mLBOA: A Modified Butterfly Optimization Algorithm with Lagrange Interpolation for Global Optimization

Sharma, Sushmita; Chakraborty, Subhankari Prasad; Saha, Apu Kumar; Nama, Sukanta; Sahoo, Saroj Kumar

doi:10.1007/s42235-022-00175-3

Cited by 69 publications

(17 citation statements)

References 51 publications

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“…Moreover, A few additional studies have been done by scholars with the help of several unique meta-heuristic algorithms in (Houssein et al 2021c), ( Houssein et al 2021d ), ( Sharma et al 2021 ), ( Houssein et al 2021e ), ( Chakraborty et al 2022a ) to address image segmentation issues.…”

Section: Related Workmentioning

confidence: 99%

Self-adaptive moth flame optimizer combined with crossover operator and Fibonacci search strategy for COVID-19 CT image segmentation

Sahoo¹,

Houssein²,

Premkumar³

et al. 2023

Expert Systems with Applications

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Section: Related Workmentioning

confidence: 99%

Self-adaptive moth flame optimizer combined with crossover operator and Fibonacci search strategy for COVID-19 CT image segmentation

Sahoo¹,

Houssein²,

Premkumar³

et al. 2023

Expert Systems with Applications

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“…When the CHs are chosen and clusters are optimally generated, the TLBO-MHR algorithm is applied to generate optimal routes in the IoT-assisted WSN. TLBO is a populationbased technique simulated by the procedure of teacher as well as learner [29][30][31][32][33][34][35][36]. Differing from other heuristic techniques, TLBO requires fewer approaches to certain parameters, which is an essential reason for choosing TLBO technique to optimize problems.…”

Section: Design Of Tlbo-mhr Techniquementioning

confidence: 99%

Improved Metaheuristic-Driven Energy-Aware Cluster-Based Routing Scheme for IoT-Assisted Wireless Sensor Networks

Neelakandan²,

et al. 2022

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The Internet of Things (IoT) is a network of numerous devices that are consistent with one another via the internet. Wireless sensor networks (WSN) play an integral part in the IoT, which helps to produce seamless data that highly influence the network’s lifetime. Despite the significant applications of the IoT, several challenging issues such as security, energy, load balancing, and storage exist. Energy efficiency is considered to be a vital part of the design of IoT-assisted WSN; this is accomplished by clustering and multi-hop routing techniques. In view of this, we introduce an improved metaheuristic-driven energy-aware cluster-based routing (IMD-EACBR) scheme for IoT-assisted WSN. The proposed IMD-EACBR model intends to achieve maximum energy utilization and lifetime in the network. In order to attain this, the IMD-EACBR model primarily designs an improved Archimedes optimization algorithm-based clustering (IAOAC) technique for cluster head (CH) election and cluster organization. In addition, the IAOAC algorithm computes a suitability purpose that connects multiple structures specifically for energy efficiency, detachment, node degree, and inter-cluster distance. Moreover, teaching–learning-based optimization (TLBO) algorithm-based multi-hop routing (TLBO-MHR) technique is applied for optimum selection of routes to destinations. Furthermore, the TLBO-MHR method originates a suitability purpose using energy and distance metrics. The performance of the IMD-EACBR model has been examined in several aspects. Simulation outcomes demonstrated enhancements of the IMD-EACBR model over recent state-of-the-art approaches. IMD-EACBR is a model that has been proposed for the transmission of emergency data, and the TLBO-MHR technique is one that is based on the requirements for hop count and distance. In the end, the proposed network is subjected to rigorous testing using NS-3.26’s full simulation capabilities. The results of the simulation reveal improvements in performance in terms of the proportion of dead nodes, the lifetime of the network, the amount of energy consumed, the packet delivery ratio (PDR), and the latency.

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“…Metaheuristic algorithms are a subset of stochastic algorithms that have been employed for solving complex problems such as feature selection [8][9][10][11][12], engineering [13][14][15][16][17][18][19][20][21][22][23][24][25][26], community detection [27][28][29][30], and continuous optimization [31][32][33][34][35][36][37] problems. Metaheuristic algorithms employ stochastic techniques to discover the promising areas by exploring the search space in early iterations and improve solutions quality by exploiting the promising areas in the final iterations.…”

Section: Introductionmentioning

confidence: 99%

Hybridizing of Whale and Moth-Flame Optimization Algorithms to Solve Diverse Scales of Optimal Power Flow Problem

et al. 2022

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The optimal power flow (OPF) is a practical problem in a power system with complex characteristics such as a large number of control parameters and also multi-modal and non-convex objective functions with inequality and nonlinear constraints. Thus, tackling the OPF problem is becoming a major priority for power engineers and researchers. Many metaheuristic algorithms with different search strategies have been developed to solve the OPF problem. Although, the majority of them suffer from stagnation, premature convergence, and local optima trapping during the optimization process, which results in producing low solution qualities, especially for real-world problems. This study is devoted to proposing an effective hybridizing of whale optimization algorithm (WOA) and a modified moth-flame optimization algorithm (MFO) named WMFO to solve the OPF problem. In the proposed WMFO, the WOA and the modified MFO cooperate to effectively discover the promising areas and provide high-quality solutions. A randomized boundary handling is used to return the solutions that have violated the permissible boundaries of search space. Moreover, a greedy selection operator is defined to assess the acceptance criteria of new solutions. Ultimately, the performance of the WMFO is scrutinized on single and multi-objective cases of different OPF problems including standard IEEE 14-bus, IEEE 30-bus, IEEE 39-bus, IEEE 57-bus, and IEEE118-bus test systems. The obtained results corroborate that the proposed algorithm outperforms the contender algorithms for solving the OPF problem.

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mLBOA: A Modified Butterfly Optimization Algorithm with Lagrange Interpolation for Global Optimization

Cited by 69 publications

References 51 publications

Self-adaptive moth flame optimizer combined with crossover operator and Fibonacci search strategy for COVID-19 CT image segmentation

Self-adaptive moth flame optimizer combined with crossover operator and Fibonacci search strategy for COVID-19 CT image segmentation

Improved Metaheuristic-Driven Energy-Aware Cluster-Based Routing Scheme for IoT-Assisted Wireless Sensor Networks

Hybridizing of Whale and Moth-Flame Optimization Algorithms to Solve Diverse Scales of Optimal Power Flow Problem

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