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PV Cell Parameter Extraction Using Data Prediction-Based meta-heuristic algorithm via Extreme Learning Machine

FRONTIERS IN ENERGY RESEARCH 2021年 9卷

作者： Li, Bing Chen, Huang Tan, Tian Shantou Univ Coll Engn Shantou Peoples R China

To reliably evaluate the practical performance and to undertake optimal control of PV systems, a precise PV cell parameter extraction-based accurate modeling of PV cells is extremely crucial. However, its inherent high nonlinear and multimodal characteristics usually hinder conventional optimization methods to obtain a fast and satisfactory performance. Besides, insufficient current-voltage (I-V) data provided by manufacturers cannot guarantee high accuracy and flexibility of PV cell parameter extraction under various operation scenarios. Hence, this article proposes a novel parameter extraction strategy by data prediction-based meta-heuristic algorithm (DPMhA). An extreme learning machine (ELM) is adopted to predict output I-V data from measured data, which can provide a more reliable fitness function to meta-heuristic algorithms (MhAs). Consequently, MhAs can undertake a more stable search for optimal solution through extended I-V data;thus, PV cell parameters can be obtained with high accuracy and convergence rate. Its effectiveness is validated via three typical PV cell models, that is, single diode model (SDM), double diode model (DDM), and three diode model (TDM). Last, comprehensive case studies illustrate that the DPMhA can considerably enhance the accuracy and effectiveness compared with those without data prediction.

关键词： parameter extraction PV cell data prediction meta-heuristic algorithm extreme learning machine

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Sustainable modular product architecture design by Bi-level leader-follower joint optimization with switching-based meta-heuristic algorithm

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JOURNAL OF CLEANER PRODUCTION 2021年 306卷 127108-127108页

作者： Hossain, Md Sanowar Chakrabortty, Ripon K. El Sawah, Sondoss Ryan, Michael J. Univ New South Wales Sch Engn & Informat Technol Canberra ACT 2600 Australia Rajshahi Univ Engn & Technol Dept Ind & Prod Engn Rajshahi Bangladesh

Traditional product architecture design is motivated towards structural-functional modularity by a flat map all-in-one (AIO) approach, providing little emphasis on interface complexity and addressing conflicting goals. The resultant modular product architecture (PA) often reduces design robustness and increases complexity for product assembly and recovery. Sometimes, the generated architecture is inconsistent with efficient final assembly, increasing redesign costs. To overcome such problems, this paper proposes a sustainable modular product architecture (SMPA) to reduce assembly complexity and middle-aged discarding of products by enhancing assemblability and recovery. For achieving sustain ability in modular design, three sustainable modular drivers-namely, product configuration cost, interface complexity and material similarity-are considered along with several architectural drivers. The key challenge for incorporating sustainability consideration into the existing modularization theories and methods are the heterogeneity between different goals and the decision criteria. To deal with this challenge of decision hierarchy, a bi-level leader-follower joint optimization (LFJO) model is formulated as a Stackelberg game, combining the architectural and sustainability factors of modular design. The main objective is to determine the optimal level of module granularity to reduces assembly cost and enhance product recovery within a coherent framework of total product configuration cost (TPCC) and total interface complexity (TIC). A switching based bi-level meta-heuristic algorithm (SBMA) is developed incorporating Genetic algorithm (GA) and Particle Swarm Optimization (PSO) to solve the bi-level LFJO problem. A case study of a refrigerator modular design reveals that the joint optimization of product configuration design and interface complexity excels sustainability characteristics of modular design. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Sustainability Modularization Interface complexity Design structure matrix (DSM) Bi-level programming meta-heuristic algorithm

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Optimal solution to the vehicle routing problem by adopting a meta-heuristic algorithm

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TRANSPORTATION PLANNING AND TECHNOLOGY 2016年第6期39卷 574-585页

作者： Kim, Seung Hyun Bae, Sang Hoon Pukyong Natl Univ Dept Spatial Informat Engn Busan South Korea

The delivery service provided by large-scale retailers continues to grow as online sales occupy an increasingly large share of the market. This study aims to tease out efficient vehicle scheduling times as well as optimal delivery routes by applying meta-heuristic algorithms. Monthly data on existing routes were obtained from a branch of Korea's leading large-scale online retailer. The first task was to examine the status of existing routes by comparing delivery routes created using Dijkstra's algorithm with existing delivery routes and their vehicle scheduling. The second task was to identify optimal delivery routes through a comparative analysis of the genetic algorithm and Tabu search algorithm, known for its superior applicability amongst other meta-heuristic algorithms. These findings demonstrate that the optimal vehicle routing problem not only has the potential to reduce distribution costs for operators and expedite delivery for consumers, but also the added social benefit of reduced carbon emissions.

关键词： Dynamic vehicle routing problem meta-heuristic algorithm tabu search linear optimization cost reduction

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Artificial rabbits optimization: A new bio-inspired meta-heuristic algorithm for solving engineering optimization problems

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年第0期114卷

作者： Wang, Liying Cao, Qingjiao Zhang, Zhenxing Mirjalili, Seyedali Zhao, Weiguo Hebei Univ Engn Sch Water Conservancy & Hydropower Handan 056038 Hebei Peoples R China Univ Illinois Prairie Res Inst Champaign IL 61820 USA Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld 4006 Australia Yonsei Univ YFL Yonsei Frontier Lab Seoul South Korea

In this paper, a new bio-inspired meta-heuristic algorithm, named artificial rabbits optimization (ARO), is proposed and tested comprehensively. The inspiration of the ARO algorithm is the survival strategies of rabbits in nature, including detour foraging and random hiding. The detour foraging strategy enforces a rabbit to eat the grass near other rabbits' nests, which can prevent its nest from being discovered by predators. The random hiding strategy enables a rabbit to randomly choose one burrow from its own burrows for hiding, which can decrease the possibility of being captured by its enemies. Besides, the energy shrink of rabbits will result in the transition from the detour foraging strategy to the random hiding strategy. This study mathematically models such survival strategies to develop a new optimizer. The effectiveness of ARO is tested by comparison with other well-known optimizers by solving a suite of 31 benchmark functions and five engineering problems. The results show that ARO generally outperforms the tested competitors for solving the benchmark functions and engineering problems. ARO is applied to the fault diagnosis of a rolling bearing, in which the back-propagation (BP) network optimized by ARO is developed. The case study results demonstrate the practicability of the ARO optimizer in solving challenging real-world problems. The source code of ARO is publicly available at https://***/aro and https://***/matlacentral/fileexchnnge/110250-artificial-rabbits-optimization-aro.

关键词： Artificial rabbits optimization Nature-inspired algorithm meta-heuristic algorithm Engineering problems Fault diagnosis

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The influence of samples on meta-heuristic algorithm for parameter estimation of chaotic system

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MODERN PHYSICS LETTERS B 2019年第4期33卷

作者： Peng, Yuexi Sun, Kehui He, Shaobo Alamodi, O. A. Cent South Univ Sch Phys & Elect Changsha 410083 Hunan Peoples R China

Parameter estimation of chaotic system is an important issue in nonlinear science. The meta-heuristic algorithm is one of the effective estimation approaches, and has received increasing attention. However, few people have noticed the influence of the samples on parameter estimation. In fact, when using meta-heuristic algorithm for parameter estimation, the number of samples will affect the efficiency greatly. In this paper, this problem is investigated. The relationship between sample and step size, and the factors that affect the difficulty of parameter estimation are also considered. Experimental results show that it is more efficient to set the samples to a small number, and the number of estimated parameters is the most important factor affecting the difficulty of parameter estimation. Finally, to improve the precision further, a new hybrid chaotic particle swarm optimization algorithm which combines a special inertia weight with chaotic search is proposed. Results demonstrate that the new hybrid algorithm is more effective than the other four meta-heuristic algorithms.

关键词： Parameter estimation samples chaotic system meta-heuristic algorithm particle swarm optimization

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A new meta-heuristic optimization algorithm using star graph

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SMART STRUCTURES AND SYSTEMS 2017年第1期20卷 99-114页

作者： Gharebaghi, Saeed Asil Kaveh, Ali Asl, Mohammad Ardalan KN Toosi Univ Technol Dept Civil Engn Tehran Iran Iran Univ Sci & Technol Ctr Excellence Fundamental Studies Struct Engn Tehran 16 Iran

In cognitive science, it is illustrated how the collective opinions of a group of individuals answers to questions involving quantity estimation. One example of this approach is introduced in this article as Star Graph (SG) algorithm. This graph describes the details of communication among individuals to share their information and make a new decision. A new labyrinthine network of neighbors is defined in the decision-making process of the algorithm. In order to prevent getting trapped in local optima, the neighboring networks are regenerated in each iteration of the algorithm. In this algorithm, the normal distribution is utilized for a group of agents with the best results (guidance group) to replace the existing infeasible solutions. Here, some new functions are introduced to provide a high convergence for the method. These functions not only increase the local and global search capabilities but also require less computational effort. Various benchmark functions and engineering problems are examined and the results are compared with those of some other algorithms to show the capability and performance of the presented method.

关键词： meta-heuristic algorithm global optimization graph theory optimal design truss structures frame structures

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A New meta-heuristic Optimization algorithm Inspired by FIFA World Cup Competitions: Theory and Its Application in PID Designing for AVR System

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JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS 2016年第4期27卷 419-440页

作者： Razmjooy, Navid Khalilpour, Mohsen Ramezani, Mehdi Univ Tafresh Dept Elect Engn Tafresh Iran Islamic Azad Univ Ardabil Branch Young Researchers & Elite Club Ardebil Iran Univ Tafresh Dept Math Tafresh Iran

This paper presents a new optimization algorithm based on human society's intelligent contests. FIFA World Cup is an international association football competition competed by the senior men's national teams. This contest is one of the most significant competitions among the humans in which people/teams try hard to overcome the others to earn the victory. In this competition there is only one winner which has the best position rather than the others. This paper introduces a new technique for optimization of mathematic functions based on FIFA World Cup competitions. The main difficulty of the optimization problems is that each type of them can be interpreted in a specific manner. World Cup Optimization (WCO) algorithm has a number of parameters to solve any type of problems due to defined parameters. For analyzing the system performance, it is applied on some benchmark functions. It is also applied on an optimal control problem as a practical case study to find the optimal parameters of PID controller with considering to the nominal operating points , changes of the AVR system. The main objective of the proposed system is to minimize the steady-state error and also to improve the transient response of the AVR system by optimal PID controller. Optimal values of the PID controller which are achieved by WCO algorithm are then compared with particle swarm optimization and imperialist competitive algorithm in different situations. Finally for illustrating the system capability against the disturbance, it is applied on a generator with disturbance on it and the results are compared by the other algorithms. The simulation results show the excellence of WCO algorithm performance into the nature base and other competitive algorithms.

关键词： meta-heuristic algorithm Optimization Continuous optimization problems World Cup Optimization (WCO) Genetic algorithm PSO algorithm ICA AVR system Optimal PID control

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A novel meta-heuristic optimization algorithm inspired by group hunting of animals: Hunting search

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COMPUTERS & MATHEMATICS WITH APPLICATIONS 2010年第7期60卷 2087-2098页

作者： Oftadeh, R. Mahjoob, M. J. Shariatpanahi, M. Univ Tehran Sch Mech Engn Ctr Mechatron & Automat Tehran Iran

A novel optimization algorithm is presented, inspired by group hunting of animals such as lions, wolves, and dolphins. Although these hunters have differences in the way of hunting, they are common in that all of them look for a prey in a group. The hunters encircle the prey and gradually tighten the ring of siege until they catch the prey. In addition, each member of the group corrects its position based on its own position and the position of other members. If the prey escapes from the ring, hunters reorganize the group to siege the prey again. Several benchmark numerical optimization problems, constrained and unconstrained, are presented here to demonstrate the effectiveness and robustness of the proposed Hunting Search (HuS) algorithm. The results indicate that the proposed method is a powerful search and optimization technique. It yields better solutions compared to those obtained by some current algorithms when applied to continuous problems. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： meta-heuristic algorithm Continuous optimization problems Group hunting

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Trust Based QOS Protocol (TBQP) using meta-heuristic Genetic algorithm for Optimizing and Securing MANET

Trust Based QOS Protocol (TBQP) using Meta-heuristic Genetic...

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International Conference on Optimization, Reliability, and Information Technology (ICROIT)

作者： Zafar, Sherin Soni, M. K. Manav Rachna Int Univ Faridabad Dept Comp Sci & Engg Fac Engn Faridabad Haryana India

ISBN: (纸本)9781479929955

This paper includes a prospective approach of developing a trust based QOS protocol (TBQP) using meta-heuristic genetic algorithm for optimizing and securing MANET. Genetic algorithm will help in maintaining Quality of Service(QOS) by selecting a fittest i.e. shortest route hence providing better performance. Intelligent optimization approaches or meta heuristic algorithms like, genetic algorithm(GA), neural networks(NN) based on artificial intelligence(AI), particle swarm optimization(PSO) technique and simulated annealing(SA) in the recent years have well consigned QOS issues. Ad-hoc networks face the primary defiance of restraining attacks against data, like, unauthorized data modification impersonation etc caused by malicious nodes in the network. This problem is tackled by trust application in our proposed approach which helps in securing ad-hoc networks.

关键词： meta-heuristic algorithm user authentication security challenges confronted by MANET trust

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Competitive Learning: A New meta-heuristic Optimization algorithm

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2018年第8期27卷

作者： Afroughinia, Afshin Moghaddam, Reihaneh Kardehi Islamic Azad Univ Dept Elect Engn Mashhad Branch Mashhad Iran

This work proposes a new powerful meta-heuristic optimization algorithm in education process called Competitive Learning (CLA). The algorithm is benchmarked on 8 well-known test functions, and the results are verified by a comparative study with some meta-heuristic optimization methods including: Imperialist Competitive algorithm (ICA), Teaching-Learning-Based Optimization (TLBO), Grey Wolf algorithm (GWO), Particle Swarm Optimization (PSO) and Genetic algorithm (GA). Analyzing the findings, it is shown that the CLA algorithm is able to provide more accurate results than other well-known meta-heuristic ones. Also, those results applied to famous unimodal and multimodal benchmarks show CLA is efficient in improving accuracy as well as computational speed.

关键词： Optimization meta-heuristic algorithm competitive learning algorithm

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