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Improved butterfly optimisation algorithm based on guiding weight and population restart

JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2021年第1期33卷 127-145页

作者： Guo, Yanju Liu, Xianjie Chen, Lei Hebei Univ Technol Sch Elect Informat Engn Tianjin Peoples R China Tianjin Univ Commerce Sch Informat Engn Tianjin Peoples R China

butterfly optimisation algorithm (BOA) is a kind of meta-heuristic swarm intelligence algorithm based on butterfly foraging strategy, but it still needs to be improved in the aspects of convergence speed and accuracy when solving with high-dimensional optimisation problems. In this paper, an improved butterfly optimisation algorithm is proposed, in which guiding weight and population restart strategy are applied to the original algorithm. By adding guiding weight to the global search equation, the convergence speed and accuracy of the algorithm are improved, and the possibility of jumping out of the local optimal solution is increased by the population restart strategy. In order to verify the performance of the proposed algorithm, 24 benchmark functions commonly used for optimisation algorithm experiments are applied in this paper, including 12 unimodal functions and 12 multimodal functions. Experimental results show that the proposed algorithm improves the convergence speed, accuracy and the ability to jump out of the local optimal solution.

关键词： butterfly optimisation algorithm meta-heuristic swarm intelligence algorithm guiding weight population restart

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Network-aware virtual machine placement using enriched butterfly optimisation algorithm in cloud computing paradigm

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第6期27卷 8557-8575页

作者： Shanmugam, Veeramani Ling, Huo-Chong Gopal, Lenin Eswaran, Sivaraman Chiong, Choo W. R. Curtin Univ Malaysia CDT 250 Miri 98009 Sarawak Malaysia RMIT Univ Vietnam Ho Chi Minh City Vietnam Univ Southampton Malaysia Iskandar Puteri 79100 Johor Malaysia

This article presents a virtual machine placement technique aimed at minimizing power usage in heterogeneous cloud data centers. In this study, an innovative model for minimizing the power usage of a datacenter's network is provided. The Enriched Discrete butterfly Optimization method (EDBOA) is used as a meta-heuristic method in order to achieve an effective mapping of virtual machines (VMs) onto physical machines (PMs). The Reverse Order Filling Method (ROFM) was developed as a solution repair technique to meet the requirements of the BOA. It is used to manipulate the solutions in order to identify potential candidates for more optimum solutions. Furthermore, we constructed VM's that had both Left-Right and Top-Down communication capabilities. Additionally, PM's with limited capacities in terms of CPU, memory, and bandwidth are designed and included for the purpose of testing. The integration of our network power model into the EDBOA algorithms facilitates the calculation of both power modules and network power consumption. A detailed comparative analysis was conducted on our suggested approaches and many other comparable methods. The evaluation findings demonstrate that the offered approaches exhibit strong performance, with the BOA algorithm using the ROFM solution repair surpassing other methods in terms of power usage. The assessment findings also demonstrate the importance of network power usage.

关键词： Reverse order filling butterfly optimisation algorithm Virtual machine placement Network-aware

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Nonlinear system identification using butterfly optimisation algorithm and Hammerstein model

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INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL 2023年第2期42卷 171-179页

作者： Singh, Sandeep Rawat, Tarun Kumar Ashok, Alaknanda Uttarakhand Tech Univ Dept Elect & Commun Engn Dehra Dun Uttarakhand India Maharaja Surajmal Inst Technol New Delhi India Netaji Subhas Univ Technol Dept Elect & Commun Engn Delhi India GB Pant Univ Agr & Technol Dept Elect Engn Pantnagar Uttarakhand India

This paper focuses on the nonlinear system identification using butterfly optimisation algorithm (BOA) optimised with adaptive Hammerstein model which is the cascade of nonlinear second-order Volterra (SOV) and linear finite impulse response (FIR) systems. Generally, gradient-based methods have been applied for solving such problems. However, these methods may face the problem of getting trapped in local minimum solution. In this paper, a novel butterfly optimisation algorithm is used to identify the nonlinear system by using three different models, namely Hammerstein model, memoryless polynomial nonlinear (MPN)-FIR model and SOV model. Furthermore, to measure the accuracy of the employed BOA, mean square error (MSE), coefficient estimation and convergence speed are considered. To prove the efficacy of the proposed BOA, the simulated results have been compared with those of the antlion optimisation algorithm and dragonfly algorithm. The simulated results confirm that Hammerstein model with SOV-FIR optimised with BOA is able to outperform the other models and algorithms.

关键词： nonlinear system identification Hammerstein model meta-heuristic algorithms butterfly optimisation algorithm BOA antlion optimisation algorithm dragonfly algorithm

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A Swish RNN based customer churn prediction for the telecom industry with a novel feature selection strategy

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CONNECTION SCIENCE 2022年第1期34卷 1855-1876页

作者： Sudharsan, R. Ganesh, E. N. Vels Inst Sci Technol & Adv Studies Dept Comp Sci & Engn Chennai Tamil Nadu India Vels Inst Sci Technol & Adv Studies Sch Engn Chennai 600117 Tamil Nadu India

Owing to saturated markets, fierce competition, dynamic criteria, along with introduction of new attractive offers, the considerable issue of customer churn was faced by the telecommunication industry. Thus, an efficient Churn Prediction (CP) model is required for monitoring customer churn. Therefore, this work proposes a novel framework to predict customer churn through a deep learning model namely Swish Recurrent Neural Network (S-RNN). Finally, SRNN is adapted to classify the Churn Customer (CC) and a normal customer. If the result is a churn customer, network utilisation history is analysed for retention process. Whereas, the number of churn customers based on the area network usage is not recognised in this frameworkOwing to saturated markets, fierce competition, dynamic criteria, along with introduction of new attractive offers, the considerable issue of customer churn was faced by the telecommunication industry. Thus, an efficient Churn Prediction (CP) model is required for monitoring customer churn. Therefore, this work proposes a novel framework to predict customer churn through a deep learning model namely Swish Recurrent Neural Network (S-RNN). Finally, S-RNN is adapted to classify the Churn Customer (CC) and a normal customer. If the result is a churn customer, network utilisation history is analysed for retention process.

关键词： Customer churn prediction butterfly optimisation algorithm recurrent neural network (RNN) brownian motion clustering large application swish activation function

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Maiden coordinated load frequency control strategy for ST-AWEC-GEC-BDDG-based independent three-area interconnected microgrid system with the combined effect of diverse energy storage and DC link using BOA-optimised PFOID controller

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IET RENEWABLE POWER GENERATION 2019年第14期13卷 2634-2646页

作者： Latif, Abdul Das, Dulal Chandra Barik, Amar Kumar Ranjan, Sudhanshu NIT Silchar Dept Elect Engn Silchar Assam India

This work presents a maiden approach of coordinated frequency control of novel solar tower (ST)-Archimedes wave energy conversion (AWEC)-geothermal energy conversion (GEC)-biodiesel driven generator (BDDG)-energy storage (ES) units and direct current (DC) links based independent three-area interconnected microgrid system (ImGS). A recent metaheuristic technique, named butterfly optimisation algorithm (BOA) is applied to obtain the optimal gains of the controllers employed with the ImGS and system participation factors. The dynamic performance of proportional-integral derivative (PID), PID with filter (PIDN), proportional-fractional-order integral derivative (PFOID) controllers with their gains tuned by different algorithms such as particle swarm optimisation (PSO), firefly algorithm (FA), whale optimisation algorithm (WOA), and BOA have been compared. Further, the effect of ES units and DC links in all the areas is analysed first time in ImGS. The results have established the superiority of the BOA-based PFOID controllers under different real-world scenarios in terms of frequency deviation, tie-line power, and objective functions. Finally, rigorous sensitivity analysis has been conducted to evaluate the superiority of BOA-optimised PFOID controller towards preserving system stability of ImGS with +/- 25% change in synchronising tie-line coefficients and bias values, and +20% change in loading condition without resetting the nominal condition gain values.

关键词： sensitivity analysis three-term control optimisation load regulation power generation control particle swarm optimisation power system interconnection frequency control distributed power generation poles and towers wave power generation geothermal power stations biofuel AC generators energy storage power system stability solar power stations ImGS system participation factors particle swarm optimisation whale optimisation algorithm DC links BOA-optimised PFOID controller load frequency control strategy diverse energy storage coordinated frequency control novel solar tower-Archimedes wave energy conversion-geothermal energy conversion-biodiesel driven generator-energy storage units butterfly optimisation algorithm ST-AWEC-GEC-BDDG-based independent three-area interconnected microgrid system direct current link-based independent three-area interconnected microgrid system metaheuristic technique dynamic performance proportional-integral derivative control PID with filter PIDN proportional-fractional-order integral derivative controllers firefly algorithm ES unit effect frequency deviation tie-line power objective functions sensitivity analysis system stability tie-line coefficient synchronization bias values

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Direct optimal fuzzy logic adapted to sliding mode for lateral autonomous vehicle control

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INTERNATIONAL JOURNAL OF VEHICLE PERFORMANCE 2024年第3期10卷 349-372页

作者： Jennan, Najlae Mellouli, El Mehdi Sidi Mohamed Ben Abdellah Univ Lab Engn Syst & Applicat Fes Morocco

In this article, a direct Takagi-Sugeno fuzzy logic controller adapted to fast terminal sliding mode has been developed to control the lateral dynamics of an autonomous vehicle under external disturbances. The strategy aims to improve the overall performance of the autonomous vehicle under varying driving conditions by exploiting the advantages of FTSMC to ensure fast convergence and reduced transient time, thereby overcoming uncertainties and the common chattering problem in sliding mode. First, we applied the direct T-S fuzzy logic controller based on FTSMC to the autonomous vehicle. To minimise both effects of uncertainties and fuzzy inaccuracy, a new adaptive control term is introduced and optimised using particle swarm optimisation and butterfly optimisation algorithm. A comparative analysis of these techniques is presented to show their advantages in improving the controller. The Lyapunov approach is employed to guarantee the system stability. The results prove the strategy's applicability for advancing overall vehicle behaviour.

关键词： adaptive lateral control T-S fuzzy logic fast terminal sliding mode control FTSMC autonomous vehicles particle swarm optimisation PSO butterfly optimisation algorithm BOA

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Optimising Control and Prediction Horizons of a Model Predictive Control-Based Motion Cueing algorithm Using butterfly Optimization algorithm

Optimising Control and Prediction Horizons of a Model Predic...

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

作者： Qazani, Mohammad Reza Chalak Jalali, Seyed Mohammad Jafar Asadi, Houshyar Nahavandi, Saeid Deakin Univ Inst Intelligent Syst Res & Innovat Geelong Vic 3125 Australia

ISBN: (纸本)9781728169293

The Motion Cueing algorithm (MCA) oversees regenerating the motion feeling of the real vehicle for the simulation-based motion platform (SBMP) within the physical limitations. Model Predictive Control (MPC) is recently employed as an MCA, which is called MPC-based MCA due to the consideration of the plant's boundaries in finding the optimal input signal. The computational load of the MPC directly relates to the control horizon and prediction horizon of the MPC. In this paper, a new optimisation method using butterfly optimisation algorithm is developed to find the optimal control horizon and prediction horizon of MPC-based MCA. The proposed method reduces the time of the tuning process of the MPC-based MCA, which is usually carried out via trial-and-error and genetic algorithm methods. Also, the trial-and-error method increases the motion sensation error and insufficient usage of the SBMP. The model is validated using MATLAB simulation environment, and the outcomes show that the developed butterfly optimisation algorithm will lead better motion sensation with less wrong motion signals and low computational burden compared with the trial-and-error and genetic algorithm method.

关键词： motion cueing algorithm model predictive control MPC horizons meta-heuristic algorithm butterfly optimisation algorithm genetic algorithm

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A Cluster Based Routing Using Energy Efficiency-Based Multi-Objective Optimization in Wireless Sensor Networks 1

A Cluster Based Routing Using Energy Efficiency-Based Multi-...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Sreekanth, N. Babu, G. Nagendra Kumar, Gotte Ranjith Madhukar, G. Shakir, Aboothar Mahmood Malla Reddy Engineering College for Women Department of Ece Hyderabad India School of Computing Mohan Babu University Department of Cse Tirupati India School of Computer Science & Artificial Intelligence Sr University Warangal India Cmr Technical Campus Department of Computer Science and Engineering Hyderabad India The Islamic University Najaf Iraq

ISBN: (纸本)9798350316469

Wireless Sensor Network (WSN) is a growing technology used for monitoring the physical world. The WSN's sensors are utilised to monitor the surrounding environment, and the information gathered is communicated wirelessly. The primary objective of wireless sensor network design is to maximise network lifetime. Network lifetime can be extended and energy consumption can be properly balanced through the use of clustering and routing. However, the WSN's restricted battery supply means that energy efficiency is regarded as a difficult task in a network. In this paper, an Energy Efficiency-based Multi-Objective Optimization method (EE- MOOA) is used to construct a cluster-based routing method for achieving energy efficiency in WSN. The Improved butterfly Optimization algorithm (IBOA) is used in the development of the EE-MOOA. Improving energy efficiency is the main goal of the EE-MOOA approach, which aims to improve data delivery for WSNs agricultural applications. The energy efficiency of the EE-MOOA method is 99.12% for 100 nodes, which is a higher measure when compared to the energy efficiency value of existing methods. © 2023 IEEE.

关键词： butterfly optimisation algorithm Cluster Based Routing Energy Efficiency Multi-Objective optimisation Wireless Sensor Networks

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