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A binary tree seed algorithm with selection-based local search mechanism for huge-sized optimization problems

APPLIED SOFT COMPUTING 2022年第0期129卷

作者： Karakoyun, Murat Ozkis, Ahmet Necmettin Erbakan Univ Fac Engn Comp Engn Dept Konya Turkey Necmettin Erbakan Univ Fac Engn Comp Forens Engn Dept Konya Turkey

tree seed algorithm (TSA) is a recently proposed metaheuristic algorithm for solving continuous optimization problems. In order to use TSA in binary optimization problems, the SimLogicTSA method was developed by adding logic gates and Jaccard's similarity measure to this algorithm by Cinar and Kiran. Although SimLogicTSA is generally successful in small, medium, and large size problems, it has not been successful in the huge-sized problems by stucking into local minima. To overcome this problem, a new local search mechanism called enhanced local search module (ELSM) is proposed and the SimLogicTSA-ELSM algorithm is suggested by implementing the ELSM mechanism to the original SimLogicTSA algorithm. The proposed ELSM mechanism consists of a swap operator and logic-based gates. To analyze the contribution of the ELSM mechanism to the algorithm, firstly, the original SimLogicTSA and SimLogicTSA-ELSM algorithms were compared on the Cap and M* problem sets. The obtained results showed that the proposed algorithm produced more successful results than the original SimLogicTSA. Then, the proposed SimLogicTSA-ELSM is compared with many state-of -art algorithms in the literature by using different performance metrics on Cap and M* problem sets. The results show that SimLogicTSA-ELSM outperforms the compared algorithms in nearly all cases. Especially, the performance of the SimLogicTSA-ELSM stands out in huge-sized problems. (C) 2022 Elsevier B.V. All rights reserved.

关键词： tree seed algorithm Binary optimization Uncapacitated facility location problem Local search mechanism

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An efficient tree seed inspired algorithm for parameter estimation of Photovoltaic models

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ENERGY REPORTS 2022年 8卷 291-298页

作者： Beskirli, Ayse Dag, Idiris Eskisehir Osmangazi Univ Dept Comp Engn TR-26040 Eskisehir Turkey Burdur Mehmet Akif Ersoy Univ Dept Comp Engn TR-15000 Burdur Turkey

Energy is one of the most important requirements for countries and serves as an indicator of the development level of a country. Developments in social, economic and industrial areas have increased the energy needs of countries. Solar energy is one of the most promising renewable energy sources to meet increased energy demands. Photovoltaic (PV) models used to generate electricity from solar energy are among the most popular renewable energy systems. To obtain maximum efficiency from PV models, it is very important to estimate parameters based on current-voltage data, simulate and control PV models for electricity generation. To achieve maximum efficiency for PV models, their parameters should be estimated in an optimum way. Therefore, tree seed algorithm (TSA) developed inspired by nature was proposed in this study. TSA is a meta-heuristic algorithm inspired by the connection between trees and seeds and proposed for solving continuous optimization problems. Parameters of three different PV models (single diode model, double diode model and PV module) were optimized with TSA. Various analyses were performed to prove the success of the proposed method in parameter optimization of PV models. The results were also compared with those of similar studies in the literature. The analysis results indicated that the proposed method yielded very successful, effective and reliable results, and it was concluded that it was a competitive method compared to other methods in the literature. (C) 2021 The Author(s). Published by Elsevier Ltd.

关键词： tree seed algorithm Photovoltaic models Parameter estimation Optimization

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Solving continuous optimization problems using the tree seed algorithm developed with the roulette wheel strategy

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 170卷 114579-114579页

作者： Beskirli, Mehmet Sirnak Univ Dept Comp Engn TR-73000 Sirnak Turkey

The tree seed algorithm (TSA) is a metaheuristic algorithm inspired by the relationship between trees and seeds. It has been proposed for very low-dimensional optimization problems and achieved promising results compared to other optimization algorithms. However, it has been determined that the performance of the TSA is lower than other algorithms for high-dimensional problems. This is due to the fact that TSA cannot scan the local optimum and search space effectively. A new TSA based on the roulette wheel strategy (R-TSA) has been proposed in this study to eliminate this disadvantage and solve high-dimensional problems. With this strategy, the trees selected at the seed production phase of TSA were diversified and the locations of the seeds were updated to prevent it from being stuck in local minima, with the aim of scanning the search space more effectively. The R-TSA was applied to high-dimensional (20, 50 and 100) benchmark functions and both convergence and box-plot graphs were obtained by using the results of these functions. Moreover, current algorithms in published literature were applied to these functions and the results obtained were compared with the R-TSA. It was observed from the analysis results that the performance of the R-TSA was higher than that of TSA.

关键词： tree seed algorithm Roulette wheel strategy Optimization Benchmark functions Metaheuristic algorithms

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Parameter Identification and State-of-Charge Estimation for Li-Ion Batteries Using an Improved tree seed algorithm

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2019年第8期E102D卷 1489-1497页

作者： Chen, Weijie Cai, Ming Tan, Xiaojun Wei, Bo Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou Guangdong Peoples R China Waseda Univ Grad Sch Fundamental Sci & Engn Tokyo 1698555 Japan

Accurate estimation of the state-of-charge is a crucial need for the battery, which is the most important power source in electric vehicles. To achieve better estimation result, an accurate battery model with optimum parameters is required. In this paper, a gradient-free optimization technique, namely tree seed algorithm (TSA), is utilized to identify specific parameters of the battery model. In order to strengthen the search ability of TSA and obtain more quality results, the original algorithm is improved. On one hand, the DE/rand/2/bin mechanism is employed to maintain the colony diversity, by generating mutant individuals in each time step. On the other hand, the control parameter in the algorithm is adaptively updated during the searching process, to achieve a better balance between the exploitation and exploration capabilities. The battery state-of-charge can be estimated simultaneously by regarding it as one of the parameters. Experiments under different dynamic profiles show that the proposed method can provide reliable and accurate estimation results. The performance of conventional algorithms, such as genetic algorithm and extended Kalman filter, are also compared to demonstrate the superiority of the proposed method in terms of accuracy and robustness.

关键词： parameter identification optimizing algorithm tree seed algorithm differential evolution swarm intelligence

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A discrete tree-seed algorithm for solving symmetric traveling salesman problem

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2020年第4期23卷 879-890页

作者： Cinar, Ahmet Cevahir Korkmaz, Sedat Kiran, Mustafa Servet Turkish State Meteorol Serv Konya Div TR-42090 Konya Turkey Konya Tech Univ Fac Engn & Nat Sci Dept Comp Engn TR-42075 Konya Turkey

tree-seed algorithm (TSA) is a recently developed nature inspired population-based iterative search algorithm. TSA is proposed for solving continuous optimization problems by inspiring the relations between trees and their seeds. The constrained and binary versions of TSA are present in the literature but there is no discrete version of TSA which decision variables represented as integer values. In the present work, the basic TSA is redesigned by integrating the swap, shift, and symmetry transformation operators in order to solve the permutation-coded optimization problems and it is called as DTSA. In the basic TSA, the solution update rules can be used for the decision variables whose are defined in continuous solution space, this rules are replaced with the transformation operators in the proposed DTSA. In order to investigate the performance of DTSA, well-known symmetric traveling salesman problems are considered in the experiments. The obtained results are compared with well-known metaheuristic algorithms and their variants, such as Ant Colony Optimization (ACO), Genetic algorithm (GA), Simulated Annealing (SA), State Transition algorithm (STA), Artificial Bee Colony (ABC), Black Hole (BH), and Particle Swarm Optimization (PSO). Experimental results show that DTSA is another qualified and competitive solver on discrete optimization. (C) 2019 Karabuk University. Publishing services by Elsevier B.V.

关键词： tree seed algorithm Transformation operator Traveling salesman problem Discrete optimization Metaheuristic

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Performance Evaluation of Radial Basis Function Networks Based on tree seed algorithm

Performance Evaluation of Radial Basis Function Networks Bas...

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IEEE International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Muneeswaran, V. Rajasekaran, M. Pallikonda Kalasalingam Univ Dept Elect & Instrumentat Engn Krishnankoil Tamil Nadu India Kalasalingam Univ Krishnankoil Tamil Nadu India

ISBN: (纸本)9781509012770

This paper develops a Radial Basis Function Neural Network (RBFN) based on tree seed Optimization algorithm (TSA). The values of clustering centers, width and weights of the Radial Basis Function Neural Network are optimized by tree seed algorithm. The proposed Radial Basis Function Neural Network optimization algorithm is tested on the application of numerical function approximation. The experimental result shows that the optimization of Radial Basis Function Neural Network with tree seed algorithm has improved significance in attaining the faster convergence and also the extent of fitness has been improved.

关键词： Radial basis function neural network Optimization function approximation tree seed algorithm

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Performance Evaluation of Radial Basis Function Networks Based on tree seed algorithm

Performance Evaluation of Radial Basis Function Networks Bas...

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International Conference on Circuits, Power and Computing Technologies

作者： V. Muneeswaran M. Pallikonda Rajasekaran Department of Electronics and Instrumentation Engineering Kalasalingam University Tamilnadu Controller of Examinations Kalasalingam University Tamilnadu

ISBN: (纸本)9781509012787

关键词： Radial basis function neural network Optimization Function approximation tree seed algorithm

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Advanced tree-seed optimization based fractional-order PID controller design for simplified decoupled industrial tank systems

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INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS 2024年第2期37卷

作者： Kottayathu Rajagopalan, Achu Govind Mahapatra, Subhasish Mahapatro, Soumya Ranjan VIT AP Univ Sch Elect Engn Vijayawada 522237 Andhra Pradesh India Vellore Inst Technol Sch Elect Engn Chennai India

Controlling coupled tank systems is challenging due to interactions between tanks, nonlinear dynamics, time delays, uncertainties, and cross-coupling effects. The design of effective control strategies to address these complexities while ensuring stability and robust performance is difficult. Hence, this study focuses on presenting an innovative approach to enhance level control in coupled tank systems by employing a fractional-order proportional-integral-derivative (FOPID) controller. The FOPID controller is designed by imposing constraints on the performance metric and closed-loop gain. Besides, the defined optimization problem is solved by employing a tree seed algorithm. Further, the stability is analyzed graphically using the singular value analysis. The inherent complexities of coupled tank systems are effectively addressed by designing decouplers. The unique characteristics of the tree seed algorithm to navigate complex solution spaces and its effective handling of constraints offer a robust optimization framework. The validity and efficiency of the proposed method are analyzed in a range of simulation experiments conducted on distinct interconnected tank systems. Besides, the stability is verified graphically. The analysis highlights the effectiveness of the control law in handling uncertainties and disturbances. Besides, the proposed method reduces the settling time to around 20%. Through a systematic integration of optimization and comprehensive stability analysis, the study provides a holistic solution for optimizing level control in coupled tank systems.

关键词： coupled tank systems decentralized control FOPID singular value analysis tree seed algorithm

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Diagnosability-optimized design of unmanned aerial vehicles based on structural analysis and maximum mean covariance differences

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MEASUREMENT 2024年 238卷

作者： Gu, XuPing Shi, Xianjun Naval Aviat Univ Coll Coastal Def Force Yantai 264001 Peoples R China

This study presents a proposed strategy for optimizing diagnosability in unmanned aerial vehicles ( UAVs ) through a combination of Structural Analysis (SA) SA ) and Maximum Mean Covariance Difference (MMCD). MMCD ). Initially, diagnosability is qualitatively assessed using SA. Residuals are then designed based on the minimal structural overdetermination (MSO) MSO ) using the sequential residual generation approach. Subsequently, diagnosability is reframed as a measurement of residual variance in the probability distribution. To address the absence of Maximum Mean Difference (MMD) MMD ) in covariance difference measurement, Maximum Covariance Difference (MCD) MCD ) is introduced. Furthermore, a method for quantitatively evaluating diagnosability based on MMCD is proposed, incorporating both MMD and MCD. Finally, an optimization model for diagnosability is established to ensure the efficient completion of fault diagnostic tasks with minimal cost and maximum effectiveness. The proposed method's rationality and effectiveness are validated through simulation using the tree seed algorithm (TSA) TSA ) on a fixed-wing UAV model.

关键词： Unmanned aerial vehicle Structural analysis Diagnosability Maximum mean covariance difference tree seed algorithm

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