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A transfer learning-based particle swarm optimization algorithm for travelling salesman problem

JOURNAL OF computationAL DESIGN AND ENGINEERING 2022年第3期9卷 933-948页

作者： Zheng, Rui-zhao Zhang, Yong Yang, Kang China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221116 Jiangsu Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China

To solve travelling salesman problems (TSPs), most existing evolutionary algorithms search for optimal solutions from zero initial information without taking advantage of the historical information of solving similar problems. This paper studies a transfer learning-based particle swarm optimization (PSO) algorithm, where the optimal information of historical problems is used to guide the swarm to find optimal paths quickly. To begin with, all cities in the new and historical TSP problems are clustered into multiple city subsets, respectively, and a city topology matching strategy based on geometric similarity is proposed to match each new city subset to a historical city subset. Then, on the basis of the above-matched results, a hierarchical generation strategy of the feasible path (HGT) is proposed to initialize the swarm to improve the performance of PSO. Moreover, a problem-specific update strategy, i.e. the particle update strategy with adaptive crossover and clustering-guided mutation, is introduced to enhance the search capability of the proposed algorithm. Finally, the proposed algorithm is applied to 20 typical TSP problems and compared with 12 state-of-the-art algorithms. Experimental results show that the transfer learning mechanism can accelerate the search efficiency of PSO and make the proposed algorithm achieve better optimal paths.

关键词： evolutionary computation particle swarm optimization travelling salesman problem

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An evolutionary Game Simulation of a Composite Subsidy Policy to Promote Military-Civilian Integration: A National System of Innovation With Chinese Characteristics

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IEEE ACCESS 2022年 10卷 88276-88294页

作者： Qi, Ning Lu, Sniping Jing, Hao Shenyang Aerosp Univ Sch Econ & Management Shenyang 110031 Peoples R China

Collaborative innovation involving military and civilian actors is a key component of National Systems of Innovation. It is an approach that is explicitly promoted in China under the name of Military-Civilian Integration. To more effectively promote Military-Civilian Integration, it is necessary to study the impact of government subsidies on enterprise-level behavior. The research reported in this paper analyzes a composite subsidy policy's influence on the cooperative innovation behavior of military and civilian enterprises with game theory. First, during the early stage of development, an R&D subsidy can stimulate firms to invest in technological innovation, but it harms the long-term and stable development of civil-military cooperation by encouraging free-riding behaviors. Second, as military-civilian integration continues and deepens, the input-output rate of the R&D will increase, and this makes the role of tax subsidies increasingly relevant. Third, the composite application of scale and tax subsidies will be most effective in reinforcing innovation during the Industrialization Stage. Fourth, further simulations show an apparent heterogeneity in the responses of the military enterprise and the civilian enterprise to the three kinds of subsidy that we have considered. Civilian enterprises are more sensitive to subsidies, while military enterprises are more sensitive to technology spillovers. Therefore, the government should build a military-civilian integration platform to share resources in ways that maintain security confidence and improve the R&D input-output rate. The government should strengthen confidentiality management, training, and risk prevention and control measures for military-civilian integration projects, to reduce the risk of technology spillover.

关键词： Government policies Research and development evolutionary computation Collaboration Investment Instruments Game theory Collaborative innovation evolutionary game military-civilian integration subsidy policy

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Enhancement of power quality issues for a hybrid AC/DC microgrid based on optimization methods

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IET RENEWABLE POWER GENERATION 2022年第8期16卷 1773-1791页

作者： Khosravi, Nima Echalih, Salwa Baghbanzadeh, Rasoul Hekss, Zineb Hassani, Rahman Messaoudi, Mustapha R&D Management NPC Dept Elect & Instrumentat Engn Tehran Iran Hassan II Univ Casablanca ENSEM Casablanca ESE Lab Casablanca Morocco Islamic Azad Univ Dept Elect Engn Tabriz Branch Tabriz Iran R&D Management PGPIC Dept Elect & Instrumentat Engn Tehran Iran Islamic Azad Univ Dept Elect Engn Khorramabad Branch Khorramabad Iran Univ Gabes Natl Engn Sch Gabes Proc Energy Environm & Elect Syst Gabes Tunisia

The purpose of this paper is to study the harmonic behavior of hybrid microgrids (MGs). To achieve the desired goal, a modified active power filter (MAPF) and a power filter compensator kit (PFCK) modules have been used to improve the harmonics of the AC part of the system. The main challenge and feature of this research is the optimization of MAPF and PFCK controller gain coefficients hierarchical, using Harris hawk optimization (HHO), grasshopper optimization algorithm (GOA), artificial bee colony (ABC), and differential evolution (DE), respectively. Following, this technique was applied in the three control loops including, voltage and current harmonics, and system controller error to reduce the range of the mentioned harmonics to the permissible range. The method will be interesting considering that the PI and PID tuning is based on parallel feed-forward. The programmed objective functions include the three components current THD, voltage THD and controller error hierarchically. From the point of view of the MGs hierarchical control, the method presented in this study will be implemented in the second level of hierarchical control. In order to improve the operation of the DC part of the grid, a boost converter module (BCM) has been introduced in the form of a new synchronization scheme. The output of the simulation shows the reduction of the harmonic amplitude of the system by the mentioned modules.

关键词： AC-DC power convertors artificial bee colony current harmonics BCM power filter compensator kit modules three-term control MAPF power generation control distributed power generation modified active power filter hybrid AC-DC microgrids synchronization scheme power harmonic filters grasshopper optimization algorithm differential evolution ABC Harris hawk optimization boost converter module voltage harmonics current THD PFCK controller gain coefficients GOA electric current control parallel feed-forward programmed objective functions compensation power quality issues system controller error voltage THD simulation evolutionary computation MG hierarchical control optimization methods harmonic amplitude power supply quality harmonic distortion HHO control loops artificial bee colony algorithm voltage control PID tuning synchronisation

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A Large-Scale Combinatorial Many-Objective evolutionary Algorithm for Intensity-Modulated Radiotherapy Planning

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IEEE TRANSACTIONS ON evolutionary computation 2022年第6期26卷 1511-1525页

作者： Tian, Ye Feng, Yuandong Wang, Chao Cao, Ruifen Zhang, Xingyi Pei, Xi Tan, Kay Chen Jin, Yaochu Anhui Univ Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei 230601 Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China Univ Sci & Technol China Sch Nucl Sci & Technol Hefei 230026 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China Bielefeld Univ Fac Technol D-33619 Bielefeld Germany

Intensity-modulated radiotherapy (IMRT) is one of the most popular techniques for cancer treatment. However, existing IMRT planning methods can only generate one solution at a time and, consequently, medical physicists should perform the planning process many times to obtain diverse solutions to meet the requirement of a clinical case. Meanwhile, multiobjective evolutionary algorithms (MOEAs) have not been fully exploited in IMRT planning since they are ineffective in optimizing the large number of discrete variables of IMRT. To bridge the gap, this article formulates IMRT planning into a large-scale combinatorial many-objective optimization problem and proposes a coevolutionary algorithm to solve it. In contrast to the existing MOEAs handling high-dimensional search spaces via variable grouping or dimensionality reduction, the proposed algorithm evolves one population with fine encoding for local exploitation and evolves another population with rough encoding for global exploration. Moreover, the convergence speed is further accelerated by two customized local search strategies. The experimental results verify that the proposed algorithm outperforms state-of-the-art MOEAs and IMRT planning methods on a variety of clinical cases.

关键词： Planning Optimization Apertures Linear programming Tumors Sequential analysis Particle beams Combinatorial optimization evolutionary computation intensity-modulated radiotherapy (IMRT) large-scale optimization many-objective optimization

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An Online Prediction Approach Based on Incremental Support Vector Machine for Dynamic Multiobjective Optimization

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IEEE TRANSACTIONS ON evolutionary computation 2022年第4期26卷 690-703页

作者： Xu, Dejun Jiang, Min Hu, Weizhen Li, Shaozi Pan, Renhu Yen, Gary G. Xiamen Univ Sch Informat Minist Culture & Tourism China Xiamen 361005 Fujian Peoples R China Xiamen Univ Key Lab Digital Protect & Intelligent Proc Intang Minist Culture & Tourism China Xiamen 361005 Fujian Peoples R China Beijing Jingdong Century Trade Co Ltd Multiparty Data Comp R&D Dept Beijing 100020 Peoples R China Fujian Longking Co Ltd Pneumat Conveying Dept Longyan 364000 Fujian Peoples R China Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA

Real-world multiobjective optimization problems usually involve conflicting objectives that change over time, which requires the optimization algorithms to quickly track the Pareto-optimal front (POF) when the environment changes. In recent years, evolutionary algorithms based on prediction models have been considered promising. However, most existing approaches only make predictions based on the linear correlation between a finite number of optimal solutions in two or three previous environments. These incomplete information extraction strategies may lead to low prediction accuracy in some instances. In this article, an incremental support vector machine (ISVM)-based dynamic multiobjective evolutionary algorithm, in short called ISVM-DMOEA, is proposed. We treat the solving of dynamic multiobjective optimization problems (DMOPs) as an online learning process, using the continuously obtained optimal solution to update an ISVM without discarding the solution information at earlier time. ISVM is then used to filter random solutions and generate an initial population for the next moment. To overcome the obstacle of insufficient training samples, a synthetic minority oversampling strategy is implemented before the training of ISVM. The advantage of this approach is that the nonlinear correlation between solutions can be explored online by ISVM, and the information contained in all historical optimal solutions can be exploited to a greater extent. The experimental results and comparison with the chosen state-of-the-art algorithms demonstrate that the proposed algorithm can effectively tackle DMOPs.

关键词： Optimization Heuristic algorithms Statistics Sociology Prediction algorithms Support vector machines evolutionary computation evolutionary algorithm incremental support vector machine (ISVM) multiobjective optimization oversampling prediction model

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Helper and Equivalent Objectives: Efficient Approach for Constrained Optimization

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第1期52卷 240-251页

作者： Xu, Tao He, Jun Shang, Changjing Aberystwyth Univ Dept Comp Sci Aberystwyth SY23 3DB Dyfed Wales Nottingham Trent Univ Sch Sci & Technol Nottingham NG11 8NS England

Numerous multiobjective evolutionary algorithms (EAs) have been designed for constrained optimization over the past two decades. The idea behind these algorithms is to transform constrained optimization problems (COPs) into multiobjective optimization problems without any constraint, and then solve them. In this article, we propose a new multiobjective method for constrained optimization, which works by converting a COP into a problem with helper and equivalent objectives. An equivalent objective means that its optimal solution set is the same as that of the constrained problem but a helper objective does not. Then, this multiobjective optimization problem is decomposed into a group of subproblems using the weighted sum approach. Weights are dynamically adjusted so that each subproblem eventually tends to a problem with an equivalent objective. We theoretically analyze the computational time of the helper and equivalent objective method on a hard problem called "wide gap." In a wide gap problem, an algorithm needs exponential time to cross between two fitness levels (a wide gap). We prove that using helper and equivalent objectives can shorten the time of crossing the wide gap. We conduct a case study for validating our method. An algorithm with helper and equivalent objectives is implemented. The experimental results show that its overall performance is ranked first when compared with other eight state-of-the-art EAs on IEEE CEC2017 benchmarks in constrained optimization.

关键词： Optimization Linear programming Sociology Statistics evolutionary computation Constraint handling Cybernetics Algorithm analysis constrained optimization constraint handling evolutionary algorithms (EAs) multiobjective optimization objective decomposition

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Impact of Boundary Control Methods on Bound-constrained Optimization Benchmarking Hot-off-the-Press Track at GECCO 2023

Impact of Boundary Control Methods on Bound-constrained Opti...

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Genetic and evolutionary computation Conference (GECCO)

作者： Kadavy, Tomas Viktorin, Adam Kazikova, Anezka Pluhacek, Michal Senkerik, Roman Tomas Bata Univ Zlin Zlin Czech Republic

ISBN: (纸本)9798400701207

Despite initial indifference towards boundary control methods (BCM) in the context of metaheuristic algorithm design, benchmarking, and execution, our research demonstrates their critical importance. This study investigates how the choice of a particular BCM can profoundly influence the performance of competitive algorithms. We analyzed the top three algorithms from the 2017 and 2020 IEEE CEC competitions, posing the following question: Could a change in BCM usage alter an algorithm's overall performance and, consequently, its ranking among competitors? Our findings reveal that paying attention to BCMs can lead to significant improvements. The experiments revealed that BCM selection can significantly impact an algorithm's performance and, in some instances, its competition rank. However, most authors omitted to mention the implemented BCM, resulting in poor reproducibility and deviating from recommended benchmarking practices for metaheuristic algorithms. The conclusion is that the BCM should be considered another vital metaheuristics input variable for unambiguous reproducibility of results in benchmarking and for a better understanding of population dynamics.

关键词： evolutionary computation computational intelligence performance evaluation benchmark testing boundary control method

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Can Individuals be Repeatedly Selected for Mutation in Matrix-Based DE? A Comparative Study 11

Can Individuals be Repeatedly Selected for Mutation in Matri...

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11th International Conference on Machine Intelligence Theory and Applications, MiTA 2024

作者： Wei, Ling-Ling Sun, Bing Li, Jian-Yu Yang, Qiang Xu, Jing Tang, Fei-Yi South China Normal University School of Computer Science Guangzhou510631 China Nankai University College of Artificial Intelligence Tianjin300350 China Nanjing University of Information Science and Technology School of Artificial Intelligence Jiangsu China Guangzhou Panyu Polytechnic Pazhou Lab School of Information Engineering Guangdong China

ISBN: (纸本)9798350385670

evolutionary computation (EC) is a class of algorithms derived from the simulation of natural phenomena, which faces the challenges of long running times and high computing loads resulting from large-scale problems. For this reason, the matrix-based EC (MEC) has been presented as an extension of classic EC algorithms to address large-scale optimization. The MEC can be implemented directly on high-performance computing resources, thus accelerating the computation of evolutionary operators. However, MEC is still in its infancy and lacks deeper analysis. Hence, this paper analyzes and investigates matrix-based differential evolution (MDE) and advances the development of related research through theoretical analysis and experimental validation, which has two contributions. First, through the comprehensive comparison of MDE and DE in different problems, we compare the solution quality and computational speed of MDE and DE, and show that the solution quality of MDE is not significantly different from that of DE, but the computational speed of MDE is much faster than that of DE. Second, we also further analyze the mutation mechanism of MDE, focusing on the effect of the repeatability of individuals in the mutation process on the performance of the algorithms, which provides a basis for future research on MDE of MEC. © 2024 IEEE.

关键词： differential evolution evolutionary computation matrix-based evolutionary computation parallel computing

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Genetic Programming Approach for the Detection of Mistletoe Based on UAV Multispectral Imagery in the Conservation Area of Mexico City

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REMOTE SENSING 2022年第3期14卷 801页

作者： Mejia-Zuluaga, Paola Andrea Dozal, Leon Valdiviezo-N., Juan C. Ctr Invest Ciencias Informac Geoespacial CtrGe Ciudad Mexico 14240 Mexico CONACYT CtrGeo Aguascalientes 20313 Aguascalientes Mexico CONACYT CtrGeo Yucatan 97302 Mexico

The mistletoe Phoradendron velutinum (P. velutinum) is a pest that spreads rapidly and uncontrollably in Mexican forests, becoming a serious problem since it is a cause of the decline of 23.3 million hectares of conifers and broadleaves in the country. The lack of adequate phytosanitary control has negative social, economic, and environmental impacts. However, pest management is a challenging task due to the difficulty of early detection for proper control of mistletoe infestations. Automating the detection of this pest is important due to its rapid spread and the high costs of field identification tasks. This paper presents a Genetic Programming (GP) approach for the automatic design of an algorithm to detect mistletoe using multispectral aerial images. Our study area is located in a conservation area of Mexico City, in the San Bartolo Ameyalco community. Images of 148 hectares were acquired by means of an Unmanned Aerial Vehicle (UAV) carrying a sensor sensitive to the R, G, B, red edge, and near-infrared bands, and with an average spatial resolution of less than 10 cm per pixel. As a result, it was possible to obtain an algorithm capable of classifying mistletoe P. velutinum at its flowering stage for the specific case of the study area in conservation area with an Overall Accuracy (OA) of 96% and a value of fitness function based on weighted Cohen's Kappa (k(w)) equal to 0.45 in the test data set. Additionally, our method's performance was compared with two traditional image classification methods;in the first, a classical spectral index, named Intensive Pigment Index of Structure 2 (SIPI2), was considered for the detection of P. velutinum. The second method considers the well-known Support Vector Machine classification algorithm (SVM). We also compare the accuracy of the best GP individual with two additional indices obtained during the solution analysis. According to our experimental results, our GP-based algorithm outperforms the results obtained by the afore

关键词： evolutionary computation image detection forest pest supervised learning vegetation index computer vision change detection

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A Buffer-Based Ant Colony System Approach for Dynamic Cold Chain Logistics Scheduling

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IEEE TRANSACTIONS ON EMERGING TOPICS IN computationAL INTELLIGENCE 2022年第6期6卷 1438-1452页

作者： Wu, Li-Jiao Shi, Lin Zhan, Zhi-Hui Lai, Kuei-Kuei Zhang, Jun South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China Pazhou Lab Guangzhou 510330 Guangdong Peoples R China Guangdong Prov Key Lab Computat Intelligence & Cy Guangzhou 510006 Guangdong Peoples R China Chaoyang Univ Technol Dept Business Adm Taichung 413 Taiwan Zhejiang Normal Univ Jinhua 321004 Zhejiang Peoples R China Hanyang Univ Ansan 15588 South Korea

Cold chain logistics (CCL) scheduling is an emerging research problem in the logistics industry in smart cities, which mainly concerns the distribution of perishable goods. As the quality loss of goods that occurs in the distribution process should be considered, the CCL scheduling problem is very challenging. Moreover, the problem is more challenging when the dynamic characteristics (e.g., the orders are unknown beforehand) of the real scheduling environment are considered. Therefore, this paper focuses on the dynamic CCL (DCCL) scheduling problem by establishing a practical DCCL model. In this model, a working day is divided into multiple time slices so that the dynamic new orders revealed in the working day can be scheduled in time. The objective of the DCCL model is to minimize the total distribution cost in a working day, which includes the transportation cost, the cost of order rejection penalty, and the cost of quality loss of goods. To solve the DCCL model, a buffer-based ant colony system (BACS) approach is proposed. The BACS approach is characterized by a buffering strategy that is carried out at the beginning of the scheduling in every time slice except the last one to temporarily buffer some non-urgent orders, so as to concentrate on scheduling the orders that are preferred to be delivered first. Besides, to further promote the performance of BACS, a periodic learning strategy is designed to avoid local optima. Comparison experiments are conducted on test instances with different problem scales. The results show that BACS is more preferred for solving the DCCL model when compared with the other five state-of-the-art and recent well-performing scheduling approaches.

关键词： Dynamic scheduling Processor scheduling Costs Vehicle dynamics Logistics Job shop scheduling Task analysis Cold chain logistics (CCL) dynamic optimization ant colony system (ACS) vehicle routing problem evolutionary computation logistics scheduling

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