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Enhanced slime mould algorithm with multiple mutation strategy and restart mechanism for global optimization

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第6期42卷 5069-5083页

作者： Zheng, Rong Jia, Heming Wang, Shuang Liu, Qingxin Sanming Univ Coll Informat Engn Sanming Fujian Peoples R China Hainan Univ Sch Comp Sci & Technol Haikou Hainan Peoples R China

Slime mould algorithm (SMA) is a new metaheuristic algorithm proposed in 2020, which has attracted extensive attention from scholars. Similar to other optimization algorithms, SMA also has the drawbacks of slow convergence rate and being trapped in local optimum at times. Therefore, the enhanced SMA named as ESMA is presented in this paper for solving global optimization problems. Two effective methods composed of multiple mutation strategy (MMS) and restart mechanism (RM) are embedded into the original SMA. MMS is utilized to increase the population diversity, and the RM is used to avoid the local optimum. To verify the ESMA's performance, twenty-three classical benchmark functions are employed, as well as three well-known engineering design problems, including welded beam design, pressure vessel design and speed reducer design. Several famous optimization algorithms are also chosen for comparison. Experimental results show that the ESMA outperforms other optimization algorithms in most of the test functions with faster convergence speed and higher solution accuracy, which indicates the merits of proposed ESMA. The results of Wilcoxon signed-rank test also reveal that ESMA is significant superior to other comparative optimization algorithms. Moreover, the results of three constrained engineering design problems demonstrate that ESMA is better than comparative algorithms.

关键词： Slime mould algorithm multiple mutation strategy restart mechanism global optimization optimization algorithm

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Heuristic and random search algorithm in optimization of route planning for Robot's geomagnetic navigation

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COMPUTER COMMUNICATIONS 2020年第0期154卷 12-17页

作者： Xu, Yan Guan, Guofei Song, Qingwu Jiang, Chao Wang, Lihui Jiangsu Frontier Elect Technol Co Ltd Nanjing 211102 Peoples R China Southeast Univ Sch Instrument Sci & Engn Key Lab Microinertial Instrument & Adv Nav Techno Minist Educ Nanjing 210096 Peoples R China

To improve the efficiency and accuracy, a new combination algorithm for route planning is proposed, by considering underwater geomagnetic matching navigation area and distribution of environmental constraints. Firstly, with geomagnetic navigation matching regions, Dijkstra algorithm can obtain the primary route points. Secondly, the environmental constraints models are built and normalized, and the route planning environment constrained cost model is established. Thirdly, with the relationships between time, function relation, constraint condition and variable in the environment constrained cost model, the particle swarm optimization algorithm is introduced. With the primary route pints, the route planning is transformed into route optimization. Finally, the primary route points are used as the initial input of the particle swarm optimization algorithm, then the methods of selecting the inertia weight of the particle swarm and the particle coding are improved. The optimal route planning of Dijkstra algorithm and particle swarm optimization is realized. Simulation results demonstrate that the particle size of the search space can get a minimized evaluation, more narrowed search range and higher efficient search. The combination algorithm guarantees the global optimal while ensures the local optimal, then, the non-matching navigation areas can be effectively avoided, and efficient route planning functions can be achieved.

关键词： Geomagnetic field navigation Route planning Geomagnetic navigation matching area Environmental constraint modeling optimization algorithm

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Robotic arm trajectory optimization based on multiverse algorithm

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第2期20卷 2776-2792页

作者： Liu, Junjie Wang, Hui Li, Xue Chen, Kai Li, Chaoyu Nanjing Univ Aeronaut & Astronaut Sch Mech & Elect Engn Nanjing 210016 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Beijing Peoples R China

For inefficient trajectory planning of six-degree-of-freedom industrial manipulators, a tra-jectory planning algorithm based on an improved multiverse algorithm (IMVO) for time, energy, and impact optimization are proposed. The multi-universe algorithm has better robustness and conver-gence accuracy in solving single-objective constrained optimization problems than other algorithms. In contrast, it has the disadvantage of slow convergence and quickly falls into local optimum. This paper proposes a method to improve the wormhole probability curve, adaptive parameter adjustment, and population mutation fusion to improve the convergence speed and global search capability. In this paper, we modify MVO for multi-objective optimization to derive the Pareto solution set. We then construct the objective function by a weighted approach and optimize it using IMVO. The results show that the algorithm improves the timeliness of the six-degree-of-freedom manipulator trajectory operation within a specific constraint and improves the optimal time, energy consumption, and impact problems in the manipulator trajectory planning.

关键词： trajectory planning optimization algorithm optimal time optimal energy consumption optimal impact

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A Novel Software Tool for Automated and Integrated Building Energy Model Calibration 11th

A Novel Software Tool for Automated and Integrated Building ...

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11th International Conference of Ar.Tec.

作者： Maracchini, Gianluca D'Orazio, Marco Di Giuseppe, Elisa Revel, Gian Marco Univ Trento Dept Civil Environm & Mech Engn I-38123 Trento Italy Marche Polytech Univ Dept Construct Civil Engn & Architecture I-60131 Ancona Italy Marche Polytech Univ Dept Ind Engn & Math Sci I-60131 Ancona Italy

ISBN: (纸本)9783031718656;9783031718632;9783031718625

In the EU, the building sector significantly impacts energy consumption and greenhouse gas emissions, accounting for 40% of the total energy use and 35% of emissions, mainly due to the energy inefficiency of the building stock. With energy demand expected to increase over the next decade, improving building energy efficiency is essential for meeting EU sustainability goals. Building Energy Models (BEMs) are crucial for evaluating and enhancing building performance throughout their lifecycle. However, a notable "energy performance gap" usually exists between predicted and actual energy use, exacerbated by challenges in accurately inputting numerous variables and the simplifications inherent in modeling. BEM calibration (BC) approaches are often adopted to reduce these discrepancies, aimed at adjusting model inputs to match output with the observed data. Yet, there is not a universal consensus on which is the best calibration method, with manual and automated approaches offering different benefits. Automated methods, especially those using optimization algorithms, have gained prominence for their efficiency and ability to handle uncertainties. However, BC still significantly depends on the energy modelers' expertise. This paper introduces a novel software tool for automated BC, aiming to simplify the process by integrating expert knowledge, sensitivity analysis, and optimization algorithms techniques in a unique workflow. This tool reduces the dependence of BC success on modeler expertise, representing a significant step towards more accessible automated BC in the research field and engineering practice, thence allowing a more effective design of energy conservation measures.

关键词： Building Energy Model Model calibration optimization algorithm Sensitivity analysis Software

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Arch bridge damage detection using vibration data and gradient-based optimizer algorithm

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INNOVATIVE INFRASTRUCTURE SOLUTIONS 2025年第2期10卷 1-12页

作者： Mohebian, Pooya Motahari, Mohammad Reza Aval, Seyed Bahram Beheshti Arak Univ Fac Engn Dept Civil Engn Arak Iran KN Toosi Univ Technol Fac Civil Engn Tehran Iran

Bridges are one of the most pivotal civil engineering structures. Nevertheless, they are highly susceptible to localized damage, which poses a gradual threat to their long-term stability and functionality. Hence, it is of utmost importance to detect any damage in bridges to ensure their continued safe and reliable performance. In view of this, the present study proposes an optimization-based finite element model updating method to perform arch bridge damage detection by using vibration data and the gradient-based optimizer (GBO) algorithm. The damage severity of structural elements in different parts of the bridge, including those in the arch, columns, and deck, are taken as damage variables. The objective function of the optimization problem is also defined in terms of discrepancies between the vibration data of the actual damaged bridge and those computed from the finite element model. The GBO algorithm, as a new robust meta-heuristic algorithm, is applied to tackle the damage detection problem, and its performance is compared to other well-known meta-heuristic algorithms. The applicability of the proposed method is numerically assessed on an arch bridge structure under both noise-free and noisy conditions. The obtained results demonstrate the accuracy and computational efficiency of the presented method in dealing with the arch bridge damage detection problem.

关键词： Damage detection Arch bridge Vibration method Finite element model updating optimization algorithm

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Turboprop Cycle optimization Using Repulsive Particle Swarm algorithm

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JOURNAL OF PROPULSION AND POWER 2010年第4期26卷 882-891页

作者： Boulkeraa, Tayeb Ghenaiet, Adel EMP Lab Thermal Power Syst Appl Mech Algiers 16111 Algeria

A numerical approach to select an optimized turboprop engine configuration that matches with the power requirement of a class of propeller-driven aircraft, was reported. Biobjective optimizations of the propulsion cycles' parameters for three variants of turboprops were carried out based on an efficient repulsive particle swarm algorithm (RPSO). On each Pareto front, three distinguishable design points were selected. A cycle optimization was performed considering a basic technology level to determine the optimum turbine inlet temperature (TIT), overall pressure ratio (OPR), and turbine temperature expansion rate (TTR) and the subsequent performance for the three configurations of turboprops. The optimizations carried out for the three turboprops' configurations at the same cruising conditions showed that for a basic technology level the two-shaft fixed-turbine turboprop is preferred, owing to better performance for the same OPR range, but needs more LP turbine stages as compared with the free-turbine version.

关键词： Turboprop Engine Evolutionary algorithm Power Specific Fuel Consumption Turbine Inlet Temperature Fuel Consumption Mass Flow Temperature Ratio Numerical Modeling Exhaust Nozzles optimization algorithm

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Numerical Simulation Study on optimization of Freezing Pipe Position under Seepage Conditions Based on Simulated Annealing algorithm

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KSCE JOURNAL OF CIVIL ENGINEERING 2024年第12期28卷 5441-5451页

作者： Song, Renjie Cent South Univ Sch Civil Engn Changsha 410075 Peoples R China

Artificial Ground Freezing (AGF) is a promising method for controlling seepage in permeable strata. However, AGF faces challenges, including difficulties in achieving a frozen barrier in high-flow conditions and concerns about cost-effectiveness. This study optimizes freezing pipe placement in AGF using a simulated annealing algorithm and a coupled hydrothermal finite element model, focusing on AGF system responses under varying seepage velocities. The optimized layout significantly reduces freeze-ring formation time (by 2.5 days) and the overall freezing duration (by 12.5 days). Moreover, it substantially decreases the required frozen soil volume, facilitating drilling and excavation. Across different seepage velocities, the difference in freeze-ring formation time between the optimized and uniform layouts gradually increases with higher seepage velocity, reaching a maximum difference of 5.9 days. Finally, the relationship between freezing time and seepage velocity was quantitatively described using exponential functions. This study underscores the critical role of optimizing freezing pipe placement in AGF, providing a foundation for efficient and cost-effective geotechnical engineering practices.

关键词： Hydrothermal coupling Artificial ground freezing Placement of freezing pipes optimization algorithm Seepage

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An image encryption scheme based on an optimal chaotic map derived by multi-objective optimization using ABC algorithm

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NONLINEAR DYNAMICS 2021年第2期105卷 1885-1909页

作者： Toktas, Abdurrahim Erkan, Ugur Ustun, Deniz Karamanoglu Mehmetbey Univ Fac Engn Dept Elect & Elect Engn TR-70200 Karaman Turkey Karamanoglu Mehmetbey Univ Fac Engn Dept Comp Engn TR-70200 Karaman Turkey Tarsus Univ Fac Engn Dept Comp Engn TR-33400 Tarsus Mersin Turkey

A novel optimal chaotic map (OCM) is proposed for image encryption scheme (IES). The OCM is constructed using a multi-objective optimization strategy through artificial bee colony (ABC) algorithm. An empirical model for the OCM with four unknown variables is first constituted, and then, these variables are optimally found out using ABC for minimizing the multi-objective function composed of the information entropy and Lyapunov exponent (LE) of the OCM. The OCM shows better chaotic attributes in the evaluation analyses using metrics such as bifurcation, 3D phase space, LE, permutation entropy (PE) and sample entropy (SE). The encrypting performance of the OCM is demonstrated on a straightforward IES and verified by various cryptanalyses that compared with many reported studies, as well. The main superiority of the OCM over the studies based on optimization is that it does not require any optimization in the encrypting operation;thus, OCM works standalone in the encryption. However, those reported studies use ciphertext images obtained through encrypting process in every cycle of optimization algorithm, resulting in long processing time. Therefore, the IES with OCS is faster than the others optimization-based IES. Furthermore, the proposed IES with the OCM manifests satisfactory outcomes for the compared results with the literature.

关键词： Image encryption Chaotic map optimization algorithm Multi-objective optimization Artificial bee colony

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Multi-objective optimization of process parameters in stamping based on an improved RBM-BPNN network and MOPSO algorithm

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STRUCTURAL AND MULTIDISCIPLINARY optimization 2021年第6期64卷 4209-4235页

作者： Xie, Yanmin Du, Lingfeng Zhao, Jiangbo Liu, Cheng Li, Wei Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Sichuan Peoples R China

Stamping is the main manufacturing process for sheet metal parts. However, during the stamping process, based on excessive blank holder force, unreasonable mold design, and other factors, it is easy to generate defects such as cracks in the drawing area and flange wrinkles. In this paper, a novel hybrid model based on a restricted Boltzmann machine and back-propagation neural network is proposed and its validity is verified through different testing functions. Additionally, an improved multi-objective particle swarm optimization (MOPSO) method based on a crowding operator is proposed and compared to several powerful existing algorithms. The proposed method was applied to the process optimization of a double-C part. The sensitivity of the forming quality to different process parameters was analyzed and a novel index was used to describe quality changes. A mapping relationship between the process parameters and forming quality was established based on the proposed hybrid model. Furthermore, optimal process parameters were obtained using MOPSO. The results demonstrated that the proposed method significantly reduces flange wrinkles without excessive thinning and improves the uniformity of formed parts.

关键词： Stamping Process parameters Neural network optimization algorithm

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optimization of Red Luminescent Intensity in Eu³⁺-Doped Lanthanide Phosphors Using Genetic algorithm

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ACS BIOMATERIALS SCIENCE & ENGINEERING 2018年第12期4卷 4378-4384页

作者： Lv, Ruichan Xiao, Liyang Jiang, Xue Feng, Miao Yang, Fan Tian, Jie Xidian Univ Sch Life Sci & Technol Minist Educ Engn Res Ctr Mol & Neuro Imaging Xian 710071 Shaanxi Peoples R China Chinese Acad Sci Inst Automat Key Lab Mol Imaging Beijing 100190 Peoples R China

In this research, four steps including synthesis experiment, brightness evaluation, optimized calculation using brightness as fitness reference, and new calculated composition for the next preparation have been proceeded to find the brightest Eu3+ doped phosphors combined with chemical experiments and genetic algorithm (GA) calculation. The evolutionary operations, such as elitism, selection, crossover, and mutation, are applied to the compound combination. Feasible optimized combination would be obtained until the phosphor is found to be satisfactory. Through GA calculation and thd experimental process, the final luminescence enhancement factor of the optimal phosphor is up to 141% compared with the best one in the first generation. Thus, the GA calculation could be well applied to combinatorial chemistry to find the better phosphor. Additionally, the optimized phosphor is potentially applied as the fingerprint detection nanoparticle and dual-modal imaging agent of the CT/luminescent agent with high penetration and resolution.

关键词： Eu3+-doped phosphors optimization algorithm genetic algorithm bioimaging fingerprint detection

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