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Student psychology based optimization algorithm: A new population based optimization algorithm for solving optimization problems

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ADVANCES IN ENGINEERING SOFTWARE 2020年 146卷 102804-102804页

作者： Das, Bikash Mukherjee, V. Das, Debapriya Govt Coll Engn & Textile Technol Dept Elect Engn Berhampur W Bengal India Indian Sch Mines Indian Inst Technol Dept Elect Engn Dhanbad Bihar India Indian Inst Technol Dept Elect Engn Kharagpur W Bengal India

In this article, a new metaheuristic optimization algorithm (named as, student psychology based optimization (SPBO)) is proposed. The proposed SPBO algorithm is based on the psychology of the students who are trying to give more effort to improve their performance in the examination up to the level for becoming the best student in the class. Performance of the proposed SPBO is analyzed while applying the algorithm to solve thirteen 50 dimensional benchmark functions as well as fifteen CEC 2015 benchmark problems. Results of the SPBO is compared to the performance of ten other state-of-the-art optimization algorithms such as particle swarm optimization, teaching learning based optimization, cuckoo search algorithm, symbiotic organism search, covariant matrix adaptation with evolution strategy, success-history based adaptive differential evolution, grey wolf optimization, butterfly optimization algorithm, poor and rich optimization algorithm, and barnacles mating optimizer. For fair analysis, performances of all these algorithms are analyzed based on the optimum results obtained as well as based on convergence mobility of the objective function. Pairwise and multiple comparisons are performed to analyze the statistical performance of the proposed method. From this study, it may be established that the proposed SPBO works very well in all the studied test cases and it is able to obtain an optimum solution with faster convergence mobility.

关键词： Benchmark function CEC 2015 Global optimum solution optimization algorithm Student psychology based optimization (SPBO)

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Identification procedure of concrete constitutive model with optimization algorithm

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Jianzhu Jiegou Xuebao/Journal of Building Structures 2006年第SUPPL.期27卷 41-43+49页

作者： Li, Shouju Shangguan, Zichang Zhao, Jiazhen State Key Lab. of Structural Analysis for Ind. Equip Dalian University of Technology Dalian 116023 China School of Civil and Hydraul. Eng Dalian University of Technology Dalian 116023 China Institute of Civil Engineering Dalian Fishery University Dalian 116023 China

Identification procedure of concrete constitutive model is proposed according to measured system dynamic responses. The inverse problem of material characterization is formulated as parameter identification. A set of parameters corresponding to the material property can be found by minimizing objective function which accounts for experimental data and the calculated response of the mechanical model. The performances of the proposed optimization algorithm were investigated with simulating data, and the effectiveness was consequently confirmed. Numerical simulation results show that the proposed identification has good robustness and high estimation precision.

关键词： Concrete Elastic-plastic constitutive relation optimization algorithm Parameter identification System response

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A technique for designing nano-scale circuits using a fuzzy logic and nature-inspired fish swarm optimization algorithm

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OPTIK 2022年 268卷

作者： Zhang, Kaifei Zhang, Jufang Lvliang Univ Dept Comp Sci & Technol Lvliang 033000 Shanxi Peoples R China Shanxi Inst Energy Jinzhong 030600 Shanxi Peoples R China

Recent breakthroughs in quantum processing software and hardware have shown substantial development toward operational results. We anticipate quantum computing to have significant applications in some domains ranging from machine learning and optimization to drug development in the future. Quantum computing and designing have gotten a lot of interest from investigators. Physical design is one of the two main stages in designing nano-scale circuits that receive a list of circuit nodes as input and produce the final arrangement in a particular technology. However, it has encountered several design issues and execution limits in the long term. Moreover, designing nano-scale circuits is an NP-hard problem in terms of cost, delay, and energy consumption. This paper has provided a new method for solving the optimal design of nano-scale circuits using a fuzzy-based Fish Swarm algorithm (FSA). The FSA represents fish, hunting, and fish behavior to get the best results. It has a quick integration speed, a powerful global search capacity, and a significant shortcoming. In our proposed method, the fuzzy observer is responsible for detecting the system's state within the defined area and maintaining the state within it. The proposed method has been evaluated against some benchmarks, and cost, delay, energy, and area characteristics have been reduced. Simulation outcomes demonstrated the suggested method's correct performance in terms of "energy," "cost", "area", and "delay" where the suggested technique outperforms others (GA, PSO, FSA and ACO).

关键词： Quantum circuits Nano-scale Designing Fuzzy Fish swarm optimization optimization algorithm

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Teamwork optimization algorithm: A New optimization Approach for Function Minimization/Maximization

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SENSORS 2021年第13期21卷 4567-4567页

作者： Dehghani, Mohammad Trojovsky, Pavel Shiraz Univ Technol Dept Elect & Elect Engn Shiraz *** Iran Univ Hradec Kralove Fac Sci Dept Math Hradec Kralove 50003 Czech Republic

Population-based optimization algorithms are one of the most widely used and popular methods in solving optimization problems. In this paper, a new population-based optimization algorithm called the Teamwork optimization algorithm (TOA) is presented to solve various optimization problems. The main idea in designing the TOA is to simulate the teamwork behaviors of the members of a team in order to achieve their desired goal. The TOA is mathematically modeled for usability in solving optimization problems. The capability of the TOA in solving optimization problems is evaluated on a set of twenty-three standard objective functions. Additionally, the performance of the proposed TOA is compared with eight well-known optimization algorithms in providing a suitable quasi-optimal solution. The results of optimization of objective functions indicate the ability of the TOA to solve various optimization problems. Analysis and comparison of the simulation results of the optimization algorithms show that the proposed TOA is superior and far more competitive than the eight compared algorithms.

关键词： optimization optimization algorithm optimization problem population-based teamwork

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Group Intelligent optimization algorithm and Its Evaluation

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AGRO FOOD INDUSTRY HI-TECH 2017年第1期28卷 1084-1088页

作者： Feng, Chen Shihezi Univ Coll Informat Sci & Technol Shihezi 832003 Peoples R China

With the rapid development of economy and science and technology in our country, the types and quantities of various optimization problems have increased in large scale, and the optimization of the model structure can improve the efficiency. Based on this, the group intelligent optimization algorithm and its evaluation were studied. The intelligent homogenization partition was introduced first, and then the calculation of the cluster intelligent alignment probabilities was introduced, and the optimization process of the last group intelligent optimization algorithm was described in detail. The test results show that the optimization model of group intelligent optimization algorithm has more realistic and reasonable application.

关键词： Group intelligence optimization algorithm evaluation fuzzy optimization

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Study on a Hybrid Hydrological Forecasting Model SCE-GUH by Coupling SCE-UA optimization algorithm and General Unit Hydrograph

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WATER 2023年第15期15卷 2783-2783页

作者： Xu, Yingying Liu, Chengshuai Yu, Qiying Zhao, Chenchen Quan, Liyu Hu, Caihong Zhengzhou Univ Yellow River Lab Zhengzhou 450001 Peoples R China

Implementing real-time prediction and warning systems is an effective approach for mitigating flash flood disasters. However, there is still a challenge in improving the accuracy and reliability of flood prediction models. This study develops a hydrological prediction model named SCE-GUH, which combines the Shuffled Complex Evolution-University of Arizona optimization algorithm with the general unit hydrograph routing method. Our aims were to investigate the applicability of the general unit hydrograph in runoff calculations and its performance in predicting flash flood events. Furthermore, we examined the influence of parameter variations in the general unit hydrograph on flood simulations and conducted a comparative analysis with the conventional Nash unit hydrograph. The research findings demonstrate that the utilization of the general unit hydrograph method can considerably decrease computational errors and enhance prediction accuracy. The flood peak detection rate was found to be 100% in all four study watersheds. The average Nash-Sutcliffe efficiency coefficients were 0.83, 0.83, 0.84, and 0.87, while the corresponding coefficients of determination were 0.86, 0.85, 0.86, and 0.94, and the absolute errors of peak present time were 0.19 h, 0.40 h, 0.91 h, and 0.82 h, respectively. Moreover, the utilization of the general unit hydrograph method was found to significantly reduce the peak-to-current time difference, thereby enhancing simulation accuracy. Parameter variations have a substantial influence on peak flow characteristics. The SCE-GUH model, which incorporates the topographic and geomorphological features of the watershed along with the optimization algorithm, is capable of effectively characterizing the catchment properties of the watershed and offers valuable insights for enhancing the early warning and prediction of hydrological forecasting.

关键词： general unit hydrograph rainfall-runoff relationship optimization algorithm flash flood simulation application test surface confluence

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A new optimization algorithm inspired by the quest for the evolution of human society: Human felicity algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2022年第0期193卷 116468-116468页

作者： Kazemi, Mohammad Verij Veysari, Elham Fazeli Inst Higher Educ Pooyandegan Danesh Chalous Mazandaran Iran Islamic Azad Univ Fac Management & Accounting Tonekabon Branch Tonkabon Mazandaran Iran

The present paper introduces a new evolutionary algorithm to solve optimization issues called the human felicity algorithm (HFA). The main idea of the HFA has been inspired the efforts of human society to become felicity. Because the change in human felicity is possible, only through a change in human thought, in the proposed algorithm, the objective function is human felicity in society and the search space optimization is the human thought in society. Like other optimization algorithms, the human felicity algorithm starts solving the problem by an initial random population. Then the population is divided into three categories of elites, disciples, and ordinary people. Afterward, people in human society change their thoughts by 3 ways: 1- being influenced by elites 2- personal experiences 3- sudden and drastic changes of thoughts for various reasons, such as war and famine. To test the capability of the proposed algorithm, the standard test functions of IEEE (Institute of Electrical and Electronics Engineers) Congress on Evolutionary Computation (CEC) 2014, CEC 2019, and CEC2020 with different dimensions have been used. In addition, to evaluate the ability of the proposed algorithm to solve various real-world complex engineering problems, 4 standard classic engineering optimization problems are examined and compared. To test the ability of the proposed algorithm in hyper-parameters tuning, the fivedatabase classification was simulated using the Support Vector Machines (SVM). The simulation of the proposed algorithm on different test functions shows its high ability to solve complex problems with many local optimal points. For statistical analysis, powerful nonparametric methods such as Friedman and Nemenyi postHoc test for statistical analysis have been used. Comparing the HFA with some other optimization algorithms demonstrates its better performance in solving complicated multidimensional problems with the lower iteration.

关键词： Elite Disciple Human felicity optimization algorithm Revolution

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Dynamic performance improvement of oscillating water column wave energy conversion system using optimal walrus optimization algorithm-based control strategy

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AIN SHAMS ENGINEERING JOURNAL 2024年第12期15卷

作者： Eldemery, Habiba A. Hasanien, Hany M. Alharbi, Mohammed Sun, Chuanyu Zaky, Dina A. Ain Shams Univ Fac Engn Elect Power & Machines Dept Cairo 11517 Egypt Future Univ Egypt Fac Engn & Technol Cairo 11835 Egypt King Saud Univ Coll Engn Elect Engn Dept Riyadh 11421 Saudi Arabia Harbin Inst Technol Sch Elect Engn & Automat Harbin 150006 Peoples R China Higher Inst Engn & Technol Elect Power Engn Dept Cairo Egypt

The optimal design of the proportional-integral (PI) controller using the walrus optimization algorithm (WOA) with the aim to enhance the dynamic performance of a grid-connected wave energy conversion (WEC) system when subjected to diverse operational conditions is presented in this paper. The proposed system under study consists of an oscillating water column (OWC) device coupled with a permanent magnet synchronous generator (PMSG) to supply power to the grid. Power electronics devices in the form of a generator-side converter (GSC) and a grid-side inverter (GSI) are used to couple the grid and WEC systems. The GSC is used to minimize generator losses and maximize generator real power through the control of d-axis and q-axis currents (id, and iq) of the PMSG. The GSI is used to control the point of common coupling (PCC) and DC-link voltages (VPCC,VDC), respectively. The PI controllers, used to minimize the error between the actual current and voltage values with their respective reference values, are optimally designed using the WOA. The fitness function of the optimization problem is based on the integral square error criterion (ISE). Presented in this paper is a model for the OWC-WEC system and a control strategy to maximize generated power, minimize generator losses, and keep the VDC, V PCC at required values, the usage of WOA to design the PI controllers, and the simulations of system results. The proposed WOA-based PI controller design's effectiveness is evaluated by comparing its simulation results with that obtained from using genetic algorithm (GA), grey wolf (GWO), particle swarm (PWO), and harmony search (HS) optimization-based PI controllers under symmetrical and unsymmetrical faults. The proposed strategy shows an enhancement in the dynamic performance of OWC wave energy systems when compared to the other optimization algorithm-based PI controllers, as well as achieving the least value for ISE, which reached 0.172.

关键词： optimization algorithm Walrus optimization algorithm Oscillating water column Renewable energy system Wave energy

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Development of Deer Hunting linked Earthworm optimization algorithm for solving large scale Traveling Salesman Problem

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KNOWLEDGE-BASED SYSTEMS 2021年 227卷 107199-107199页

作者： Kanna, S. K. Rajesh Sivakumar, K. Lingaraj, N. Rajalakshmi Inst Technol Dept Mech Engn Chennai Tamil Nadu India Saveetha Sch Engn Dept Mech Engn Chennai Tamil Nadu India

Traveling Salesman Problem (TSP) has been seen in diverse applications, which is proven to be NP-complete in most cases. Even though there are multiple heuristic techniques, the problem is still a complex combinatorial optimization problem. The candidate solutions are chosen by considering only a set of high values of the objective function which may not lead to the best solutions. Hence, this paper develops a hybrid optimization algorithm, named Earthworm-based DHOA (EW-DHOA) to solve the TSP problem by finding an optimal solution. The proposed EW-DHOA is developed by integrating the two well-performing meta-heuristic algorithms, such as Deer Hunting optimization algorithm (DHOA) and Earthworm optimization algorithm (EWA). The EW-DHOA intends to optimize the constraint as the number of cities traveled by the salesman in terms of an optimal path. The main process for attaining this objective is to minimize the distance traveled by the salesman concerning the entire cities. The effectiveness of the proposed hybrid meta-heuristic algorithm is validated over the benchmark dataset. Finally, the experimental results show that the convergence of the proposed hybrid optimization will be better while solving TSP with less computational complexity, and improved significantly in attaining optimal results. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Meta-heuristic algorithm Hybridization Traveling Salesman Problem Minimized Traveling Distance Earthworm-based Deer Hunting optimization algorithm

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Feature Selection and SVM Parameter Synchronous optimization Based on a Hybrid Intelligent optimization algorithm

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HUMAN-CENTRIC COMPUTING AND INFORMATION SCIENCES 2023年 13卷

作者： Wang, Qingjun Mu, Zhendong Shenyang Aerosp Univ Coll Econom & Management Shenyang Peoples R China Nanjing Univ Aeronut & Astronaut Nanjing Peoples R China Jiangxi Univ Technol Ctr Collaborat & Innovat Nanchang Peoples R China

Amid the rapid development of science and technology and the increasingly fierce market competition, low cost coupled with high performance is the key to a company's competitiveness. This requires optimizing all the links in the actual output. Hybrid intelligent algorithms can combine the advantages of different algorithms to solve a large number of optimization problems in engineering practice. Therefore, this study is focused on a hybrid swarm intelligence algorithm and its application. As a classic method of machine learning, the kernel function, which is based on the support vector machine (SVM) and the selection of the parameters in the kernel function, has an important influence on the performance of the classifier. The use of kernel function technology cannot only greatly reduce the amount of calculation in the input space, but can also effectively improve machine learning classification performance. In the field of machine learning, choosing and building the core functions is a notable difficulty. However, little research has been conducted in this area so far. In view of the above problems, this study discusses and analyzes the structure of the support frame machine core in detail, and improves the traditional parameter optimization algorithm. It also proposes a new method of fuzzy clustering algorithm automatic parameter learning combined with the basic ideas of a genetic algorithm in order to improve the parameter optimization strategy of support vector regression, so as to obtain better prediction results. Through simulation experiments, the improved hybrid core SVM and parameter optimization algorithm were applied to the ORL face database, greatly improving the recognition rate, and experiments were carried out after adding noise to the images in the face database to verify the practicability and practicality of the algorithm. The robustness and reliability of the algorithm were improved by at least 30%, thus confirming the feasibility of the proposed a

关键词： Hybrid Intelligence optimization algorithm Feature Selection Parameter Synchronization optimization

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