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Three-Periods optimization algorithm: A New Method for Solving Various optimization Problems

IETE JOURNAL OF RESEARCH 2023年第11期69卷 CXCVII-CCXI页

作者： Dehghani, Mohammad Trojovsky, Pavel Hubalovsky, Stepan Alsenani, Theyab R. Singh, Jaswinder Univ Hradec Kralove Fac Sci Dept Math Hradec Kralove 50003 Czech Republic Univ Hradec Kralove Fac Sci Dept Appl Cybernet Hradec Kralove 50003 Czech Republic Prince Sattam Bin Abdulaziz Univ Elect Engn Dept Al Kharj 11942 Saudi Arabia Punjab Inst Technol Dept Comp Sci & Engn & Comp Applicat Moga 142038 Punjab India

Using appropriate tools to solve optimization problems is a major challenge in various sciences. In this paper, a new optimization algorithm called Three-Period optimization algorithm (TPOA) is designed to provide suitable quasi-optimal solutions for various optimization problems. The main idea in designing the proposed TPOA is to manage the updating of population members during replication of the algorithm. In this way, the total execution time of the algorithm is divided into three periods in which each period follows a specific update process for members of the population. In the first period, a composite member, in the second period, the worst member of the population, and in the third period, the best member of the population is used to update the members of the population. The proposed TPOA is mathematically modeled and implemented on a standard set of objective functions of unimodal and multimodal types. The optimization results indicate the optimal ability of the TPOA to solve various optimization problems and provide appropriate quasi-optimal solutions. To analyze the quality of the proposed TPOA, the results are compared with the performance of eight optimization algorithms, including Particle Swarm optimization (PSO), Teaching-Learning-Based optimization (TLBO), Grey Wolf optimization (GWO), Whale optimization algorithm (WOA), Marine Predators algorithm (MPA), Tunicate Swarm algorithm (TSA), Gravitational Search algorithm (GSA), and Genetic algorithm (GA). Examining the simulation results and comparing the performance of optimization algorithms shows that the proposed TPOA has high power in solving optimization problems and is superior and much more competitive than the eight compared algorithms.

关键词： Best member composite member optimization optimization algorithm optimization problem population-based stochastic method three periods worst member

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DeSGOA: double exponential smoothing gazelle optimization algorithm-based deep learning model for blind source separation

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KNOWLEDGE-BASED SYSTEMS 2024年 305卷

作者： Muhsina, N. Dhoulath, Beegum J. TKM Coll Engn Dept Elect & Commun Engn Kollam 691005 Kerala India APJ Abdul Kalam Technol Univ Thiruvananthapuram Kerala India

Blind Source Separation (BSS) pertains to a scenario, wherein the sources, as the method used for mixing are not known;only the mixed signals are accessible for subsequent separation. In several applications, it is preferable to retrieve all sources from the mixed signal or, at least isolate a specific source. The research proposes a novel approach, named Double Exponential Smoothing Gazelle optimization algorithm-based Generative Adversarial Network (DeSGOA-based GAN), and designed for BSS. The proposed algorithm, DeSGOA combines the power of Double Exponential Smoothing (DES) with the efficiency of the Gazelle optimization algorithm (GOA) to achieve superior results in source separation tasks. The research aims to enhance the accuracy and performance of BSS processes using the presented approach. At first, the mixed input signals attained from the dataset are fed to pre-processing phase. This phase aspires to eradicate noise present in the signal via the application of PCA model. The final objective is to capture a important amount of data information in the reduced dataset. Following this, the BSS process is carried out by utilizing the GAN, which is trained through the innovative DeSGOA algorithm. Experimental outcomes illustrate the efficacy of DeSGOA-based GAN method in achieving high-quality source separation, underscoring its potential as a valuable tool in audio signal processing and related applications. Finally, the experimental evaluation illustrated that the presented strategy gained SDR of 35.05, SIR of 11.94, SAR of 8.247, and ISR of 13.02.

关键词： optimization Deep learning Blind source separation Double exponential smoothing gazelle optimization algorithm Generative adversarial network-neural egg separation Principal component analysis

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A reconfiguration method for photovoltaic array of stratospheric airship based on multilevel optimization algorithm

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APPLIED ENERGY 2023年 352卷

作者： Shan, Chuan Sun, Kangwen Ji, Xinzhe Cheng, Dongji Beihang Univ Sch Aeronaut Sci & Engn 37 Xueyuan Rd Beijing 100191 Peoples R China Beihang Univ Yunnan Innovat Inst 8 Shibo Rd Kunming 650233 Peoples R China

Reducing the mismatch loss to increase the output power of the photovoltaic (PV) array is crucial for extending the flight time of stratospheric airships. This paper presents a reconfiguration system for PV arrays based on a switch matrix designed for stratospheric airships. The proposed system employs a multilevel optimization reconfiguration algorithm that combines smart choice, greedy, and Munkres' assignment algorithms. Simulations were conducted under single working conditions, full-day sunlight cycles, and full-year PV array reconfigurations, respectively. The results demonstrated that the reconfigured PV array significantly improved the output power with a smooth P-V curve. The instantaneous power under extreme working conditions could be increased by 50.1%. Furthermore, during the 7-day simulation process, the average daily power output of the PV array increased by 14.68%, whereas the output fluctuation during circular cruising was reduced. The reconfiguration system offers greater advantages during months with weak irradiance in high-latitude regions, where the daily output power of the PV array can be increased by up to 24.46%. This significantly reduces the installation area and weight ratio of a stratospheric airship PV array.

关键词： Photovoltaic array Stratospheric airship Reconfiguration optimization algorithm Mismatch loss Energy improvement

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Protective Project Decision Model Based on Critic-Topsis and optimization algorithm

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Procedia Computer Science 2024年 247卷 191-200页

作者： Zhengrong Liang Congcong Wu Jinkang Li College of Physics and Communication Electronics Jiangxi Normal University Nanchang China

This paper deals with one of the dire needs of wildlife conservation: effective decision-making frameworks. Growing global connectivity and economic development result in growing demand for certain wildlife products, causing enormous ecological damage and losses in biodiversity. This three-dimensional model includes evaluation based on power, resources, and interest for finding the best partners for conservation projects using the Critic-Topsis model combined with optimization algorithms. The model is then applied using a case study of the United States' terrestrial and inland water reserves. Data used in the study were sourced from 21 countries and organizations identifying key stakeholders like governments, specifically that of the US. The research showed that successful conservation would only be reached by cooperation between governments, NGOs, and other stakeholders that need to collaborate on the same platform. The findings show that strategic and data-driven approaches can make a huge difference in enhancing the impact of wildlife protection initiatives. Conclude that further research into and adaptation of these models must be continued to respond to the ever-changing challenges that surround wildlife conversation, sustainable development, and ecological balance. This forms a very strong foundation for future planning and implementation of the conservation plan.

关键词： Protective Project Critic-Topsis optimization algorithm Data-Driven Conservation Plan

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Fruit fly optimization algorithm based on a hybrid adaptive-cooperative learning and its application in multilevel image thresholding

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APPLIED SOFT COMPUTING 2019年 84卷 105704-000页

作者： Ding, Guoshen Dong, Fengzhong Zou, Hai Chinese Acad Sci Anhui Inst Opt & Fine Mech Anhui Prov Key Lab Photon Devices & Mat Hefei 230031 Anhui Peoples R China Univ Sci & Technol China Hefei 230061 Anhui Peoples R China Anhui Univ Sch Comp Sci & Technol Hefei 230601 Anhui Peoples R China

Multilevel thresholding is widely exploited in image processing, however, most of the techniques are time-consuming. In this paper, we present a novel approach, multilevel thresholding with fruit fly optimization algorithm (FOA). As yet, FOA has not been applied to resolve the complex image processing problems. Nevertheless, the merits of FOA were validated in former research, which include few parameters, simple structure, easy to understand and implement. Here, we introduce it into the study of multi-threshold image processing area. Moreover, we incorporate a hybrid adaptive-cooperative learning strategy with the proposed method called HACLFOA. The fruit fly population is divided into two sub-populations and both of them have a different iteration step range. In addition, each dimension of the solution vector will be optimized during one search, and we also make the best of the temporary global optimum information. The results of computational experiments on 24 benchmark functions demonstrate that the proposed algorithm has superior global convergence ability against other algorithms. Most significantly, extensive results show that the proposed algorithm is time-saving in multilevel image thresholding, and that it has great potential in the image processing field. (C) 2019 Elsevier B.V. All rights reserved.

关键词： optimization algorithm Fruit fly optimization algorithm (FOA) Hybrid adaptive-cooperative learning method Multilevel image thresholding

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The influence of optimization algorithm on the signal prediction accuracy of VMD-LSTM for the pumped storage hydropower unit

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JOURNAL OF ENERGY STORAGE 2024年 78卷

作者： Fang, Mingkun Zhang, Fangfang Yang, Yang Tao, Ran Xiao, Ruofu Zhu, Di China Agr Univ Coll Water Resources & Civil Engn Beijing 100083 Peoples R China Yangzhou Univ Coll Hydraul Sci & Engn Yangzhou 225009 Jiangsu Peoples R China Yangzhou Univ Engn Res Ctr High Efficiency & Energy Saving Large Yangzhou 225009 Jiangsu Peoples R China China Agr Univ Beijing Engn Res Ctr Safety & Energy Saving Techno Beijing 100083 Peoples R China China Agr Univ Coll Engn Beijing 100083 Peoples R China

Pumped storage hydropower units, as a crucial type of energy conversion equipment, play a pivotal role in energy storage and balance. Predicting pressure pulsation signals in pump-turbine units can help anticipate potential unit malfunctions within the pump-turbine system. However, conventional prediction methods may be constrained by their ability to extract signal features and perform pattern recognition. With the continuous advancement in the realm of artificial intelligence, there has been a rise in the evolution of prediction involving variational mode decomposition (VMD) for time series decomposition and the utilization of genetic algorithms (GAs) for parameter optimization of long short-term memory (LSTM) networks has emerged. To address the fitness function issue in the context of genetic algorithms, this study employs three optimization algorithms to combine with the VMD-LSTM model. Three different prediction models are formed and compared with both the conventional LSTM and VMD-LSTM prediction methods. The results indicate that, in the prediction of pressure pulsation data at the three monitoring points of the pump-turbine, the predictive performance of the VMD-whale optimization algorithm (WOA)-LSTM model surpasses that of the conventional LSTM model and the VMD-LSTM prediction approach. Furthermore, the magnitude of prediction errors is reduced in comparison to the VMDsparrow search algorithm (SSA)-LSTM and VMD-improved grey wolf optimization (IGWO)-LSTM prediction models.

关键词： optimization algorithm Variational mode decomposition Long short-term memory Pump-turbine

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An innovative ensemble model based on multiple neural networks and a novel heuristic optimization algorithm for COVID-19 forecasting

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EXPERT SYSTEMS WITH APPLICATIONS 2023年 212卷 118746页

作者： Qu, Zongxi Li, Yutong Jiang, Xia Niu, Chunhua Lanzhou Univ Sch Management Lanzhou 730000 Peoples R China Lanzhou Univ Res Ctr Emergency Management Lanzhou 730000 Peoples R China Northwest Minzu Univ Affiliated Hosp Prov Peoples Hosp Gansu 2 Lanzhou 730099 Peoples R China

During the global fight against the novel coronavirus pneumonia (COVID-19) epidemic, accurate outbreak trend forecasting has become vital for outbreak prevention and control. Effective COVID-19 outbreak trend prediction remains a complex and challenging issue owing to the significant fluctuations in the COVID-19 data series. Most previous studies have limitations only using individual forecasting methods for outbreak modeling, ignoring the combination of the advantages of different prediction methods, which may lead to insufficient results. Therefore, this paper develops a novel ensemble paradigm based on multiple neural networks and a novel heuristic opti-mization algorithm. First, a new hybrid sine cosine algorithm-whale optimization algorithm (SCWOA) is exer-cised on 15 benchmark tests. Second, four neural networks are used as predictors for the COVID-19 outbreak forecasting. Each predictor is given a weight, and the proposed SCWOA is used to optimize the best matching weights of the ensemble model. The daily COVID-19 series collected from three of the most-affected countries were taken as the test cases. The experimental results demonstrate that different neural network models have different performances in various complex epidemic prediction scenarios. The SCWOA-based ensemble model can outperform all comparable models with its high accuracy and robustness.

关键词： COVID-19 forecasting Ensemble forecasting Neural network optimization algorithm

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A particle swarm optimization algorithm with novelty search for combustion systems with ultra-low emissions and minimum fuel consumption

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APPLIED SOFT COMPUTING 2023年 143卷

作者： Martinez-Rodriguez, David Novella, Ricardo Bracho, Gabriela Gomez-Soriano, Josep Fernandes, Cassio Lucchini, Tommaso Della Torre, Augusto Villanueva, Rafael-J. Hidalgo, J. Ignacio Univ Politecn Valencia Inst Univ Matemat Multidisciplinar Algiros Spain Univ Politecn Valencia Ctr Motores Term Algiros Spain Politecn Milan Milan Italy Univ Complutense Madrid Dept Comp Arquitecture Madrid Spain Univ Complutense Madrid Inst Tecnol Conocimiento Madrid Spain Univ Politecn Valencia CMT Motores Term E-46022 Valencia Spain

The particle swarm optimization algorithm is primarily inspired by the natural behaviour of swarms and achieves important results in different applications. However, it is not exempt from stagnation in local optima and has a tendency to prematurely converge to them. Novelty Search is a concept that appeared recently in different fields of computational intelligence. It aims at exploring non-visited areas of the search space through solutions that bring novelty to already discovered solutions. The novelty of this work can be divided into two steps: on one side, this article proposes a variant of the particle swarm optimization algorithm which uses Novelty Search concepts to improve the algorithm's performance. Our proposal is first checked and compared using the CEC 2005 benchmark suite and then, we apply it to solve a real-world optimization problem: the design of a combustion system targeting the reduction of pollutant emissions and fuel consumption. The combustion chamber design phase usually is a complex and time-consuming process even with advanced supercomputers, since it depends on several input variables which are highly non-linear and with crossed interaction. Then, the second contribution of this work is to develop a methodology that couples a computational fluid dynamics (CFD) simulation tool with the new optimization algorithm for minimizing the specific fuel consumption of a compression-ignited engine, while constraining the NOx and soot emissions. A 3D-CFD model of the combustion system was built to predict and analyse the performance of the combustion system and hence, select the parameters with a higher impact on the system. The method reduces the computational time and includes tools for the automatic preparation of the input parameters and geometry of the system. The input parameters correspond to geometrical variables that control the bowl shape, the number of holes in the injector, the injection pressure, the swirl number and the exhaust gas recir

关键词： PSO NS optimization algorithm CI engine Combustion Emissions Efficiency

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Enhanced design parameters of a cylindrical helical compression spring using an optimization algorithm and a non-linear behavior model

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MECHANICS BASED DESIGN OF STRUCTURES AND MACHINES 2024年第12期52卷 9609-9620页

作者： Cadet, Guillaume Paredes, Manuel Univ Toulouse ICA CNRS UMR 5312 INSA ISAE SUPAEROMINES-ALBIUPS Toulouse France Univ Toulouse ICA UPS INSAISAE SUPAEROMINES ALBICNRS 3 rue Caroline Aigle F-31400 Toulouse France

An optimization algorithm based on SLSQP solver was created to design helical compression springs. The goal is to reach the required behavior even when the standards formula is inaccurate. Indeed, it was already shown in previous works that the classic formula determining the global load-length behavior of the spring is not always accurate enough because it does not consider the effects of the spring's ends, in particular for springs with low index and low number of coils. Based on a new tri-linear model, this algorithm finds optimized spring design which respects the required operative points. In order to test the algorithm, the first tests were carried out to optimize the design of a PLA 3D-printed spring. It was shown that the initial design stiffness is overestimated by over 28% by the formula extracted from the standards. Then, the design of the spring was implemented in the algorithm to optimize the pitch and the number of active coils. The experimental curve of the optimized design spring goes through the target behavior, with a non-linearity at the beginning of the deflection. The experimental error about the final stiffness is successfully reduced to 1.1%. It is hoped that this work will provide a valuable progress to assist engineers and manufacturers in helical spring design.

关键词： optimization algorithm 3D printing manufacturing helical spring

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An online optimization algorithm for the real-time quantum state tomography

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QUANTUM INFORMATION PROCESSING 2020年第10期19卷 361-361页

作者： Zhang, Kun Cong, Shuang Li, Kezhi Wang, Tao Univ Sci & Technol China Dept Automat Hefei 230027 Peoples R China UCL 222 Euston Rd London NW1 2DA England

Considering the presence of measurement noise in the continuous weak measurement process, the optimization problem of online quantum state tomography (QST) with corresponding constraints is formulated. Based on the online alternating direction multiplier method (OADM) and the continuous weak measurement (CWM), an online QST algorithm (QST-OADM) is designed and derived. Specifically, the online QST problem is divided into two subproblems about the quantum state and the measurement noise. The proposed algorithm adopts adaptive learning rate and reduces the computational complexity to O(d(3)), which provides a more efficient mechanism for real-time quantum state tomography. Compared with most existing algorithms of online QST based on CWM which require time-consuming iterations in each estimation, the proposed QST-OADM can exactly solve two subproblems at each sampling. The merits of the proposed algorithm are demonstrated in the numerical experiments of online QST for 1-, 2-, 3-, and 4-qubit systems.

关键词： Online quantum state tomography optimization algorithm Online alternating direction multiplier method Continuous weak measurement

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