors for different photonic applications by manipulating the reflection/transmission coefficient and light trapping mechanism.

关键词： Solar cells Anti-reflection Absorption enhancement Surface texturing Light trapping multi-objective particle swarm optimization

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