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teaching-learning guided salp swarm algorithm for global optimization tasks and feature selection

SOFT COMPUTING 2023年第23期27卷 17887-17908页

作者： Li, Jun Ren, Hao Chen, Huiling Li, ChenYang Wenzhou Univ Key Lab Intelligent Informat Safety & Emergency Zh Wenzhou 325035 Peoples R China

The basic salp swarm algorithm (SSA) is a novel nature-inspired swarm intelligence optimization algorithm based on the foraging behavior of salp individuals in the deep sea. Since its development, the salp swarm algorithm has attracted widespread interest from scholars both at home and abroad for solving complex real-world practical problems. With continuous research, the SSA algorithm has revealed some shortcomings such as slow convergence speed and low accuracy. To enhance the optimization capability of the algorithm, in this paper, we propose an improved hybrid algorithm called TLSSA based on two phases of the teaching-learning-based optimization method: the teaching phase and the learner phase. In the teaching phase, students' ability is improved by updating the difference between the teacher and the class average level, which helps to improve the overall learning ability of the salp population, resulting in higher quality solutions. In the learning phase, by simulating the discussion, statement, and communication between students, the average level of the individual is improved, and the global search speed of the algorithm is accelerated. To verify the effectiveness and competitiveness of the proposed method, it is first tested on 30 IEEE CEC 2017 benchmark functions. The test results demonstrate that the proposed TLSSA method obtains better overall performance compared to 8 mainstream meta-heuristics and 8 advanced algorithms. After that, we applied the proposed method to solve two classical real-world engineering design problems and feature selection. Again, the experimental results show that our method has significant advantages over the traditional methods in solving these practical problems. The remarkable performance of our proposed improved TLSSA algorithm in solving theoretical and practical complex optimization problems also provides potential possibilities for applying more intelligent optimization algorithms to solve complex optimization problems in

关键词： Salp swarm algorithm teaching-learning-based optimization Engineering design Feature selection

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INNA: An improved neural network algorithm for solving reliability optimization problems

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NEURAL COMPUTING & APPLICATIONS 2022年第23期34卷 20865-20898页

作者： Kundu, Tanmay Garg, Harish Chandigarh Univ Dept Math Mohali 140413 Punjab India Thapar Inst Engn & Technol Sch Math Patiala 147004 Punjab India

The main objective of this paper is to present an improved neural network algorithm (INNA) for solving the reliability-redundancy allocation problem (RRAP) with nonlinear resource constraints. In this RRAP, both the component reliability and the redundancy allocation are to be considered simultaneously. Neural network algorithm (NNA) is one of the newest and efficient swarm optimization algorithms having a strong global search ability that is very adequate in solving different kinds of complex optimization problems. Despite its efficiency, NNA experiences poor exploitation, which causes slow convergence and also restricts its practical application of solving optimization problems. Considering this deficiency and to obtain a better balance between exploration and exploitation, searching procedure for NNA is reconstructed by implementing a new logarithmic spiral search operator and the searching strategy of the learner phase of teaching-learning-based optimization (TLBO) and an improved NNA has been developed in this paper. To demonstrate the performance of INNA, it is evaluated against seven well-known reliability optimization problems and finally compared with other existing meta-heuristics algorithms. Additionally, the INNA results are statistically investigated with the Wilcoxon sign-rank test and Multiple comparison test to show the significance of the results. Experimental results reveal that the proposed algorithm is highly competitive and performs better than previously developed algorithms in the literature.

关键词： Neural network algorithm teaching-learning-based optimization Constrained optimization Reliability redundancy allocation problem

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An effective PSO-TLBO algorithm for multi-objective optimization

An effective PSO-TLBO algorithm for multi-objective optimiza...

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IEEE Congress on Evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Cheng, Tingli Chen, Minyou Fleming, Peter J. Yang, Zhile Gan, Shaojun Chongqing Univ Sch Elect Engn Chongqing Peoples R China Univ Sheffield Dept Automat Control & Syst Engn Sheffield S10 2TN S Yorkshire England Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast BT7 1NN Antrim North Ireland

ISBN: (纸本)9781509006229

In this study, we present an effective hybrid algorithm based on particle swarm optimization (PSO) and teaching-learning-based optimization (TLBO), called PSO-TLBO, to solve multi-objective optimization (MOO) problems. In PSO-TLBO, teacher phase and learner phase in TLBO are incorporated to a PSO to improve the exploration and exploitation ability. Another feature of PSO-TLBO is the improved circular crowed sorting (CCS) to truncate the external archive which stores the nondominated solutions found during the search. Moreover, the polynomial mutation is used only when the velocities of all solutions are very small. The performance of PSO-TLBO algorithm for MOO is tested on ZDT and DTLZ benchmarks functions and compared with some powerful MOO algorithms in terms of both convergence and spread performance. Also, the contribution of CCS is compared with crowded sorting in NSGA-II. Comparison results reveal the powerful ability of PSO-TLBO to obtain remarkable solutions in terms of inverted generational distance (IGD) metrics.

关键词： Multi-objective optimization particle swarm optimization teaching-learning-based optimization

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Meta-heuristic based optimization Techniques for the Steady-state Analysis of Self-excited Induction Generator 2

Meta-heuristic based Optimization Techniques for the Steady-...

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International Conference on Inventive Communication and Computational Technologies (ICICCT)

作者： Saha, Swarup Kumar Sandhu, Kanwarjit Singh NIT Kurukshetra Sch Renewable Energy & Efficiency Kurukshetra Haryana India NIT Kurukshetra Dept Elect Engn Kurukshetra Haryana India

ISBN: (纸本)9781538619742

The self-excited induction generator is found to be the most suitable generator in wind turbine applications for the extraction of electrical energy from the moving wind. This machine has the advantage of generating electrical energy from the variable wind speed. This paper aims to the use of different optimization algorithms for the analysis of the steady-state performance of the Self-excited induction generator (SEIG) feeding a resistive load. The nodal admittance method is used to build up the equivalent circuit equations of the SEIG which are highly non-linear in nature. These nonlinear equations are solved by the three optimization techniques such as teaching learning-based optimization (TLBO), Genetic algorithm (GA), and Particle Swarm optimization (PSO). The generated frequency and the magnetization reactance of the machine are found out and also the obtained simulated results are compared with the experimental data.

关键词： Self-excited induction generator teaching-learning-based optimization Particle Swarm optimization

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A hybrid evolutionary algorithm for solving function optimization problems 12

A hybrid evolutionary algorithm for solving function optimiz...

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12th International Conference on Computational Intelligence and Security (CIS)

作者： Gu, Fahui Li, Kangshun Liu, Yue South China Agr Univ Coll Math & Informat Guangzhou 510006 Guangdong Peoples R China Jiangxi Appl Technol Vocat Coll Sch Informat Engn Ganzhou 341000 Peoples R China

ISBN: (纸本)9781509048403

This paper proposes a hybrid evolutionary algorithm based on DE and TLBO (HDT). The HDT algorithm independently evolved the two sub populations of DE and TLBO to preserve the diversity of population. In every generation of evolution, we first get a composite individual from current best individuals of the two sub populations, then we use search guide of composite individual to remain the variety of population and to achieve complementary advantages of DE and TLBO. Experiments have been conducted on 13 benchmark problems. Empirical results fully demonstrate the effectiveness and competitiveness of the proposed hybrid evolutionary algorithm.

关键词： global optimization evolutionary algorithm differential evolutionary teaching-learning-based optimization

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An optimization method of active distribution network considering uncertainties of renewable DGs 10

An optimization method of active distribution network consid...

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10th International Conference on Applied Energy (ICAE)

作者： Yong, Chengsi Kong, Xiangyu Chen, Ying Xu, Quan Yu, Li Tianjin Univ Minist Educ Key Lab Smart Grid Tianjin Peoples R China China Southern Power Gnat Elect Power Res Inst Guangzhou 510080 Guangdong Peoples R China

With the access of the renewable DGs such as wind turbines and photovoltaic generations, network operation state is uncertain due to the randomness of these renewable DGs. This paper proposes a novel optimal method of the active distribution network (ADN) considering the uncertain conditions and the coordination control of source-network-load. By optimizing the controllable distributed power output, controlling the network switches, and managing the demand-side load synchronously, the impact of distributed renewable energies can be reduced and the reliable operation of ADN can be ensured. The chance constrained programming is used to deal with the uncertainties. The proposed model is settled by the improved teaching-learning-based optimization algorithm (ITLBO) and the performance of the algorithm is verified by the comparison with the TLBO in the modified IEEE 33-bus distribution system. (C) 2019 The Authors. Published by Elsevier Ltd.

关键词： Active distribution network distributed renewable energy teaching-learning-based optimization chance-constrained programming

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A novel technique based on artificial intelligence for modeling the required temperature of a solar bread cooker equipped with concentrator through experimental data

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FOOD AND BIOPRODUCTS PROCESSING 2020年 123卷 437-449页

作者： Nazari, Saeed Karami, Alimohammad Bahiraei, Mehdi Olfati, Mohammad Goodarzi, Marjan Khorasanizadeh, Hossein Razi Univ Dept Mech Engn Kermanshah Iran Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Natl Iranian Gas Co NIGC Tech & Engn Dept Kermanshah Iran Ton Duc Thang Univ Fac Environm & Labour Safety Sustainable Management Nat Resources & Environm R Ho Chi Minh City Vietnam Univ Kashan Dept Mech Engn Kashan Iran Univ Kashan Energy Res Inst Kashan Iran

In the present study, a combination of the modified sine and cosine algorithm (MSCA) and teaching-learning-based optimization (TLBO) algorithm is integrated with the neuro-fuzzy system to obtain three hybrid models. The proposed model predicts the effects of design parameters including the bottom galvanized protective edge of the cooking plate, top insulator cap of the cooking plate, position of the cooking plate, time duration of bread cooking and weather conditions on the dependent parameter which is the required temperature of a solar bread cooker equipped with a concentrator. For this purpose, the networks are trained on the basis of the experimentally measured data. The goal is to assess the ability of the hybrid networks for modeling cooking plate required based on the input variables. The quality of the bread produced by the solar cooker is evaluated by proper selection of the design parameters. The results show twelve breads per hour each with 200 g weight of dough can be produced by the cooker for at least six hours in every sunny day in eight months of the year, and also, the best hybrid network predicts the results with a low error which guarantees the performance of the applied hybrid model. (C) 2020 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： Solar bread cooker Cooking plate Design parameters Hybrid Model teaching-learning-based optimization Algorithm Experimental study

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Parameter estimation of nonlinear chaotic system by improved TLBO strategy

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SOFT COMPUTING 2016年第12期20卷 4965-4980页

作者： Zhang, Hongjun Li, Baozhu Zhang, Jun Qin, Yuanhui Feng, Xiaoyi Liu, Bo Chinese Acad Sci Acad Math & Syst Sci Beijing 100190 Peoples R China Syst Engn Res Inst Beijing 100094 Peoples R China

Estimation of parameters of chaotic systems is a subject of substantial and well-developed research issue in nonlinear science. From the viewpoint of optimization, parameter estimation can be formulated as a multi-modal constrained optimization problem with multiple decision variables. This investigation makes a systematic examination of the feasibility of applying a newly proposed population-based optimization method labeled here as teaching-learning-based optimization (TLBO) to identify the unknown parameters for a class of chaotic system. The preliminary test demonstrates that despite its global fast coarse search capability, teaching-learning-based optimization often risks getting prematurely stuck in local optima. To enhance its fine (local) searching performance of TLBO, Nelder-Mead simplex algorithm-based local improvement is incorporated into TLBO so as to continually search for the global optima through the reflection, expansion, contraction, and shrink operators. Working with the well-established Lorenz system, we assess the effectiveness and efficiency of the proposed improved TLBO strategy. The empirical results indicate the success of the proposed hybrid approach in which the global exploration and the local exploitation are well balanced, providing the best solutions for all instances used over other state-of-the-art metaheuristics for chaotic identification in literature, including particle swarm optimization, genetic algorithm, and quantum-inspired evolutionary algorithm.

关键词： Parameter estimation System identification Chaotic system teaching-learning-based optimization Nelder-Mead simplex algorithm Memetic algorithm

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An optimal extreme learning-based classification method for power quality events using fractional Fourier transform

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NEURAL COMPUTING & APPLICATIONS 2021年第10期33卷 4979-4995页

作者： Samanta, Indu Sekhar Rout, Pravat Kumar Mishra, Satyasis Centurion Univ Dept Elect & Commun Engn Paralakhemundi India Siksha O Anusandhan Univ Dept Elect & Elect Engn Bhubaneswar Odisha India

Mitigation of power quality events (PQEs) needs an accurate, faster, and efficient detection and classification technique for designing the compensating devices as a remedial measure to the problem. This study motivates on this issue to formulate a better technique based on fractional Fourier transform (FRFT) and extreme learning machine (ELM). FRFT is considered for relevant feature extraction due to its characteristics like enhanced order control and capability to provide time, frequency, and intermediate time-frequency depictions for any non-stationary power signals. The possibility of multi-domain feature extraction capability due to its easy order control arrives at a robust feature matrix, which makes the classification more accurate. An optimal ELM-based classifier is designed by tuning its system parameters applying modified teaching-learning-based optimization (MTLBO). This optimal ELM is implemented in this study along with FRFT to formulate the proposed approach denoted as FRFT-MTLBO-ELM. To give a good reason for the enhanced performance of the proposed technique, noisy and hybrid synthetic signals of ten PQEs are generated and tested considering fully all real-time condition cases. At last, a comparative result is presented with other applied signal processing-based approaches and it is found that the proposed FRFT-MTLBO-ELM approach is very effective and comparatively better justifying its real-time implementation in the monitoring systems.

关键词： Time-frequency analysis Signal processing techniques PQ events Fractional Fourier transform (FRFT) Extreme learning machine (ELM) teaching-learning-based optimization

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A novel intrusion detection system for internet of things devices and data

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WIRELESS NETWORKS 2024年第1期30卷 285-294页

作者： Kaushik, Ajay Al-Raweshidy, Hamed Brunel Univ London Dept Elect & Elect Engn London England

As we enter the new age of the Internet of Things (IoT) and wearable gadgets, sensors, and embedded devices are extensively used for data aggregation and its transmission. The extent of the data processed by IoT networks makes it vulnerable to outside attacks. Therefore, it is important to design an intrusion detection system (IDS) that ensures the security, integrity, and confidentiality of IoT networks and their data. State-of-the-art IDSs have poor detection capabilities and incur high communication and device overhead, which is not ideal for IoT applications requiring secured and real-time processing. This research presents a teaching-learning-based optimization enabled intrusion detection system (TLBO-IDS) which effectively protects IoT networks from intrusion attacks and also ensures low overhead at the same time. The proposed TLBO-IDS can detect analysis attacks, fuzzing attacks, shellcode attacks, worms, denial of service (Dos) attacks, exploits, and backdoor intrusion attacks. TLBO-IDS is extensively tested and its performance is compared with state-of-the-art algorithms. In particular, TLBO-IDS outperforms the bat algorithm and genetic algorithm (GA) by 22.2% and 40% respectively.

关键词： Data security Internet of Things Intrusion detection Machine learning teaching-learning-based optimization

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