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Studying the varied shapes of gold clusters by an elegant optimization algorithm that hybridizes the density functional tight-binding theory and the density functional theory

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COMPUTER PHYSICS COMMUNICATIONS 2017年 220卷 143-149页

作者： Yen, Tsung-Wen Lim, Thong-Leng Yoon, Tiem-Leong Lai, S. K. Natl Cent Univ Dept Phys Complex Liquids Lab Chungli 32001 Taiwan Multimedia Univ Fac Engn & Technol Melaka CampusJalan Ayer Keroh Lama Bukit Beruang 75450 Melaka Malaysia Univ Sains Malaysia Sch Phys Usm 11800 Pulau Penang Malaysia

We combined a new parametrized density functional tight-binding (DFTB) theory (Fihey et al. 2015) with an unbiased modified basin hopping (MBH) optimization algorithm (Yen and Lai 2015) and applied it to calculate the lowest energy structures of Au clusters. From the calculated topologies and their conformational changes, we find that this DFTB/MBH method is a necessary procedure for a systematic study of the structural development of Au clusters but is somewhat insufficient for a quantitative study. As a result, we propose an extended hybridized algorithm. This improved algorithm proceeds in two steps. In the first step, the DFTB theory is employed to calculate the total energy of the cluster and this step (through running DFTB/MBH optimization for given Monte-Carlo steps) is meant to efficiently bring the Au cluster near to the region of the lowest energy minimum since the cluster as a whole has explicitly considered the interactions of valence electrons with ions, albeit semi-quantitatively. Then, in the second succeeding step, the energy-minimum searching process will continue with a skilledly replacement of the energy function calculated by the DFTB theory in the first step by one calculated in the full density functional theory (DFT). In these subsequent calculations, we couple the DFT energy also with the MBH strategy and proceed with the DFT/MBH optimization until the lowest energy value is found. We checked that this extended hybridized algorithm successfully predicts the twisted pyramidal structure for the Au-40 cluster and correctly confirms also the linear shape of C-8 which our previous DFTB/MBH method failed to do so. Perhaps more remarkable is the topological growth of Au-n: it changes from a planar (n = 3-11) -> an oblate-like cage (n = 12-15) -> a hollow-shape cage (n = 16-18) and finally a pyramidal-like cage (n = 19, 20). These varied forms of the clusters shapes are consistent with those reported in the literature. (C) 2017 Elsevier B.V. All righ

关键词： Metallic cluster Density functional theory optimization algorithm Topological transition

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Optimal operation for marine hybrid power system: Novel optimization algorithm and comprehensive analysis for penalty functions

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第5期43卷 6627-6649页

作者： An, Qing Tang, Ruoli Li, Xueyan Zhang, Xiaodi Li, Xin Wuchang Univ Technol Sch Artificial Intelligence Wuhan Peoples R China Wuhan Univ Technol Sch Naval Architecture Ocean & Energy Power Engn Wuhan Peoples R China State Grid Beijing Elect Maintenance Co Beijing Peoples R China

In order to optimally control the marine hybrid power system (HPS) under increasingly complex regulation constraints or hardware constraints, an efficient power-flowscheduling model and optimization algorithm are of great importance. This work focuses on the optimal power-flow scheduling of marine HPS, especially on the efficiency improvement of the penalty functions for satisfying complex constraints. To be specific, an optimal operation model of marine HPS is discussed, and the complex model constraints are described as various penalty functions. Secondly, a novel optimization algorithm, namely adaptive multi-context cooperatively coevolving differential evolution algorithm with random topology and mutated context vector (AMCCDE(-rt-mcv)) is developed to optimize the aforementioned model. In order to ensure the satisfaction of the complex model constraints, the detailed forms for penalty functions are researched and the optimal parameters for penalty functions are comprehensively compared, analysed and tested by a set of numerical experiments. Finally, the developed methodologies are tested by simulation experiments. Experimental results show that the damping factor, exponent parameter and punish strength constant effect the efficiency of penalty functions a lot, and the developed penalty functions can effectively satisfy all the model constraints with fast response speed. With the integration of penalty functions, the developed methodology can obtain promising performance on the optimal scheduling of the evaluated marine HPS.

关键词： Hybrid power system optimal energy management penalty function optimization algorithm differential evolution

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A Novel Impedance Estimation Method for Determining Voltage Unbalance Contributions Based on an optimization algorithm

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IEEE ACCESS 2024年 12卷 145944-145954页

作者： Brito, Vinicius Henrique Farias de Oliveira, Jose Carlos Gregory, Raquel Cristina Filiagi Univ Fed Uberlandia Elect Engn Fac BR-38400902 Uberlandia Brazil Univ Fed Ceara Dept Elect Engn BR-60020181 Fortaleza Brazil

The problem of "unbalance responsibility-sharing" is a matter of great concern for utility and consumer entities due to the increase in voltage unbalances, especially in electrical distribution systems. This issue arises from the need to determine the individual contributions of the utility and the consumer to the total unbalance at the point of common coupling between the entities. In the literature, the Superposition Method stands out as an effective approach to solving this problem. However, this procedure requires utility and consumer negative-sequence impedances, which are difficult to obtain in real-life scenarios. There is a variety of impedance estimation methods to overcome this issue, with emphasis on non-invasive methods. According to the literature, some of these procedures have practical and accuracy limitations. Therefore, this paper proposes a novel non-invasive impedance estimation method, termed the Correlation Minimization Method (CMM), which is based, essentially, on an optimization algorithm. In addition, computational simulations and field tests were carried out to evaluate the performance of the proposed method in comparison with existing approaches. The results show that the proposed method performed satisfactorily, even in the most adverse situations.

关键词： Impedance Estimation Equivalent circuits Fluctuations Harmonic analysis Voltage measurement optimization Frequency modulation Voltage Vectors Power quality Impedance estimation method non-invasive method optimization algorithm power quality unbalance unbalance responsibility-sharing problem

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Multi-core-, multi-thread-based optimization algorithm for large-scale traveling salesman problem

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ALEXANDRIA ENGINEERING JOURNAL 2021年第1期60卷 189-197页

作者： Wei, Xin Ma, Liang Zhang, Huizhen Liu, Yong Univ Shanghai Sci & Technol Sch Management Shanghai 200093 Peoples R China

With the rapid development of general hardware technology, microcomputers with multi-core CPUs have been widely applied in commercial services and household usage in the last ten years. Multi-core chips could, theoretically, lead to much better performance and computational efficiency than single-core chips. But so far, they have not shown general advantages for users, other than for operating systems and some specialized software. It is not easy to transform traditional single-core-based algorithms into multi-core-, multi-thread-based algorithms that can greatly improve efficiency, because of difficulties in computation and scheduling of hardware kernels, and because some programming languages cannot support multi-core, multi-thread programming. Therefore, a kind of multi-core-, multi-thread-based fast algorithm was designed and coded with Delphi language for the medium- and large-scale traveling salesman problem instances from TSPLIB, which can fully speed up the searching process without loss of quality. Experimental results show that the algorithm proposed can, under the given hardware limitations, take full advantage of multi-core chips and effectively balance the conflict between increasing problem size and computational efficiency and thus acquire satisfactory solutions. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Alexandria University.

关键词： Multi-core Multi-thread Traveling Salesman Problem optimization algorithm

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Application of a Meta-heuristic optimization algorithm Motivated by a Vision Correction Procedure for Civil Engineering Problems

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KSCE JOURNAL OF CIVIL ENGINEERING 2018年第7期22卷 2623-2636页

作者： Lee, Eui Hoon Lee, Ho Min Yoo, Do Guen Kim, Joong Hoon Korea Univ Res Ctr Disaster Prevent Sci & Technol Seoul 02481 South Korea Univ Suwon Dept Civil Engn Suwon 18323 South Korea Korea Univ Sch Civil Environm & Architectural Engn Seoul 02481 South Korea

In this study, a metaheuristic optimization algorithm inspired by a vision correction procedure is applied to civil engineering problems. The Vision Correction algorithm (VCA) has the ability to solve various problems related to mathematical benchmark functions and civil engineering. Vision correction processes have three main steps: myopic/hyperopic correction, brightness adjustment/compression enforcement, and astigmatic correction. This procedure is essential for increasing the usability of glasses and obtaining high-quality vision in humans. Unlike conventional meta-heuristic algorithms, VCA automatically adjusts the global/ local search probability and global search direction based on accumulated optimization results. In VCA, all decision variables have their own search probabilities and require different processes according to whether a global search or local search is required. The proposed algorithm is applied to representative optimization problems, and the results are compared with those of existing algorithms. In civil engineering problems including design problem of water distribution network, VCA shows respectable results compared with those of existing algorithms. In all benchmark problems and civil engineering problems, VCA shows good results and it showed the applicability to other civil engineering problems.

关键词： vision correction optimization algorithm metaheuristic global search local search

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m-MBOA: a novel butterfly optimization algorithm enhanced with mutualism scheme

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SOFT COMPUTING 2020年第7期24卷 4809-4827页

作者： Sharma, Sushmita Saha, Apu Kumar Natl Inst Technol Dept Math Agartala 799046 Tripura India

The simplicity and effectiveness of a recently proposed metaheuristic, butterfly optimization algorithm (BOA) have gained huge popularity among research community and are being used to solve optimization problems in various disciplines. However, the algorithm is suffering from poor exploitation ability and has a tendency to show premature convergence to local optima. On the other hand, the mutualism phase of another popular metaheuristic symbiosis organisms search (SOS) is known for its exploitation capability. In this paper, a novel hybrid algorithm, namely m-MBOA is proposed to enhance the exploitation ability of BOA with the help of mutualism phase of SOS. To evaluate the effectiveness of m-MBOA, thirty-seven (37) classical benchmark functions are considered and the performance of m-MBOA is compared with the performance of ten (10) state-of-the-art algorithms. Statistical tools have been employed to observe the efficiency of the m-MBOA qualitatively, and obtained results confirm the superiority of the proposed algorithm compared to the state-of-the-art metaheuristic algorithms. Finally, four real-life optimization problem, namely gear train design problem, gas compressor design problem, cantilever beam design problem and three-bar truss design problem are solved with the help of the newly proposed algorithm, and the results are compared with the obtained results of different popular state-of-the-art optimization techniques and found that the proposed algorithm is more efficient than the compared algorithms.

关键词： optimization algorithm Butterfly optimization algorithm (BOA) Mutualism phase Symbiosis organisms search (SOS) m-MBOA Hybrid method Benchmark function

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HGAB3C: A new hybrid global optimization algorithm

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2019年第5期27卷 3557-3566页

作者： Kaur, Kamaljeet Kumar, Shakti Saxena, Jyoti IK Gujral Punjab Tech Univ Jalandhar Punjab India Panipat Inst Engn & Technol Dept Elect & Commun Engn Panipat Haryana India Maharaja Ranjit Singh Punjab Tech Univ Giani Zail Singh Campus Coll Engn & Technol Dept Elect & Commun Engn Bathinda Punjab India

This paper proposes a new optimization algorithm, namely HGAB3C, and presents its performance on the CEC-2014 test suite. In HGAB3C, simple genetic algorithms (GAs) and big bang-big crunch (BB-BC) are hybridized. The algorithm carries out global searches using a simple GA. In every generation the BB-BC algorithm is used to carry out local searches. The addition of local search has improved the capability of simple GAs significantly. The performance of the proposed algorithm is compared with 17 other optimization algorithms on all 30 functions of the CEC-2014 benchmark suite. It is observed that HGAB3C outperforms all other algorithms on 4 benchmark functions. For the 3 other functions, its performance equaled the best of the competing algorithms, which makes HGAB3C's performance best in a total of 7 benchmark functions. Out of the 18 competing algorithms, the proposed algorithm ranked second for the unmatched best mean error measure. For the best performance measure (number of functions giving unmatched best and equaled best mean error), the proposed algorithm was the third best. As far as the speed of convergence is concerned, the algorithm gave an unmatched best performance for the shifted Schwefel function (function 10 of CEC-2014 test bench). It obtained a mean error value of 0.00E+00, outperforming the previous best of 1.23E-03, converging to the target result in an average of 346.44 generations, which no other algorithm could achieve.

关键词： optimization algorithm HGAB3C CEC-2014

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Recent Studies on Chicken Swarm optimization algorithm: a review (2014-2018)

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ARTIFICIAL INTELLIGENCE REVIEW 2020年第3期53卷 1737-1765页

作者： Deb, Sanchari Gao, Xiao-Zhi Tammi, Kari Kalita, Karuna Mahanta, Pinakeswar Indian Inst Technol Ctr Energy Gauhati India Univ Eastern Finland Sch Comp Kuopio Finland Aalto Univ Dept Mech Engn Espoo Finland Indian Inst Technol Dept Mech Engn Gauhati India

Solving a complex optimization problem in a limited timeframe is a tedious task. Conventional gradient-based optimization algorithms have their limitations in solving complex problems such as unit commitment, microgrid planning, vehicle routing, feature selection, and community detection in social networks. In recent years population-based bio-inspired algorithms have demonstrated competitive performance on a wide range of optimization problems. Chicken Swarm optimization algorithm (CSO) is one of such bio-inspired meta-heuristic algorithms mimicking the behaviour of chicken swarm. It is reported in many literature that CSO outperforms a number of well-known meta-heuristics in a wide range of benchmark problems. This paper presents a review of various issues related to CSO like general biology, fundamentals, variants of CSO, performance of CSO, and applications of CSO.

关键词： Chicken Swarm optimization algorithm Nature inspired intelligence optimization algorithm Applications Review

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Mud Ring algorithm: A New Meta-Heuristic optimization algorithm for Solving Mathematical and Engineering Challenges

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IEEE ACCESS 2022年 10卷 50448-50466页

作者： Desuky, Abeer S. Cifci, Mehmet Akif Kausar, Samina Hussain, Sadiq El Bakrawy, Lamiaa M. Al Azhar Univ Fac Sci Dept Math Cairo 11754 Egypt Bandirma Onyedi Eylul Univ Dept Comp Engn TR-10200 Balikesir Turkey Univ Kotli Azad Jammu & Kashmir Dept Comp Sci & Informat Technol Kotli Azad Kashmir 11100 Pakistan Dibrugarh Univ Examinat Branch Dibrugarh 786004 Assam India

This paper proposes a new meta-heuristic optimization algorithm, namely Mud Ring algorithm (MRA) that mimics the mud ring feeding behaviour of bottlenose dolphins in the Atlantic coast of Florida. The inspiration of MRA is mainly based on the foraging behaviour of bottlenose dolphins and their mud ring feeding strategy. This strategy is applied by dolphins to trap fish via creating a plume by a single dolphin moving his tail swiftly in the sand and swims around the group of fish. The fishes become disoriented and jump over the surface only to find the waiting mouths of dolphins. MRA optimization algorithm mathematically simulates this feeding strategy and proves its optimization effectiveness through a comprehensive comparison with other meta-heuristic algorithms. Twenty-nine benchmark functions and four commonly used benchmark engineering challenges are used in the comparison. The statistical comparisons and results prove that the proposed MRA has the superiority in dealing with these optimization problems and can obtain the best solutions than other meta-heuristic optimizers.

关键词： Dolphins Particle swarm optimization Fish Benchmark testing Welding Springs Pressure vessels optimization algorithm meta-heuristic nature-inspired algorithms swarm intelligence 3-bar truss design challenge tension compression spring design challenge pressure vessel design challenge welded beam design challenge

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Minimization of blast-induced dust emission using gene-expression programming and grasshopper optimization algorithm: a smart mining solution based on blasting plan optimization

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CLEAN TECHNOLOGIES AND ENVIRONMENTAL POLICY 2022年第8期24卷 2313-2328页

作者： Hosseini, Shahab Monjezi, Masoud Bakhtavar, Ezzeddin Tarbiat Modares Univ Fac Engn Tehran Iran Urmia Univ Technol Fac Environm Orumiyeh Iran

In surface mining, blast-induced dust can be discharged to the atmosphere and impact the surrounding environment and nearby residential areas, especially if a large volume of rock is blasted under inappropriate meteorological conditions such as high wind speed. Many attempts have been done to predict the blast-induced dust emission distance but the literature of the dust reduction is limited to change stemming materials based on water capsules. This study develops a methodology using gene expression programming and grasshopper optimization algorithm to find an optimal blasting plan with minimum blast-induced dust in a mine close to sensitive ecosystem and residential areas. The best gene expression programming model, which indicates relationship between dependent and independent variables, was first determined based on 100 blasting data collected from the mine. The model with the R-2 of 0.9559 and 0.9145, respectively, for training and validating parts was chosen as the best model. The model, as an objective function, was considered in grasshopper optimization algorithm to find the optimal blasting plan with minimum dust emission level. Compared to the old blasting plans of the mine, the optimal plan resulted in a reduction of 76.82% in the emission distance of the blast-induced under constant meteorological conditions. Sensitivity analysis on the system parameters revealed the high sensitivity of the output to wind speed, air temperature, air humidity, powder factor, and stemming. [GRAPHICS] .

关键词： Mine blasting Dust dispersion optimization algorithm Gene expression programming Grasshopper optimization algorithm

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