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A novel star-nosed mole optimization algorithm applied for MPPT of PV systems

Scientific reports 2025年第1期15卷 17807页

作者： Ali M Eltamaly Zeyad A Almutairi Sustainable Energy Technology Center King Saud University Riyadh Saudi Arabia. eltamaly@ksu.edu.sa. Sustainable Energy Technology Center King Saud University Riyadh Saudi Arabia. Mechanical Engineering Department King Saud University 11421 Riyadh Saudi Arabia.

Star-nosed mole optimization (SNMO) algorithm is a novel metaheuristic algorithm inspired by the unique foraging behavior of the star-nosed mole. This animal's exceptional ability to rapidly sense and process information from its environment has been translated into a powerful optimization algorithm in this study. SNMO leverages the mole's parallel exploration, focused foraging, and adaptive movement strategies to efficiently search for optimal solutions. The algorithm initializes a population of candidate solutions and iteratively refines them through a process of sensory exploration, promising region prioritization, and redundancy avoidance. SNMO's key features include its ability to balance exploration and exploitation, adapt to dynamic environments, and efficiently converge to optimal solutions. This paper presents the core principles, mathematical formulation, and potential applications of the SNMO algorithm, showcasing its effectiveness in solving a wide range of optimization problems especially the maximum power point tracker (MPPT) of PV systems. The power-voltage characteristics of the PV arrays under partial shading conditions (PSCs) which have several peaks show an amazing idea to send each mole to the vicinity of each peak to separately determine the peaks around it. Once moles determine the peaks, they will be attracted to the highest one. This algorithm has been examined in grid-connected PV systems through simulation and experimental work and showed superior convergence speed and accuracy performance. Future improvements to this novel optimization algorithm can lead to further improvement to the MPPT of PV systems and other applications.

关键词： Boost converter Maximum power point tracker Metaheuristic optimization algorithm Photovoltaic Star-nosed mole

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An Efficient optimization algorithm to Study the Stochastic Responses of Vehicle-Bridge Coupling Systems

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COMPUTING IN SCIENCE & ENGINEERING 2019年第3期21卷 6-17页

作者： Zhu, Siyu Li, Yongle Togbenou, Koffi Xiang, Tianyu Chengdu Univ Technol Coll Environm & Civil Engn Chengdu Sichuan Peoples R China Southwest Jiaotong Univ Dept Bridge Engn Chengdu Sichuan Peoples R China Xihua Univ Dept Civil & Struct Engn Chengdu Sichuan Peoples R China

An efficient algorithm is proposed for the stochastic responses of vehicle-bridge systems. The pseudo-excitation method is combined with the self-adaptive Gauss integration to handle the stochastic responses of vehicle-bridge systems, for which the statistic characteristics are calculated. The applicability and accuracy of the proposed hybrid numerical method are demonstrated by comparing the results with those from Monte Carlo simulation. In addition, nondimensional time is introduced to evaluate the stochastic responses of vehicles.

关键词： Bridges Structures Gaussian Processes Integral Equations Integration Monte Carlo Methods Time Domain Analysis Vibrations Stochastic Responses Vehicle Bridge Coupling System Pseudoexcitation Method optimization algorithm Self Adaptive Gauss Integration Monte Carlo Simulation Intelligent Vehicles Couplings Vibrations Mathematical Model Stochastic Processes Vehicle Dynamics Monte Carlo Methods Stochastic Responses Vehicle Bridge System Pseudo Excitation Method Self Adaptive Gauss Integration

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A Region Preference-based optimization algorithm for Satellite Mission Planning Problem

A Region Preference-based Optimization Algorithm for Satelli...

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作者： Minghui Xiong Wei Xiong Science and Technology on Complex Electronic System Simulation Laboratory Space Engineering University

For the earth observation satellite mission planning problem, objectives such as observed target quantity, observation profit, energy consumption, image quality should be considered simultaneously, which is a many-objective optimization problem. Classical optimization-based mission planning algorithms obtain a set of non-dominated solutions in the entire search space, while only a single satisfy final plan is desired by decision maker. In this paper, a five-objective optimization model for satellite mission planning problem is constructed, then a region preference-based evolutionary algorithm, HMOEA-T, is applied to obtain the desired solutions. The decision makers describe the preference on each objective in target region form, then the algorithm guides a more detailed search within the preference region rather than the entire Pareto front. Comparative studies with preference-based methods(T-NSGA-III) and classical methods(NSGA-III) are conducted. We have exemplified the proposed method manage to obtain the solutions satisfying the mission planning preference and achieve better performance in convergence and diversity.

关键词： optimization algorithm Preference articulation Satellite mission planning Many-objective optimization model

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Effective Neural Network Training With a New Weighting Mechanism-Based optimization algorithm

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IEEE ACCESS 2019年 7卷 72403-72410页

作者： Yu, Yunlong Liu, Fuxian Air Force Engn Univ Dept Air Def & Antimissile Xian 710051 Shaanxi Peoples R China

First-order gradient-based optimization algorithms have been of core practical importance in the field of deep learning. In this paper, we propose a new weighting mechanism-based first-order gradient descent optimization algorithm, namely NWM-Adam, to resolve the undesirable convergence behavior of some optimization algorithms which employ fixed sized window of past gradients to scale the gradient updates and improve the performance of Adam and AMSGrad. The NWM-Adam is developed on the basis of the idea, i.e., placing more memory of the past gradients than the recent gradients. Furthermore, it can easily adjust the degree to which how much the past gradients weigh in the estimation. In order to empirically test the performance of our proposed NWM-Adam optimization algorithm, we compare it with other popular optimization algorithms in three well-known machine learning models, i.e., logistic regression, multi-layer fully connected neural networks, and deep convolutional neural networks. The experimental results show that the NWM-Adam can outperform other optimization algorithms.

关键词： Deep learning optimization algorithm learning rate neural network training

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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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Modeling of an Air-Core Compulsator and the Pulse Shaping Using optimization algorithm

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IEEE TRANSACTIONS ON PLASMA SCIENCE 2019年第1期47卷 611-618页

作者： Tao, Xuefeng Liu, Kun Wang, Haoze Space Engn Univ Dept Grad Management Beijing 101400 Peoples R China Sun Yat Sen Univ Sch Aeronaut & Astronaut Shenzhen 518101 Peoples R China

Compensated pulsed alternator (compulsator) plays a significant role in the field of pulsed power supply. Different kinds of compulsators have been used to drive high-energy weapons. In this paper, the mathematical model of a two-phase four-pole air-core compulsator is established, taking the current coupling, the change of rotor speed and changing load characteristics into account. Both the self-excitation process and the discharge process are modeled. Simulation results indicate that our model has high accuracy in comparison with the results of the co-simulation method using finite-element method and circuit principle. Moreover, the usage of the mathematical model can improve the simulation efficiency and flexibility. The current pulse requirements of electromagnetic rail gun (EMRG), flash lamp, and electro-thermal-chemical gun (ETCG) are analyzed, respectively, and the pulse shape optimization problem is studied based on the intelligent optimization algorithm. For EMRG and flash lamp, the term "acceleration ratio" is introduced to identify whether a pulse is flat or spiked. For ETCG, the conception of "shape variance" is proposed to evaluate the fitness of its pulse shape. With the help of suitable objection function and intelligent optimization algorithm, the optimal discharge pulse for specific load can be obtained easily.

关键词： Air-core compensated pulsed alternator (compulsator or CPA) mathematical model optimization algorithm pulse shaping techniques

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An Effective Hybrid optimization algorithm for Static Rebalance Problem of Bicycle-Sharing System

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IET Intelligent Transport Systems 2025年第1期19卷

作者： Shuhui Liang Yutong Ye Jingli Wu Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin China School of Computer Science and Engineering Guangxi Normal University Guilin China Software Engineering Institute East China Normal University Shanghai China

Although the bicycle-sharing system plays an important role in easing urban traffic congestion and reducing carbon emissions, it still suffers from the problem of unbalanced bicycle distribution between different stations, which significantly limits the utilization of bicycles. To solve this problem, this paper proposes a novel hybrid optimization algorithm, MixPS, based on the combination of a partheno-genetic algorithm and a simulated annealing algorithm. This algorithm can effectively rebalance the distribution of bicycles. To further improve performance, we design a decimal code to represent a scheduled path and introduce six mutation operators to change the chromosome. The whole evolution is controlled with the simulated annealing process. Moreover, we apply the Metropolis acceptance criterion to effectively reduce the possibility that the population falls into the local optimum. Comprehensive experimental results obtained from both synthetic and real-world data sets show that our proposed MixPS algorithm can achieve better optimization and generate a shorter schedule path scheme in less time. These results confirm the efficiency and effectiveness of our method in solving the static bicycle rebalancing problem with single-vehicle and multiple-access.

关键词： bicycle rebalancing problem effective heuristic low-carbon optimization algorithm

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AdaptAhead optimization algorithm for Learning Deep CNN Applied to MRI Segmentation

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JOURNAL OF DIGITAL IMAGING 2019年第1期32卷 105-115页

作者： Hoseini, Farnaz Shahbahrami, Asadollah Bayat, Peyman Islamic Azad Univ Dept Comp Engn Rasht Branch Rasht Iran Univ Guilan Dept Comp Engn Fac Engn Rasht Iran

Deep learning is one of the subsets of machine learning that is widely used in artificial intelligence (AI) field such as natural language processing and machine vision. The deep convolution neural network (DCNN) extracts high-level concepts from low-level features and it is appropriate for large volumes of data. In fact, in deep learning, the high-level concepts are defined by low-level features. Previously, in optimization algorithms, the accuracy achieved for network training was less and high-cost function. In this regard, in this study, AdaptAhead optimization algorithm was developed for learning DCNN with robust architecture in relation to the high volume data. The proposed optimization algorithm was validated in multi-modality MR images of BRATS 2015 and BRATS 2016 data sets. Comparison of the proposed optimization algorithm with other commonly used methods represents the improvement of the performance of the proposed optimization algorithm on the relatively large dataset. Using the Dice similarity metric, we report accuracy results on the BRATS 2015 and BRATS 2016 brain tumor segmentation challenge dataset. Results showed that our proposed algorithm is significantly more accurate than other methods as a result of its deep and hierarchical extraction.

关键词： Deep learning Convolutional neural networks MRI segmentation Deep convolutional neural networks optimization algorithm

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An efficient optimization algorithm that hybridizes DFTB and DFT theories both operated within the modified basin hopping method

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COMPUTER PHYSICS COMMUNICATIONS 2019年 236卷 164-175页

作者： Lai, S. K. Maftuhin, Wafa Natl Cent Univ Dept Phys Complex Liquids Lab Chungli 320 Taiwan Sampoerna Univ Fac Engn & Technol Jakarta Indonesia

The density functional tight-binding (DFTB) theory and density functional theory (DFT) are separately combined with the modified basin hopping (MBH) method into a two-stage optimization algorithm that is then applied in sequence to find the lowest-energy structures of Au clusters. In the first-stage, the DFTB/MBH is conducted mainly to provide a speedy and yet semi-quantitatively reliable searching of the lowest-energy structures so as to pave the way for the second-stage DFT/MBH for more refined and accurate sorting of them at the DFT level. To ensure high efficiency in searching the minimized energy, we pay more attention to the DFTB theory in particular the repulsive part energy and examine the quality of the DFTB parameters fitted to different sets of reference structures. This parametrization study is physically relevant since the cluster's structure is basically determined, by and large, by the repulsive potential. To appreciate the nicety of the present method, we apply the latter and two other existing sets of DFTB parameters to perform the first-stage DFTB/MBH but, separately, we continue the second-stage optimization all by the same DFT/MBH method. We found that the lowest-energy geometries of gold clusters obtained are independent of the use of the DFTB theory to calculate the energy function in the first-stage minimization. Only the efficacy of executing the second-stage DFT/MBH which is generally time-consuming differs, however. In general, for duly-fitted DFTB parameters less computer time is required at the second-stage operation. On comparing further our calculated optimized Au structures with other theoretical calculations and existing experimental clusters, the very good agreement among them clearly explains that the present hybridized scheme has greater potential to efficiently perform with less or within affordable computing time the high-level OFT calculation for medium to larger sized clusters. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Density functional tight-binding theory Density functional theory Metallic cluster optimization algorithm

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Efficient and merged biogeography-based optimization algorithm for global optimization problems

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SOFT COMPUTING 2019年第12期23卷 4483-4502页

作者： Zhang, Xinming Kang, Qiang Tu, Qiang Cheng, Jinfeng Wang, Xia Henan Normal Univ Coll Comp & Informat Engn Xinxiang Peoples R China Engn Technol Res Ctr Comp Intelligence & Data Min Xinxiang Peoples R China

In order to improve the optimization efficiency of the biogeography-based optimization (BBO) algorithm, this study proposes a novel BBO algorithm, namely an efficient and merged biogeography-based optimization (EMBBO) algorithm. Firstly, BBO's mutation operator is got rid of. Then, a differential mutation operator and a sharing operator are merged into BBO's migration operator to obtain an improved migration operator. In the improved migration operator, the emigration habitats are selected by a new example learning approach. The above improvements can enhance the optimization performance and reduce the computation complexity. Thirdly, a new single-dimensional and all-dimensional alternating strategy is combined with the improved migration operator to balance exploration and exploitation and reduce more computation complexity. Fourthly, the opposition-based learning approach is merged to prevent the algorithm from falling into the local optima. Finally, the greedy selection method is used instead of the elitist strategy to avoid setting the elitist parameter and to get rid of one sorting step. We make a large number of experiments on a set of classic benchmark functions and CEC2017 test set and apply EMBBO to clustering optimization. Experiment results verify that EMBBO can obtain the highest optimization efficiency compared with quite a few state-of-the-art algorithms.

关键词： optimization algorithm Evolutionary algorithm Biogeography-based optimization Differential mutation operator Example learning approach

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