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Stochastic Fractal Search Algorithm for Template Matching with Lateral Inhibition

SCIENTIFIC PROGRAMMING 2017年第Pt.2期2017卷 1-14页

作者： Luo, Qifang Zhang, Sen Zhou, Yongquan Guangxi Univ Nationalities Coll Informat Sci & Engn Nanning 530006 Peoples R China Guangxi High Sch Key Lab Complex Syst & Computat Nanning 530006 Peoples R China

Template matching is a basic and crucial process for image processing. In this paper, a hybrid method of stochastic fractal search (SFS) and lateral inhibition (LI) is proposed to solve complicated template matching problems. The proposed template matching technique is called LI-SFS. SFS is a new metaheuristic algorithm inspired by random fractals. Furthermore, lateral inhibition mechanism has been verified to have good effects on image edge extraction and image enhancement. In this work, lateral inhibition is employed for image preprocessing. LI-SFS takes both the advantages of SFS and lateral inhibition which leads to better performance. Our simulation results show that LI-SFS is more effective and robust for this template matching mission than other algorithms based on LI.

关键词： TEMPLATE matching (Digital image processing) FRACTALS metaheuristic algorithms IMAGE enhancement (Imaging systems) DIGITAL image processing

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Dragonfly Algorithm-Based Optimization for Selective Harmonics Elimination in Cascaded H-Bridge Multilevel Inverters with Statistical Comparison

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ENERGIES 2022年第18期15卷 6826页

作者： Tariq, Muhammad Ayyaz Shami, Umar Tabrez Fakhar, Muhammad Salman Kashif, Syed Abdul Rahman Abbas, Ghulam Ullah, Nasim Mohammad, Alsharef Farrag, Mohamed Emad Univ Engn & Technol Dept Elect Engn Lahore 54890 Pakistan Univ Lahore Dept Elect Engn Lahore 54000 Pakistan Taif Univ Coll Engn Dept Elect Engn Taif 21944 Saudi Arabia Glasgow Caledonian Univ Sch Comp Engn & Built Environm C011 70 Cowcaddens Rd Glasgow G4 0BA Lanark Scotland

Harmonics worsen the quality of electrical signals, hence, there is a need to eliminate them. The test objects under discussion are single-phase versions of cascaded H-bridge (CHB) multilevel inverters (MLIs) whose switching angles are optimized to eliminate specific harmonics. The Dragonfly Algorithm (DA) is used to eradicate low-order harmonics, and its statistical performance is compared to that of many other optimization techniques, including Particle Swarm Optimization (PSO), Accelerated Particle Swarm Optimization (APSO), Differential Evolution (DE), and Grey Wolf Optimization (GWO). Various scenarios of the algorithms' search agent population for inverters with seven, nine, and eleven levels of output voltages are comprehensively addressed in this research. No algorithm shows total dominance in every scenario. The DA is least impacted by the change in dimensions of the narrated problem.

关键词： selective harmonics elimination (SHE) cascaded H-bridge (CHB) multilevel inverter (MLI) switching angles metaheuristic algorithms dragonfly algorithm (DA)

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An advanced RIME optimizer with random reselection and Powell mechanism for engineering design

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2024年第6期11卷 139-179页

作者： Xu, Shiqi Jiang, Wei Chen, Yi Heidari, Ali Asghar Liu, Lei Chen, Huiling Liang, Guoxi Wenzhou Univ Inst Big Data & Informat Technol Wenzhou 325000 Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran *** Iran Sichuan Univ Coll Comp Sci Chengdu 610065 Sichuan Peoples R China Wenzhou Polytech Dept Artificial Intelligence Wenzhou 325035 Peoples R China

RIME is a recently introduced optimization algorithm that draws inspiration from natural phenomena. However, RIME has certain limitations. For example, it is prone to falling into Local Optima, thus failing to find the Global Optima, and has the problem of slow convergence. To solve these problems, this paper introduces an improved RIME algorithm (PCRIME), which combines the random reselection strategy and the Powell mechanism. The random reselection strategy enhances population diversity and helps to escape Local Optima, while the Powell mechanism helps to improve the convergence accuracy and thus find the optimal solution. To verify the superior performance of PCRIME, we conducted a series of experiments at CEC 2017 and CEC 2022, including qualitative analysis, ablation studies, parameter sensitivity analysis, and comparison with various advanced algorithms. We used the Wilcoxon signed- rank test and the Friedman test to confirm the performance advantage of PCRIME over its peers. The experimental data show that PCRIME has superior optimization ability and robustness. Finally, this paper applies PCRIME to five real engineering problems and proposes feasible solutions and comprehensive performance index definitions for these five problems to prove the stability of the proposed algorithm. The results show that the PCRIME algorithm can not only effectively solve practical problems, but also has excellent stability, making it an excellent algorithm.

关键词： RIME random reselection strategy Powell mechanism engineering optimization metaheuristic algorithms

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Hybrid and enhanced PSO: Novel first order reliability method-based hybrid intelligent approaches

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2022年 393卷 114730-114730页

作者： Shun-Peng Zhu Keshtegar, Behrooz Mohamed El Amine Ben Seghier Zio, Enrico Taylan, Osman Univ Elect Sci & Technol China Sch Mech & Elect Engn Chengdu 611731 Peoples R China Univ Zabol Fac Engn Dept Civil Engn PB *** Zabol Iran Ton Duc Thang Univ Inst Computat Sci Div Computat Math & Engn Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Civil Engn Ho Chi Minh City Vietnam PSL Res Univ Ctr Rech Sur Risques & Crises Mines ParisTech Sophia Antipolis France Politecn Milan Energy Dept Via La Masa 34 I-20156 Milan Italy Kyung Hee Univ Dept Nucl Engn 1732 Deogyeong Daero Yongin 17104 Gyeonggi Do South Korea King Abdulaziz Univ Fac Engn Dept Ind Engn POB 80204 Jeddah Saudi Arabia

Computing the sensitivity vector in the traditional first order reliability method may provide inaccurate reliability outcomes for discrete performance functions and inefficient computation burden for high-dimensional problems. In this study, two improved particle swarm optimization algorithms are proposed to enhance the convergence rate with global optimal results during the structural reliability analysis. The abilities for convergence speed and global convergence of the particle swarm optimization algorithm are improved using a novel hybrid method called particle swarm optimization-based harmony search algorithm (PSO-HS), and enhanced particle swarm optimization (EPSO). The proposed methods use a dynamic self-adaptive term to execute the local adjusting process. Using twelve numerical-based engineering problems, the structural reliability frameworks developed based on modified versions of particle swarm optimization algorithms are compared to numerous FORM algorithms and the current metaheuristic methods. Results indicated that the novel proposed methods using the improved PSO algorithms are more robust and efficient than the analytical FORM methods for solving high-dimensional engineering problems. Furthermore, compared to the previous metaheuristic approaches, the suggested methods enabled faster convergence. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Structural reliability analysis First order reliability method Enhanced particle swarm optimization Hybrid method metaheuristic algorithms

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A state-of-the-art differential evolution algorithm for parameter estimation of solar photovoltaic models

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ENERGY CONVERSION AND MANAGEMENT 2021年 230卷 113784-113784页

作者： Gao, Shangce Wang, Kaiyu Tao, Sichen Jin, Ting Dai, Hongwei Cheng, Jiujun Univ Toyama Fac Engn Toyama 9308555 Japan Nanjing Forestry Univ Sch Sci Nanjing Peoples R China Jiangsu Ocean Univ Sch Comp Engn Lianyugang 222005 Peoples R China Tongji Univ Key Lab Embedded Syst & Serv Comp Minist Educ Shanghai 200092 Peoples R China

Photovoltaic (PV) generation systems are vital to the utilization of the sustainable and pollution-free solar energy. However, the parameter estimation of PV systems remains very challenging due to its inherent nonlinear, multi-variable, and multi-modal characteristics. In this paper, we propose a state-of-the-art optimization method, namely, directional permutation differential evolution algorithm (DPDE), to tackle the parameter estimation of several kinds of solar PV models. By fully utilizing the information arisen from the search population and the direction of differential vectors, DPDE can possess a strong global exploration ability of jumping out of the local optima. To verify the performance of DPDE, six groups of experiments based on single, double, triple diode models and PV module models are conducted. Extensive comparative results between DPDE and other fifteen representative algorithms show that DPDE outperforms its peers in terms of the solution accuracy. Additionally, statistical results based on Wilcoxon rank-sum and Friedman tests indicate that DPDE is the most robust and best performing algorithm for the parameter estimation of PV systems.

关键词： Photovoltaic cell Parameter estimation Optimization methods Differential evolution metaheuristic algorithms Computational intelligence

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Solving the Manufacturing Cell Design Problem through Binary Cat Swarm Optimization with Dynamic Mixture Ratios

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COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2019年第1期2019卷 4787856-4787856页

作者： Soto, Ricardo Crawford, Broderick Aste Toledo, Angelo de la Fuente-Mella, Hanns Castro, Carlos Paredes, Fernando Olivares, Rodrigo Pontificia Univ Catolica Valparaiso Ave Brasil 2241 Valparaiso 2362807 Chile Univ Tecn Federico Santa Maria Ave Espana 1680 Valparaiso 2390123 Chile Univ Diego Portales Ave Ejercito 441 Santiago 8370109 Chile Univ Valparaiso Gen Cruz 222 Valparaiso 2603631 Chile

In this research, we present a Binary Cat Swarm Optimization for solving the Manufacturing Cell Design Problem (MCDP). This problem divides an industrial production plant into a certain number of cells. Each cell contains machines with similar types of processes or part families. The goal is to identify a cell organization in such a way that the transportation of the different parts between cells is minimized. The organization of these cells is performed through Cat Swarm Optimization, which is a recent swarm metaheuristic technique based on the behavior of cats. In that technique, cats have two modes of behavior: seeking mode and tracing mode, selected from a mixture ratio. For experimental purposes, a version of the Autonomous Search algorithm was developed with dynamic mixture ratios. The experimental results for both normal Binary Cat Swarm Optimization (BCSO) and Autonomous Search BCSO reach all global optimums, both for a set of 90 instances with known optima, and for a set of 35 new instances with 13 known optima.

关键词： CAT behavior MATHEMATICAL optimization MANUFACTURING cells metaheuristic algorithms SEARCH algorithms

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Development and application of Quantum Entanglement inspired Particle Swarm Optimization

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KNOWLEDGE-BASED SYSTEMS 2021年 219卷 106859-106859页

作者： Vaze, Rujuta Deshmukh, Nagraj Kumar, Rajesh Saxena, Akash Malaviya Natl Inst Technol Jaipur Dept Elect Engn Jaipur Rajasthan India Swami Keshvanand Inst Technol Management & Gramot Dept Elect Engn Jaipur Rajasthan India

Particle Swarm Optimization has been extensively researched and applied to tackle optimization problems due to the ease in implementation and less number of parameters to be tuned. But particle swarm optimization (PSO) algorithm gets trapped into local optimum in high-dimensional space and it is inefficient in solving optimization problems which show high dependency. To overcome the above problems without compromising the advantages of PSO, this paper proposes Quantum Entanglement inspired Particle Swarm Optimization (QEPSO). QEPSO incorporates entangled states in its Q-bits to efficiently solve high-dependency problems and uses quantum local search to accelerate the optimization process. The proposed algorithm is tested on several standard benchmark functions and is also further benchmarked on IEEE Congress of Evolutionary computing (CEC 2017) benchmark set. The performance of QEPSO is compared with existing variants of PSO and some other popular algorithms. The results show that QEPSO outperforms other algorithms and is especially useful in high dimensional problems. Finally it is used for a real-life application of Multi-level Image Segmentation where eight gray-scale standard test images were used. The performance of QEPSO was superior to the other algorithms as it gave better results with high stability and quick convergence. (C) 2021 Elsevier B.V. All rights reserved.

关键词： metaheuristic algorithms Particle Swarm Optimization Quantum Entanglement High-dependency problems

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Soft computing based closed form equations correlating L and N-type Schmidt hammer rebound numbers of rocks

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TRANSPORTATION GEOTECHNICS 2021年 29卷

作者： Asteris, Panagiotis G. Mamou, Anna Hajihassani, Mohsen Hasanipanah, Mahdi Koopialipoor, Mohammadreza Tien-Thinh Le Kardani, Navid Armaghani, Danial J. Sch Pedag & Technol Educ Computat Mech Lab GR-14121 Athens Greece Urmia Univ Fac Engn Dept Min Engn Orumiyeh *** Iran Univ Kashan Dept Min Engn Kashan Iran Amirkabir Univ Technol Fac Civil & Environm Engn Tehran 15914 Iran PHENIKAA Univ Fac Mech Engn & Mechatron Hanoi 12116 Vietnam PHENIKAA Res & Technol Inst PRATI A&A Green Phoenix Grp JSC 167 Hoang Ngan Hanoi 11313 Vietnam Royal Melbourne Inst Technol RMIT Sch Engn Civil & Infrastruct Engn Discipline Melbourne Vic 3001 Australia South Ural State Univ Inst Architecture & Construct Dept Urban Planning Engn Networks & Syst 76 Lenin Prospect Chelyabinsk 454080 Russia

This paper reports the results of soft computing-based models correlating L and N-type Schmidt hammer rebound numbers of rock. A data-independent database was compiled from available measurements reported in the literature, which was used to train and develop back propagating neural networks, genetic programming and least square method models for the prediction of L-type Schmidt hammer rebound numbers. The results show that the highest predictive accuracy was obtained for the neural network model, which predicts the L type Schmidt hammer rebound number, with less than +/- 20% deviation from the experimental data for 97.27% of the samples. The optimum neural network is presented as a closed form equation and is also incorporated into an Excel-based graphical user interface, which directly calculates the Rn(L) number for any input Rn(N) = 12.40-75.97 and which is made available as supplementary material.

关键词： Artificial neural networks Genetic programming Machine learning metaheuristic algorithms Non-destructive testing Rocks Schmidt hammer rebound number

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Theoretical and Empirical Analyses of an Improved Harmony Search Algorithm Based on Differential Mutation Operator

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JOURNAL OF APPLIED MATHEMATICS 2012年第unknown期2012卷 1-20页

作者： Yong, Longquan Liu, Sanyang Zhang, Jianke Feng, Quanxi Xidian Univ Dept Appl Math Xian 710071 Peoples R China Shaanxi Univ Technol Sch Math & Comp Sci Hanzhong 723001 Peoples R China Xian Univ Posts & Telecommun Sch Sci Xian 710121 Peoples R China Guilin Univ Technol Sch Sci Guilin 541004 Peoples R China

Harmony search (HS) method is an emerging metaheuristic optimization algorithm. In this paper, an improved harmony search method based on differential mutation operator (IHSDE) is proposed to deal with the optimization problems. Since the population diversity plays an important role in the behavior of evolution algorithm, the aim of this paper is to calculate the expected population mean and variance of IHSDE from theoretical viewpoint. Numerical results, compared with the HSDE, NGHS, show that the IHSDE method has good convergence property over a test-suite of well-known benchmark functions.

关键词： SEARCH algorithms DIFFERENTIAL operators EMPIRICAL research metaheuristic algorithms ARITHMETIC mean ANALYSIS of variance NUMERICAL analysis

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Control of Discrete Event Systems by Means of Discrete Optimization and Disjunctive Colored PNs: Application to Manufacturing Facilities

ABSTRACT AND APPLIED ANALYSIS

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ABSTRACT AND APPLIED ANALYSIS 2014年第Spe17期2014卷 1-16页

作者： Latorre-Biel, Juan-Ignacio Jimenez-Macias, Emilio Perez de la Parte, Mercedes Blanco-Fernandez, Julio Martinez-Camara, Eduardo Univ Publ Navarra Dept Mech Energy & Mat Engn Tudela 31500 Spain Univ La Rioja Dept Elect Engn Logrono 26006 Spain Univ La Rioja Dept Mech Engn Logrono 26006 Spain

Artificial intelligence methodologies, as the core of discrete control and decision support systems, have been extensively appliedin the industrial production sector. The resulting tools produce excellent results in certain cases;however, the NP-hard nature of many discrete control or decision making problems in the manufacturing area may require unaffordable computational resources, constrained by the limited available time required to obtain a solution. With the purpose of improving the efficiency of a control methodology for discrete systems, based on a simulation-based optimization and the Petri net (PN) model of the real discrete event dynamic system (DEDS), this paper presents a strategy, where a transformation applied to the model allows removing the redundant information to obtain a smaller model containing the same useful information. As a result, faster discrete optimizations can be implemented. This methodology is based on the use of a formalism belonging to the paradigm of the PN for describing DEDS, the disjunctive colored PN. Furthermore, the metaheuristic of genetic algorithms is applied to the search of the best solutions in the solution space. As an illustration of the methodology proposal, its performance is compared with the classic approach on a case study, obtaining faster the optimal solution.

关键词： DISCRETE systems PETRI nets NETS (Mathematics) MATHEMATICAL optimization SYSTEMS theory metaheuristic algorithms GENETIC algorithms

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