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Enhanced bi-layer scheduling strategies for the cascade hydropower-photovoltaic complementary system using a novel meta-heuristic algorithm

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FRONTIERS IN ENERGY RESEARCH 2024年 11卷

作者： Shen, Li Wen, Yiyu Wang, Qing Zhang, Peng State Grid Corp China State Grid Southwest Branch Corp Chengdu Peoples R China

Improving energy efficiency is crucial for China's power industry to meet global energy conservation and emission reduction goals. The rapid development of photovoltaic (PV) and hydropower has greatly assisted in the construction of China's novel power system. The stochastic characteristics of PV power generation pose significant challenges to the reliable and economical scheduling of power systems. In fact, the cascade hydropower station can effectively address the issue. To fully utilize the advantages of hydropower, this paper proposes a bi-layer scheduling optimization model for the cascade hydro-PV complementary system considering power market. The upper-layer model simultaneously maximizes the benefit and minimizes the output volatility of the complementary system. The lower-layer model carries out market clearing with the objective of social cost. Besides, PV uncertainty and market price volatility are considered in the decision-making process for power market transactions. To solve the bi-layer model, a novel meta-heuristic algorithm (geometric mean optimizer) is applied, demonstrating excellent performance compared to similar methods. For the complementary system, the results show that its total power output can be improved, and its output volatility can be effectively alleviated.

关键词： cascaded hydropower station hydropower-PV system bi-layer scheduling strategy meta-heuristic algorithm power market

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Wild horse optimizer: a new meta-heuristic algorithm for solving engineering optimization problems

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ENGINEERING WITH COMPUTERS 2022年第SUPPL 4期38卷 3025-3056页

作者： Naruei, Iraj Keynia, Farshid Islamic Azad Univ Dept Engn Kerman Branch Kerman Iran Grad Univ Adv Technol Inst Sci & High Technol & Environm Sci Dept Energy Management & Optimizat Kerman Iran

Nowadays, the design of optimization algorithms is very popular to solve problems in various scientific fields. The optimization algorithms usually inspired by the natural behaviour of an agent, which can be humans, animals, plants, or a physical or chemical agent. Most of the algorithms proposed in the last decade inspired by animal behaviour. In this article, we present a new optimizer algorithm called the wild horse optimizer (WHO), which is inspired by the social life behaviour of wild horses. Horses usually live in groups that include a stallion and several mares and foals. Horses exhibit many behaviours, such as grazing, chasing, dominating, leading, and mating. A fascinating behaviour that distinguishes horses from other animals is the decency of horses. Horse decency behaviour is such that the foals of the horse leave the group before reaching puberty and join other groups. This departure is to prevent the father from mating with the daughter or siblings. The main inspiration for the proposed algorithm is the decency behaviour of the horse. The proposed algorithm was tested on several sets of test functions such as CEC2017 and CEC2019 and compared with popular and new optimization methods. The results showed that the proposed algorithm presented very competitive results compared to other algorithms. The source code is currently available for public from: https://***/matlabcentral/fileexchange/90787-wild-horse-optimizer.

关键词： Optimization techniques meta-heuristic algorithm Horse algorithm Wild horse optimizer

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A novel nature-inspired meta-heuristic algorithm for optimization: bear smell search algorithm

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SOFT COMPUTING 2020年第17期24卷 13003-13035页

作者： Ghasemi-Marzbali, Ali Mazandaran Univ Sci & Technol Dept Comp & Elect Engn Babol Sar Iran

In the recent years, the optimization problems show that they are a big challenge for engineering regarding the fast growth of new nature-inspired optimization algorithms. Therefore, this paper presents a novel nature-inspired meta-heuristic algorithm for optimization which is called as bear smell search algorithm (BSSA) that takes into account the powerful global and local search operators. The proposed algorithm imitates both dynamic behaviors of bear based on sense of smell mechanism and the way bear moves in the search of food in thousand miles farther. Among all animals, bears have inconceivable sense of smell due to their huge olfactory bulbs that manage the sense of different odors. Since the olfactory bulb is a neural model of the vertebrate forebrain, it can make a strong exploration and exploitation for optimization. According to the odors value, bear moves the next location. Therefore, this paper mathematically models these structures. To demonstrate and evaluate the BSSA ability, numerous types of benchmark functions and four engineering problems are employed to compare the obtained results of BSSA with other available optimization methods with several analyzed indices such as pair-wise test, Wilcoxon rank and statistical analysis. The numerical results revealed that proposed BSSA presents competitive and greater results compared to other optimization algorithms.

关键词： Nature-inspired algorithm Bear smell search algorithm Benchmark functions meta-heuristic algorithm Bear's sense of smell

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Speed control of PMSM using a fuzzy logic controller with deformed MFS tuned by a novel hybrid meta-heuristic algorithm

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ELECTRICAL ENGINEERING 2024年第6期106卷 6927-6939页

作者： Nouaoui, Tahar Dendouga, Abdelhakim Bendaikha, Abdelmalik Univ Biskra Lab Identificat Command Control & Commun Biskra Algeria Univ Biskra Dept Elect Engn Biskra Algeria Univ Msila Dept Elect Engn Msila Algeria

The goal of this study is to design a very robust and efficient standard fuzzy logic controller (FC) with tuned and deformed membership functions (FC-TMFS) using a new hybrid algorithm for speed control of the permanent magnet synchronous motor. The proposed algorithm is a partial combination of three algorithms, which are sewing trainee-based optimization (STBO), particle swarm optimization (PSO), and symbiotic organism search (SOS). To demonstrate the superiority of the proposed algorithm (ST-PS-SO), FC-TMFS optimal parameters obtained using ST-PS-SO were compared to the optimal parameters achieved when using STBO, PSO, and SOS. In terms of the system performance, and the fitness of the global optimum parameters, ST-PS-SO convincingly out-competed all other algorithms. In addition to that, through MATLAB/SIMULINK simulation, ST-PSO-SOS-based FC-TMFS was compared to the classical proportional-integral (PI), and PI-derivative (PID) controllers, as well as the conventional FC with symmetric and untuned MFS, based on the results of speed tracking, torque induction, and robustness test, FC-TMFS convincingly outperformed all other controllers in every aspect by a very significant margin. Finally, processor-in-loop (PIL) implementation was also performed to validate and prove the importance and functionality of the designed FC-TMFS, as well as the capability of the proposed hybrid algorithm.

关键词： Field oriented control Permanent magnet synchronous motor meta-heuristic algorithm Space vector pulse width modulation Standard fuzzy logic controller Processor in loop

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A hybrid meta-heuristic algorithm for flowshop robust scheduling under machine breakdown uncertainty

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INTERNATIONAL JOURNAL OF COMPUTER INTEGRATED MANUFACTURING 2016年第7期29卷 709-719页

作者： Fazayeli, Mohammad Aleagha, Mohammad-Reza Bashirzadeh, Reza Shafaei, Rasoul KNT Univ Technol Dept Ind Engn Tehran Iran

One of the most important assumptions in production scheduling is permanent availability of the machines without any breakdown. In real-world scheduling problems, machines could be unavailable due to various reasons such as preventive maintenance and unpredicted breakdowns. In this paper, a flowshop scheduling problem under machine breakdown uncertainty is studied. The machines are subject to breakdown in practice caused by components' wear-out. A proactive scheduling is considered to deal with unpredictable machine breakdown. An effective hybrid meta-heuristic algorithm based on genetic and simulated annealing algorithms is proposed to tackle such an NP-hard problem. To evaluate the performance of the proposed algorithm, its performance in terms of maximising the beta-robustness of makespan was compared with six other heuristic and meta-heuristic algorithms. Computational results confirm that the proposed algorithm outperforms the others.

关键词： flowshop robust schedule meta-heuristic algorithm uncertainty machine breakdown beta-robustness

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A fast two-stage hybrid meta-heuristic algorithm for robust corridor allocation problem

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ADVANCED ENGINEERING INFORMATICS 2022年第0期53卷

作者： Zhang, Zeqiang Gong, Juhua Liu, Junqi Chen, Feng Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Peoples R China Technol & Equipment Rail Transit Operat & Mainten Chengdu 610031 Peoples R China

The optimisation of the corridor allocation problem (CAP) belongs to the optimisation of the efficiency of the automated production line. The goal is to reduce the material handling cost (MHC) in the production process through a reasonable layout of the facilities, so as to save expenses for the enterprise. In recent years, with the acceleration of market changes, product design and production process adjustments have become more frequent, and more attention has been paid to the research on the layout of facilities under the condition of changes in the flow of materials between production facilities over time. On the basis of the CAP model, this paper considers the optimisation problem of row layout when the flow of materials between facilities fluctuates in a certain range. The new model can be utilised to obtain the overall optimisation solution under the condition of the floating material flow matrix, so as to achieve the goal of optimising the total MHC in the entire production process. As the new model introduces more variables and intermediate parameters, a two-stage solution method is previously required, which greatly increases the time to solve the problem. This paper proposes a targeted meta-heuristic algorithm optimisation method combining the advantages of tabu search algorithm and harmony search algorithm, which simplifies the solution phase of calling the precise solver in the two-stage algorithm of row facility layout problem, improves the problem solving efficiency, and makes the solution of large-scale problems become possible. The proposed model is verified through Lingo software, and then the model and the hybrid algorithm in the MATLAB environment are verified with each other. Finally, the proposed simplified algorithm is utilised to solve the large-scale problems that could not be solved by the two-stage algorithm before.

关键词： Facility layout problem Corridor allocation problem considering robustness(rCAP) meta-heuristic algorithm Simplified two-stage algorithm

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A new energy-aware tasks scheduling approach in fog computing using hybrid meta-heuristic algorithm

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2020年 143卷 88-96页

作者： Hosseinioun, Pejman Kheirabadi, Maryam Tabbakh, Seyed Reza Kamel Ghaemi, Reza Islamic Azad Univ Dept Comp Engn Neyshabur Branch Neyshabur Iran Islamic Azad Univ Dept Comp Engn Mashhad Branch Mashhad Razavi Khorasan Iran Islamic Azad Univ Dept Comp Engn Quchan Branch Quchan Iran

In recent years, large computational problems have been solved by the distributed environment in which applications are executed in parallel. Also, lately, fog computing or edge computing as a new environment is applied to collect data from the devices and preprocessing is done before sending for main processing in cloud computing. Since one of the crucial issues in such systems is task scheduling, this issue is addressed by considering reducing energy consumption. In this study, an energy-aware method is introduced by using the Dynamic Voltage and Frequency Scaling (DVFS) technique to reduce energy consumption. In addition, in order to construct valid task sequences, a hybrid Invasive Weed Optimization and Culture (IWO-CA) evolutionary algorithm is applied. The experimental results revealed that the proposed algorithm improves some current algorithms in terms of energy consumption. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Fog computing Task scheduling Energy consumption meta-heuristic algorithm DVFS

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Snow Geese algorithm: A novel migration-inspired meta-heuristic algorithm for constrained engineering optimization problems

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APPLIED MATHEMATICAL MODELLING 2024年 126卷 327-347页

作者： Tian, Ai-Qing Liu, Fei-Fei Lv, Hong-Xia Southwest Jiaotong Univ Sch Transportat & Logist Chengdu 610031 Peoples R China Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China

This paper proposes a novel nature-inspired meta-heuristic algorithm, named Snow Geese algorithm. It is inspired by the migratory behavior of snow geese and emulates the distinctive "Herringbone" and "Straight Line" shaped flight patterns observed during their migration. The algorithm is structured into three main phases for benchmark testing. In the first phase, the Snow Geese algorithm's numerical results are compared with those of several classical meta-heuristic algorithms using the same test functions and original data from these algorithms. In the second phase, in order to minimize potential variations during the comparison, all algorithms undergo evaluation on a standardized testing platform. In the third phase, this paper applies the Snow Geese algorithm to solve four widely recognized engineering optimization problems: the tubular column design, piston lever optimization design, reinforced concrete beam design and car side impact design. These real-world engineering problems serve as test cases to assess Snow Geese algorithm problem-solving capabilities. The primary objective of the Snow Geese algorithm is to provide an alternative perspective for tackling complex optimization problems. Please note that the complete source code for the Snow Geese algorithm is publicly available at https:// github .com /stones3421 /SGA -project.

关键词： meta-heuristic algorithm Snow Geese algorithm Herringbone Straight line Engineering optimization problems

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Five Phases algorithm: A Novel meta-heuristic algorithm and Its Application on Economic Load Dispatch Problem

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JOURNAL OF INTERNET TECHNOLOGY 2023年第4期24卷 837-848页

作者： Wang, Xiaopeng Chu, Shu-Chuan Snasel, Vaclav Shehadeh, Hisham A. Pan, Jeng-Shyang Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao Peoples R China VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Ostrava Czech Republic Amman Arab Univ Dept Comp Informat Syst & Technol & Comp Sci Amman Jordan Chaoyang Univ Technol Dept Informat Management Taichung Taiwan

A new meta-heuristic algorithm named the five phases algorithm (FPA) is presented in this paper. The proposed method is inspired by the five phases theory in traditional Chinese thought. FPA updates agents based on the generating and overcoming strategy as well as learning strategy from the agent with the same label. FPA has a simple structure but excellent performance. It also does not have any predefined control parameters, only two general parameters including population size and terminal condition are required. This provides flexibility to users to solve different optimization problems. For global optimization, 10 test functions from the CEC2019 test suite are used to evaluate the performance of FPA. The experimental results confirm that FPA is better than the 6 state-of-the-art algorithms including particle swarm optimization (PSO), grey wolf optimizer (GWO), multi-verse optimizer (MVO), differential evolution (DE), backtracking search algorithm (BSA), and slime mould algorithm (SMA). Furthermore, FPA is applied to solve the Economic Load Dispatch (ELD) from the real power system problem. The experiments give that the minimum cost of power system operation obtained by the proposed FPA is more competitive than the 14 counterparts. The source codes of this algorithm can be found in https://***/matlabcentral/ fileexchange/118215-five-phases-algorithm-fpa.

关键词： meta-heuristic algorithm Optimization problem Five phases algorithm Economic load dispatch

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A new meta-heuristic algorithm: Vibrating particles system

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SCIENTIA IRANICA 2017年第2期24卷 551-566页

作者： Kaveh, A. Ghazaan, M. Ilchi Iran Univ Sci & Technol Ctr Excellence Fundamental Studies Struct Engn POB 16846-13114 Tehran Iran Iran Univ Sci & Technol Sch Civil Engn POB 16846-13114 Tehran Iran

In this paper, a new meta-heuristic algorithm based on free vibration of single degree of freedom systems with viscous damping is introduced, and it is called Vibrating Particles System (VPS). The solution candidates are considered as particles that gradually approach their equilibrium positions. Equilibrium positions are achieved from the current population and historically best position in order to have a proper balance between diversification and intensification. To evaluate the performance of the proposed method, it is applied to sizing optimization of four skeletal structures including trusses and frames. Results show that the proposed algorithm is a robust and reliable method. (C) 2017 Sharif University of Technology. All rights reserved.

关键词： Vibrating particles system meta-heuristic algorithm Global optimization Optimal design Truss structures Frame structures

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