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A multi-objective particle swarm optimization algorithm based on dynamic boundary search for constrained optimization

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APPLIED SOFT COMPUTING 2018年 70卷 680-700页

作者： Zain, Mohamad Zihin bin Mohd Kanesan, Jeevan Chuah, Joon Huang Dhanapal, Saroja Kendall, Graham Univ Malaya Fac Engn Dept Elect Engn Kuala Lumpur 50603 Malaysia Univ Malaya Fac Engn Expert Syst & Optimizat Grp Kuala Lumpur 50603 Malaysia Univ Nottingham Sch Comp Sci Jubilee Campus Nottingham NG8 1BB England

Due to increased search complexity in multi-objective optimization, premature convergence becomes a problem. Complex engineering problems poses high number of variables with many constraints. Hence, more difficult benchmark problems must be utilized to validate new algorithms performance. A well-known optimizer, multi-objective particle swarm Optimizer (MOPSO), has a few weakness that needs to be addressed, specifically its convergence in high dimensional problems and its constraints handling capability. For these reasons, we propose a modified MOPSO (M-MOPSO) to improve upon these aspects. M-MOPSO is compared with four other algorithms namely, MOPSO, multi-objective Grey Wolf Optimizer (MOGWO), multi-objective Evolutionary Algorithm based on Decompositions (MOEA/D) and multi-objective Differential Evolution (MODE). M-MOPSO emerged as the best algorithm in eight out of the ten constrained benchmark problems. It also shows promising results in bioprocess application problems and tumor treatment problems. In overall, M-MOPSO was able to solve multi-objective problems with good convergence and is suitable to be used in real world problem. (C) 2018 Elsevier B.V. All rights reserved.

关键词： multi-objective particle swarm optimization swarm intelligence Constrained multi-objective optimization Fed-batch fermentation Tumor treatment

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An iterative multi-objective particle swarm optimization-based control vector parameterization for state constrained chemical and biochemical engineering problems

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BIOCHEMICAL ENGINEERING JOURNAL 2015年 103卷 138-151页

作者： Zhang, Panpan Chen, Hongmei Liu, Xinggao Zhang, Zeyin Zhejiang Univ Dept Control Sci & Engn Inst Ind Proc Control State Key Lab Ind Control Technol Hangzhou 310027 Zhejiang Peoples R China Weifang Univ Inst Electromech & Vehicle Engn Weifang 262061 Peoples R China Zhejiang Univ Dept Math Hangzhou 310027 Zhejiang Peoples R China

Dynamic optimization of the state constrained chemical and biochemical engineering problems has always been the research hotspot due to the difficulties of handling constraints. In this paper, a novel approach, CVP-IMOPSO, is presented to tackle this kind of problems, where the original optimization problem is firstly converted into a multi-objective dynamic optimization problem based on a method of handling state constraint;and control vector parameterization (CVP) is then applied to transform the resulting infinite dimensional problem into a nonlinear programming (NLP) problem;finally, an efficient iterative multi-objective particle swarm optimization (IMOPSO), embedded with an region reduction strategy, is proposed to tackle this MOO problem. This strategy reduces the search space gradually during the iteration process so as to further promote the convergence rate and diversity of MOPSO. Three well-known classic optimization problems for chemical and biochemical engineering processes have been tested as illustration, and the detailed comparisons among IMOPSO, MOPSO and NSGA-II are carried out for seven benchmark problems. The research results not only show that the proposed IMOPSO is an efficient algorithm for MOO problems, but also reveal the effectiveness of the proposed approach. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Dynamic optimization multi-objective particle swarm optimization Control vector parameterization multi-objective optimization Region reduction strategy State constrained engineering problems

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A multi-objective particle swarm optimization Based on Grid Distance

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INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2020年第3期34卷

作者： Leng, Rui Ouyang, Aijia Liu, Yanmin Yuan, Lian Wu, Zongyue Wuyi Univ Sch Math & Computat Sci Jiangmen 529020 Peoples R China Zunyi Normal Univ Dept Comp & Informat Sci Zunyi 563002 Guizhou Peoples R China Zunyi Normal Univ Sch Math Zunyi 563002 Guizhou Peoples R China Guizhou Minzu Univ Sch Data Sci & Informat Engn Guiyang 550025 Peoples R China Wuyi Univ Sch Intelligent Mfg Jiangmen 529020 Peoples R China

In modern intelligent algorithms and real-industrial applications, there are many fields involving multi-objective particle swarm optimization algorithms, but the conflict between each objective in the optimization process will easily lead to the algorithm falling into local optimal. In order to prevent the algorithm from quickly falling into local optimization and improve the robustness of the algorithm, a multi-objective particle swarm optimization algorithm based on grid distance (GDMOPSO) was proposed, which has to improve the diversity of the algorithm and the search ability. Based on the MOPSO algorithm, a new external archive control strategy was established by using the grid technology and Pareto-dominant ordering principle, and the learning samples were improved. The proposed GDMOPSO is compared with a group of benchmark function tests and four classical algorithms. The results of experiment show that our proposed algorithm can effectively avoid premature convergence in terms of generational distance and hyper-volume (HV) indicator compared with other four classical MOPSO algorithms.

关键词： External archive control grid distance learning sample multi-objective particle swarm optimization premature convergence

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Industrial carbon emission efficiency prediction and carbon emission reduction strategies based on multi-objective particle swarm optimization-backpropagation: A perspective from regional clustering

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SCIENCE OF THE TOTAL ENVIRONMENT 2024年 906卷 167692页

作者： Jiang, Hongtao Yin, Jian Wei, Danqi Luo, Xinyuan Ding, Yi Xia, Ruici Guizhou Univ Finance & Econ Ctr China Western Modernizat Guiyang 550025 Guizhou Peoples R China Guizhou Univ Finance & Econ Coll Big Data Applicat & Econ Guiyang 550025 Guizhou Peoples R China Key Lab Green Fintech Guiyang 550025 Peoples R China Guizhou Univ Finance & Econ Ctr China Western Modernizat Guiyang 550025 Guizhou Peoples R China

Against the backdrop of global climate change, industrial carbon emission reduction has become an important pathway to for global low-carbon development. This study constructs a framework of geographic spatial con-straints regionalization and multi-objective machine learning to predict future industrial carbon emission effi-ciency (ICEE) and explore strategies for carbon emission reduction. Firstly, the ICEE of 285 Chinese cities were calculated by the super-efficiency slacks-based measure. Secondly, the cities were classified into four ICEE level regions through the spatially constrained multivariate clustering. Next, the multi-objective particle swarm optimization-BP (MOPSO-BP) model was constructed to predict the future trends of ICEE in the four regions. Finally, the geographical detector and multi-scale geographically weighted regression were employed for exploring driving force and carbon emission reduction strategies in different regions. The results show that most cities had low or medium ICEE, while super efficiency cities were mainly distributed in the east coastal areas. The prediction performance of the MOPSO-BP model for the four regions was better than the ordinary particle swarm optimization-BP and traditional BP model. Except for the Agricultural Production Region, there is considerable room for improving the ICEE of other regions over the next decade. Macroeconomic and microeconomic development have a global effect in promoting regional ICEE improvement, urban construction shows a promoting or inhibiting effect in different regions, and information technology has significant spatial heterogeneity in its influence within each region. The analysis framework developed in the study is a reliable solution for managing and planning ICEE and provides constructive suggestions for future regional low-carbon development.

关键词： Industrial carbon emission efficiency Spatially constrained multivariate clustering multi-objective particle swarm optimization BP neural network Geographical detector multi-scale geographically weighted regression

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A multi-objective particle swarm optimization with density and distribution-based competitive mechanism for sensor ontology meta-matching

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COMPLEX & INTELLIGENT SYSTEMS 2023年第1期9卷 435-462页

作者： Geng, Aifeng Lv, Qing Taiyuan Univ Technol Coll Elect & Power Engn Taiyuan 030024 Peoples R China

Sensor ontology is a standard conceptual model that describes information of sensor device, which includes the concepts of various sensor modules and the relationships between them. The problem of heterogeneity between sensor ontologies is introduced because different sensor ontology engineers have different ways of describing sensor devices and different structures for the construction of sensor ontologies. Addressing the heterogeneity of sensor ontologies contributes to facilitate the semantic fusion of two sensor ontologies, enabling the sharing and reuse of sensor information. To solve the above problem, an ontology meta-matching method is proposed by this paper to find out the correspondence between entities in distinct sensor ontologies. How to measure the degree of similarity between entities with a set of suitable similarity measures and how to better integrate multiple measures to determine the equivalent entities are the challenges of the ontology meta-matching problem. In this paper, two approximate measurement methods of the quality for ontology matching results are designed, and a multi-objective optimization model for the ontology meta-matching problem is constructed based on these methods. Eventually, a multi-objective particle swarm optimization (MOPSO) algorithm is propounded to dispose of the problem and optimize the quality of ontology meta-matching results, which is named density and distribution-based competitive mechanism multi-objective particle swarm algorithm ((DCMOPSO)-C-2). The sophistication of the (DCMOPSO)-C-2 based sensor ontology meta-matching method is verified through experiments. Comparing with other matching systems and advanced systems of Ontology Alignment Evaluation Initiative (OAEI), the proposed method can improve the quality of matching results more effectively.

关键词： Sensor ontology Ontology meta-matching multi-objective particle swarm optimization Competitive mechanism Ontology Alignment Evaluation Initiative

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A novel coevolutionary multi-objective particle swarm optimization based on decomposition

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EVOLUTIONARY INTELLIGENCE 2024年第2期17卷 643-652页

作者： Zhu, Sifeng Yang, Chengrui Hu, Jiaming Chen, Hao Zhang, Hui Tianjin Chengjian Univ Sch Comp & Informat Engn Tianjin 300384 Peoples R China

To improve the performance of particle swarm optimization (PSO) and balance convergence and diversity, we propose a coevolutionary multi-objective particle swarm optimization based on decomposition (CMOPSO). CMOPSO includes 3 strategies, coevolutionary mechanism, best-effort strategy and weighted maximum approach. Coevolutionary mechanism is used to maintain convergence, while PSO operator focuses on diversity. Best-effort strategy allows operators that perform well enough to execute again, which improves the utilization of computing resources. Weighted maximum approach is an environmental selection strategy based on decomposition, which selects by comparing the maximum of weighted objective values of the subproblem. Each new individual will be compared with the best individual of all subproblems, not only in the sub domain, which helps the PSO operator to maintain the diversity in search process. The CMOPSOD is tested against 6 other algorithms on the ZDT and UF test problems, the results show that the proposed CMOPSOD has significant advantages in terms of convergence and diversity.

关键词： multi-objective particle swarm optimization Coevolutionary mechanism Best-effort strategy Weighted maximum approach

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A new method of smoothness-constrained magnetotelluric modelling with the utility of Pareto-optimal multi-objective particle swarm optimization

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GEOPHYSICAL PROSPECTING 2024年第5期72卷 1985-2004页

作者： Buyuk, Ersin Gumushane Univ Fac Engn & Nat Sci Dept Geophys Engn TR-29100 Gumushane Turkiye Marmara Res Ctr Earth Sci Res Grp TUBITAK Gebze Kocaeli Turkiye

particle swarm optimization, one of the modern global optimization methods, is attracting widespread interest because it overcomes the difficulties of conventional inversion techniques, such as trapping at a local minimum and/or initial model dependence. The main characteristic of particle swarm optimization is the large search space of parameters, which in a sense allows the exploration of the entire objective function space if the input parameters are properly chosen. However, in the case of a high-dimensional model space, the numerical instability of the solution may increase and lead to unrealistic models and misinterpretations due to the sampling problem of particle swarm optimization. Therefore, smoothness-constrained regularization techniques used for the objective function or model reduction techniques are required to stabilize the solution. However, weighting and combining objective function terms is partly a subjective process, as the regularization parameter is generally chosen based on some kind of criteria of how the smoothing constraints affect the data misfits. This means that it cannot be completely predefined but needs to be adjusted during the inversion process, which begins with the response of an initial model. In this paper, a new modelling approach is proposed to obtain a smoothness-constrained model from magnetotelluric data utilizing multi-objective particle swarm optimization based on the Pareto optimality approach without using a regularization parameter and combining several objective function terms. The presented approach was verified on synthetic models and an application with field data set from the canakkale-Tuzla geothermal field in Turkey. Findings from these analyses confirm the usefulness of the method as a new approach for all constrained inversions of geophysical data without the need to combine the objective function terms weighted by a regularization parameter.

关键词： electromagnetical modelling magnetotelluric modelling multi-objective particle swarm optimization Pareto optimality regularization smoothness constraint

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Improved multi-objective particle swarm optimization with preference strategy for optimal DG integration into the distribution system

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NEUROCOMPUTING 2015年 148卷 23-29页

作者： Cheng, Shan Chen, Min-You Fleming, Peter J. Chongqing Univ Sch Elect Engn Chongqing 400044 Peoples R China Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Se Chongqing 400044 Peoples R China Univ Sheffield Dept Automat Control & Syst Engn Sheffield S10 2TN S Yorkshire England

Considering the different requirements for decision and state variables in engineering optimizations, an improved multi-objective particle swarm optimization with preference strategy (IMPSO-PS) is presented and applied to the optimal integration of distributed generation (DG) into the distribution system. Preference factors are introduced to quantify the degree of preference for certain attributes in the constraint-space. In addition to the application of a popular non-dominated sorting technique for identifying Pareto solutions, the performance of IMPSO-PS is strengthened via the inclusion of a dynamic selection of the global bests, a novel circular non-dominated selection of particles from one iteration to the next and a special mutation operation. The proposed algorithm has been successfully applied to benchmark functions and to the multi-objective optimal integration of DG into an IEEE 33-bus system. This real-world application aims to satisfy some special preferences and determine the optimal locations and capacities of DG units to minimize the total active power loss of the system and decrease cost caused by power generation and pollutant emissions. The results show that the proposed approach can provide a wider range of Pareto solutions of high quality, while satisfying special preference demands. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Circular non-dominated selection Distributed generation (DG) multi-objective particle swarm optimization NSGA-II Optimal allocation Preference strategy

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Hyperplane-Assisted multi-objective particle swarm optimization with Twofold Proportional Assignment Strategy

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2024年第1期17卷 1-27页

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

In the simultaneous optimization of multiple objectives, how to balance convergence promotion and diversity preservation in the evolutionary process is a key and challenging problem. In this research, a hyperplane-assisted multi-objective particle swarm optimization with a twofold proportional assignment strategy (tpahaMOPSO) is suggested to ameliorate the optimization performance of MOPSO. First, the external archive is maintained in combination with hyperplane-based convergence evaluation and shift-based density estimation to retain high-quality candidate solutions. Second, a twofold proportional assignment scheme is designed to search the surrounding region of candidate solutions with better potential to emphasize convergence and diversity, respectively. Third, the domination relationship and convergence difference are combined to select a more reasonable individual historical best and reduce the risk of particle aggregation. Finally, the proposed tpahaMOPSO was compared with ten representative and advanced multi-objective optimization algorithms on 22 widely used test functions with different characteristics. The simulation results present that the developed tpahaMOPSO got the best result in 11 benchmark functions for both IGD and HV criteria. Concurrently, the Friedman test was applied for ranking analysis and the proposed algorithm also obtained excellent statistical analysis results. The promising performance and strong competitiveness of the proposed tpahaMOPSO have been verified by different experimental studies.

关键词： multi-objective particle swarm optimization Hyperplane Twofold proportional assignment Convergence difference

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Robust optimization of constrained mechanical system with joint clearance and random parameters using multi-objective particle swarm optimization

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STRUCTURAL AND multiDISCIPLINARY optimization 2018年第5期58卷 2073-2084页

作者： Sun, Dongyang Shi, Yan Zhang, Baoqiang Chongqing Univ Coll Aerosp Engn Chongqing 400044 Peoples R China Nanjing Univ Aeronaut & Astronaut State Key Lab Mech & Control Mech Struct Nanjing 210016 Jiangsu Peoples R China Xiamen Univ Sch Aerosp Engn Xiamen 361005 Peoples R China

Joint clearance and uncertainty are inevitable in mechanical systems due to design tolerance, abrasion, manufacture error, assembly error and imperfections. In this study, kinematic analysis and robust optimization of constrained mechanical systems with joint clearance and random parameters were performed. Joint clearance was modeled by Lankarani-Nikravesh contact force model, and probability space was applied for characterizing uncertain parameters. A kinematic analysis method based on Baumgarte approach and confidence region method was presented to predict kinematic error of the mechanical system. Slider-crank mechanism, an illustrative example was presented to show the influence of clearance and uncertainty on the kinematic accuracy. Then, a novel multi-objective robust optimization methodology was presented for kinematic accuracy robust optimization design of the constrained mechanical system. In this approach, a multi-objective robust optimization model derived from 95% confidence region is constructed to reduce the effects of clearance and parameter uncertainty on 95% confidence region of kinematic error. The robust optimization model is a double-loop process. A multi-objective robust optimization strategy, combing Kriging surrogate model, multi-objective particle swarm optimization, confidence region and Monte Carlo methods, was proposed to search the design variables for minimizing the optimization objectives derived from confidence region while balancing computational accuracy and efficiency of the optimization process. The optimal results of the slider-crank mechanism demonstrated the validity and feasibility of the proposed robust optimization method.

关键词： Robust optimization Kinematic analysis Kriging surrogate model multi-objective particle swarm optimization Joint clearance

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