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Performance of Quasi Oppositional butterfly optimization algorithm for Cluster Head Selection in WSNs

Procedia Computer Science 2020年 171卷 1953-1960页

作者： Nageswara Rao Malisetti Vinay Kumar Pamula Department of ECE MIC College of Technology Kanchikacherla A.P. India 521180 Department of ECE UCEK JNTUK Kakinada India 533003

Wireless sensor networks (WSNs) technology is attracting the most of researchers and industry due to its application in wide contexts that include the military, surveillance, monitoring of habitat, health care, public safety and environmental monitoring. The consumption of energy is a crucial challenge faced in the WSN research. Clustering is an efficient technique for limiting the energy consumption of WSNs. But considering the hierarchical and cluster-based WSNs, the cluster heads utilize a high-level of energy because of data aggregation from various sensor nodes and transmit the same to the sink node. So, proper cluster head (CH) selection is required for sensor nodes’ energy conservation to prolong the network’s lifespan of the WSNs. In this paper, a CH selection protocol is proposed that is based on the quasi oppositional butterfly optimization algorithm (QOBOA). The proposed method is compared with original butterfly optimization along with certain existing algorithms with respect to the networks’ lifetime and efficiency of energy. Simulation results show that the QOBOA based CH selection scheme outperforms the existing ones.

关键词： butterfly optimization algorithm Cluster head Data aggregation Network lifespan

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Parameter extraction of photovoltaic model based on butterfly optimization algorithm with chaos learning strategy

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SOLAR ENERGY 2024年 269卷

作者： Ru, Xueyan Bengbu Univ Sch Elect & Elect Engn Bengbu 233030 Peoples R China

Accurate extraction of photovoltaic (PV) model parameter is significant for the evaluation of PV system. Most of existing methods for extracting the PV parameters suffer from low extraction accuracy and are prone to fall into local optima. To solve these shortcomings, a chaos learning butterfly optimization algorithm (CLBOA) is proposed. First, the introduced Cauchy mutation increases the perturbation to butterflies and helps the algorithm to jump out of local optima. Second, a chaos learning strategy is proposed to lead the butterflies within the population to learn from the optimal individual using the improved Tent chaos mapping, which strengthens the learning exchange of the population, enhances the ability of global exploration and local exploitation, and optimizes the convergence speed and accuracy. Third, the three worst individual positions were randomly initialized using a terminal elimination mechanism to increase population diversity. After evaluating CLBOA through the CEC2022 benchmark suite, it was used to extract parameters for a total of 12 PV cell and module models for 5 materials. Compared with 9 well-known algorithms, CLBOA performs excellent in terms of convergence performance, parameters extraction accuracy, running time and improvement index. Finally, CLBOA was applied on YL PV power station model of Guizhou Power Grid in China under different irradiations and temperatures. The I -V and P -V curves reconstructed by CLBOA are highly fit to the synthesized curves, and the individual absolute error (IAE) and relative error (RE) also confirm the satisfactory accuracy of parameters extraction. Comprehensive analysis shows that CLBOA can extract parameters better than the comparison algorithms, demonstrating its strong potential for PV parameters extraction.

关键词： Photovoltaic model Parameter extraction butterfly optimization algorithm Chaos learning strategy Cauchy mutation

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Enhanced butterfly optimization algorithm for Task Scheduling in Cloud Computing Environments

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第12期15卷 435-443页

作者： Zhao, Yue Cangzhou Jiaotong Coll Coll Comp Cangzhou 061199 Peoples R China

Cloud computing is transforming the provision of elastic and adaptable capabilities on demand. A scalable infrastructure and a wide range of offerings make cloud computing essential to today's computing ecosystem. Cloud resources enable users and various companies to utilize data maintained in a distant location. Generally, cloud vendors provide services within the limitations of Service Level Agreement (SLA) terms. SLAs consist of various Quality of Service (QoS) requirements the supplier promises. Task scheduling is critical to maintaining higher QoS and lower SLAs. In simple terms, task scheduling aims to schedule tasks to limit wasted time and optimize performance. Considering the NP-hard character of cloud task scheduling, metaheuristic algorithms are widely applied to handle this optimization problem. This study presents a novel approach using the butterfly optimization algorithm (BOA) for scheduling cloud-based tasks across diverse resources. BOA performs well on non-constrained and non-biased mathematical functions. However, its search capacity is limited to shifted, rotated, and/or constrained optimization problems. This deficiency is addressed by incorporating a virtual butterfly and improved fuzzy decision processes into the conventional BOA. The suggested methodology improves throughput and resource utilization while reducing the makespan. Regardless of the number of tasks, better results are consistently produced, indicating greater scalability.

关键词： Cloud computing resource utilization task scheduling butterfly optimization algorithm fuzzy decision strategy

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Underground Face Drill Jumbos Path-planning Based on Improved butterfly optimization algorithm

Underground Face Drill Jumbos Path-planning Based on Improve...

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第43届中国控制会议

作者： Yuning Wang Jian Liu Lei Zhang Hao Sun College of Artificial Intelligence Hebei University of Technology Control Engineering Technology Innovation Center of Hebei Province Hebei University of Technology ZhangjiakouXuanhuaHuatai Mining & Metallurgical Machinery Co. Ltd

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

In this paper, a butterfly optimization algorithm is used for path planning of autonomous driving of underground trackless face drill jumbos. On account of the shortcomings of the traditional butterfly optimization algorithm whose parameters are complex and fixed in view of the more severe and complex environment of the actual underground mine path, a method based on adaptive adjustment of the power exponent parameters is proposed to improve it. The algorithm is applied to improve the search accuracy and iterative capability as opposed to the traditional butterfly optimization algorithm, which is verified by comparing the fitness function with the planning simulation of different simulation maps.

关键词： butterfly optimization algorithm Path planning Adaptive control

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Frequency regulation of hybrid shipboard microgrid system using butterfly optimization algorithm synthesis fractional-order controller

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INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS 2023年第5期36卷

作者： Mondal, Amitava Latif, Abdul Das, Dulal Chandra Hussain, S. M. Suhail Al-Durra, Ahmed Natl Inst Technol Dept Elect Engn Silchar Assam India Khalifa Univ EECS Dept Adv Power & Energy Ctr Abu Dhabi U Arab Emirates Natl Univ Singapore NUS Dept Comp Sci Singapore Singapore

Inclusion of intermittent natured renewable energy resources in microgrid to reduce global warming, especially in shipboard power system and due to highly fluctuating propulsion load, frequency control strategy of isolated shipboard hybrid microgrid (ISHMG) is major point of attraction. Hence, a power system of marine vessel with dish-stirling solar thermal system (DSTS), wind-driven generation (WDG), solid oxide fuel cell (SOFC), super-conducting magnetic energy storage (SMES), two different AC-loads, and propulsion loads are considered as an ISHMG. The main objective of this paper is reducing the mismatch between generation and demand with the help of fractional-order proportional integral-derivative (FOPID) controller. To improve the frequency control, the tuning of the controller coefficients plays a major role as the controller's performance fully dependent on the parameters. Accordingly, the recently developed butterfly optimization algorithm (BOA) has been used to optimize the FOPID controller's parameters. Comparative analysis of several techniques like FA, PSO, GOA, SSA, and BOA used for PID, PI, FOPID controllers' optimization, and sensitivity analysis under different parametric variation has been presented to prove the stability of the ISHMG model. Analyzing all the results, it is found that BOA-based FOPID controller is performing the frequency control far better than other techniques.

关键词： butterfly optimization algorithm dish-stirling solar thermal system fractional-order PID controller frequency control strategy horizontal axis wind turbine isolated microgrid Shipboard microgrid

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A Hybrid Firefly algorithm with butterfly optimization algorithm and its Application

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ENGINEERING LETTERS 2022年第2期30卷 453页

作者： Zhang, Jinqian Xie, Xuefeng Wang, Min Zhang, Mengjian Guizhou Univ Sch Elect Engn Guiyang 550025 Peoples R China Guizhou Aerosp Kaishan Petr Instruments Co Ltd Guiyang 550025 Peoples R China Guizhou Univ Coll Elect Engn Guiyang 550025 Peoples R China

butterfly optimization algorithm (BOA) is a new nature-inspired algorithm that imitates the food-searching and mating behavior of butterflies to solve the global optimization problem. In nature, butterflies not only determine the locate nectar or mates by smell, but also the visual function of butterflies cannot be ignored. We proposed a novel hybrid firefly algorithm (FA) with BOA, namely FA-BOA, in which we take the visual function of the similarity of fireflies and butterflies into consideration. To substantiate the optimization performance of the proposed algorithm, FA-BOA is tested on a set of eight benchmark functions. Besides, the proposed algorithm is used to solve two real-world engineering design problems (Three-bar truss design and Speed reducer design). Experimental results demonstrate that the proposed algorithm is effective and outpeforms other optimization algorithms in terms of convergence accuracy and stability.

关键词： butterfly optimization algorithm firefly algorithm high dimension speed reducer design three-bar truss design

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Learning automata-based butterfly optimization algorithm for engineering design problems

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INTERNATIONAL JOURNAL OF COMPUTATIONAL MATERIALS SCIENCE AND ENGINEERING 2018年第4期7卷

作者： Arora, Sankalap Anand, Priyanka DAV Univ Dept Comp Sci & Engn Jalandhar Punjab India Lovely Profess Univ Dept Comp Sci & Engn Jalandhar Punjab India

butterfly optimization algorithm (BOA) is a novel meta-heuristic algorithm inspired by the food foraging behavior of the butterflies. The performance of BOA critically depends upon the probability parameter which decides whether the butterfly has to move towards the best butterfly of the population or perform a random search. Therefore, in order to increase the potential of the BOA, which focuses on exploration phase in the initial stages and on exploitation in the later stages of the optimization, learning automata have been embedded in BOA in which a learning automaton takes the role of configuring the behavior of a butterfly in order to create a proper balance between the process of global and local search. The introduction of learning automata accelerates the global convergence speed to the true global optimum while preserving the main feature of the basic BOA. In order to validate the effectiveness of the proposed algorithm, it is evaluated on 17 benchmark test functions and 3 classical engineering design problems with different characteristics, having real-world applications. The simulation results demonstrate that the introduction of learning automata in BOA has significantly boosted the performance of BOA in terms of achievement of true global optimum and avoidance of local optima entrapment.

关键词： butterfly optimization algorithm learning automata adaptive behavior engineering design problems

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A novel adaptive butterfly optimization algorithm

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INTERNATIONAL JOURNAL OF COMPUTATIONAL MATERIALS SCIENCE AND ENGINEERING 2018年第4期7卷

作者： Singh, Bhupinder Anand, Priyanka Lovely Profess Univ Dept Comp Sci & Engn Jalandhar Punjab India

butterfly optimization algorithm (BOA) is an interesting bio-inspired algorithm that uses a nature inspired simulation model, based on the food foraging behavior of butterflies. A common problem with BOA is that in early stages of simulation process, it may converge to sub-optimal solutions due to the loss of diversity in its population. The sensory modality is the critical parameter which is responsible for searching new solutions in the nearby regions. In this work, an adaptive butterfly optimization algorithm is proposed in which a novel phenomenon of changing the sensory modality of BOA is employed during the optimization process in order to achieve better results in comparison to traditional BOA. The proposed Adaptive butterfly optimization algorithm (ABOA) is tested against seventeen standard bench mark functions. Its performance is then compared against existing standard optimization algorithms, namely artificial bee colony, firefly algorithm and standard butterfly optimization algorithm. The results indicate that the proposed adaptive BOA with improved parameter calculation mechanism produces superior results in terms of convergence and achievement of the global optimal solution efficiently.

关键词： Nature-inspired algorithm butterfly optimization algorithm adaptive algorithm sensory modality BOA convergence

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NHBBWO: A novel hybrid butterfly-beluga whale optimization algorithm with the dynamic strategy for WSN coverage optimization

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2025年第2期18卷 1-25页

作者： Chen, Xinyi Zhang, Mengjian Yang, Ming Wang, Deguang Guizhou Univ Sch Elect Engn Guiyang 550025 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China

Wireless sensor networks (WSNs) play a crucial role in the development of emerging technologies, but they face significant challenges in practical applications, particularly concerning the coverage problem. Effective coverage of sensor nodes is essential for ensuring high-quality service in WSNs. However, existing swarm intelligence algorithms for coverage optimization in WSNs often exhibit poor performance and suffer from simplistic experimental setups and low coverage rates. To address these challenges, this study proposes the hybrid butterfly-beluga optimization algorithm with a dynamic quadratic parameter adaptation strategy (NHBBWO) for optimizing WSNs coverage. NHBBWO combines the strengths of beluga whale optimization (BWO), known for rapid convergence, and butterfly optimization (BOA), which excels in global search but struggles with slow convergence and local optima. By hybridizing BWO and BOA, the hybrid butterfly-beluga whale optimization (HBBWO) algorithm improves both convergence accuracy and speed, as well as the ability to avoid local optima. Additionally, a dynamic parameter adaptation strategy using a quadratic function further enhances the exploration capability of HBBWO. The performance of NHBBWO is rigorously evaluated through statistical analyses, including the Friedman rank test and Wilcoxon rank-sum test, applied to twenty-three benchmark functions. The results confirm the superiority of NHBBWO. Finally, simulations in 2D coverage scenarios demonstrate that NHBBWO significantly improves node coverage and reduces redundancy compared to four state-of-the-art swarm intelligence algorithms. This advancement addresses critical limitations in current methodologies and contributes to the broader field of WSN optimization by providing a robust solution to the coverage challenge.

关键词： Wireless sensor network Coverage optimization Hybrid algorithm Beluga whale optimization butterfly optimization algorithm Dynamic parameter adaptation strategy

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Hybrid Mutualism Mechanism-Inspired butterfly and Flower Pollination optimization algorithm for Lifetime Improving Energy-Efficient Cluster Head Selection in WSNs

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WIRELESS PERSONAL COMMUNICATIONS 2023年第3期128卷 1567-1601页

作者： Pratha, S. Jaya Asanambigai, V Mugunthan, S. R. Annamalai Univ Dept CSE Chidambaram 608002 Tamil Nadu India Dr NGP Inst Technol Dept CSE Coimbatore 641048 Tamil Nadu India

Rapid advancement of technologies in Wireless Sensor Networks is attracting maximized attention across the scientific community due to its suitability and diversified coverage in real life applications. WSNs due to their features of resource limitation and infrastructure-less deployment introduces the most challenging issues of network lifetime improvement, energy stability and reliable cluster head selection, which is still a herculean task. Clustering is an indispensable mechanism employed for selecting an optimal cluster head with the objective of extending network lifetime with energy stability that achieves efficient data transmission. Cluster head selection through meta-heuristic algorithms introduces the merits of simplicity, flexibility, derivation free and prevents local optima. In this paper, Hybrid Mutualism Mechanism-inspired butterfly and Flower Pollination optimization algorithm (HMMB-FPOA) is proposed for energy-efficient cluster head selection that attributes towards better energy stability and sustained network lifetime. Mutualism phase includes the symbiosis organisms search over flower pollination optimization algorithm for incorporating strong exploitation capability into butterfly optimization algorithm that prevents losses of premature convergence as it may lose its diversity. This integration of FPOA and BOA improves the overall exploration ability to the expected level, such that convergence speed of the algorithms is accelerated. It also balances the capabilities of exploitation and exploration by dynamically increasing the adaptive switching probability that results in prolonged network lifetime. The simulation results of HMMB-FPOA confirmed an enhanced performance of the network in terms of alive nodes, dead nodes, residual energy, overall throughput, and convergence rate on par with the existing competitive cluster head selection algorithms.

关键词： Mutualism mechanism Cluster head selection butterfly optimization algorithm Flower pollination optimization algorithm Symbiosis organisms search

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