Lightning-Based Lion Optimization Algorithm for Monitoring the Pipelines Using Linear Wireless Sensor Network

Varshney, Sudeep; Kumar, Chiranjeev; Swaroop, Abhishek

doi:10.1007/s11277-020-07987-8

Cited by 6 publications

(6 citation statements)

References 23 publications

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“…Many researchers have successfully employed traditional swarm intelligence and evolutionary algorithms, for instance, ant colony optimization (ACO), particle swarm optimization (PSO), and genetic algorithm (GA) (Roozbeh Nia et al 2017a , b ). Despite these algorithms, there are some modern and attractive examples involving the Horse herd Optimization Algorithm (HOA) (MiarNaeimi et al 2021 ; Moldovan 2020 ), Whale Optimization Algorithm (WOA) (Mirjalili and Lewis 2016 ; Islam et al 2021 ; Yan et al 2021 ; Zhang and Wen 2021 ; Wang et al 2021b ), Lion Optimization Algorithm (LOA) (Yazdani and Jolai 2016 ; Varshney et al 2021 ; Selvi and Ramakrishnan 2020 ; Wang et al 2020b ; Gope et al 2019 ), Ant Lion Optimizer (ALO) (Mirjalili 2015 ; Wang et al 2020a ; Bekakra et al 2021 ; Singh et al 2021 ; Chen et al 2020a ; Pradhan et al 2020 ), and Grey wolf optimizer (GWO) (Mirjalili et al 2014 ; Padhy and Panda 2021 ; Bekakra et al 2021 ; Wang et al 2021a ; Liu et al 2021 ; Tütüncü et al 2021 ).…”

Section: A Solution Algorithmmentioning

confidence: 99%

“…The GA and ACO presents a high risk of falling into local optimal, accordingly might lead to an inconsistent result thus needed more iteration to find the optimal solutions (Varshney et al 2021 ). Moreover, GA, ACO and PSO have many factors, and it is complicated to decide on correct parameters (Shinoda and Miyata 2019 ).…”

Section: A Solution Algorithmmentioning

confidence: 99%

“… In most circumstances, the outcomes achieved by LOA deliver outstanding solutions in fast convergence and global optima accomplishment (Yazdani and Jolai, 2016 ). This approach uses the local as well as global optima and thus gives the optimal solution with minimum cost (fitness function) and takes less iteration (Varshney et al 2021 ). WOA demands no added modification parameters to come to an outstanding balance between its exploration and exploitation (Aala Kalananda and Komanapalli, 2021 ).…”

Section: A Solution Algorithmmentioning

confidence: 99%

See 2 more Smart Citations

Assessment of coal supply chain under carbon trade policy by extended exergy accounting method

2023

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Within an uncertain environment and following carbon trade policies, this study uses the Extended Exergy Accounting (EEA) method for coal supply chains (SCs) in eight of the world's most significant coal consuming countries. The purpose is to improve the sustainability of coal SCs in terms of Joules rather than money while considering economic, environmental, and social aspects. This model is a multi-product economic production quantity (EPQ) with a single-vendor multi-buyer with shortage as a backorder. Within the SC, there are some real constraints, such as inventory turnover ratio, waste disposal to the environment, carbon dioxide emissions, and available budgets for customers. For optimization purposes, three recent metaheuristic algorithms, including Ant Lion Optimizer, Lion Optimization Algorithm, and Whale Optimization Algorithm, are suggested to determine a near-optimum solution to an "exergy fuzzy nonlinear integer-programming (EFNIP)." Moreover, an exact method (GAMS) is employed to validate the results of the suggested algorithms. Additionally, sensitivity analyses with different percentages of exergy parameters, such as capital, labor, and environmental remediation, are done to gain a deeper understanding of sustainability improvement in coal SCs. The results showed that sustainable coal SC in the USA has the lowest fuzzy total exergy, while Poland and China have the highest.

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Section: A Solution Algorithmmentioning

confidence: 99%

Section: A Solution Algorithmmentioning

confidence: 99%

Section: A Solution Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Assessment of coal supply chain under carbon trade policy by extended exergy accounting method

2023

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“…Generally, there may be outliers for the initial CHs determined by AP, which means that the selected CHs are non-optimal. So the Loin Swarm Optimization is used to find the optimal CHs based on the initial clusters [34] . For any CH, the position of its member nodes can be denoted by a vector 1 2 ( , , ), ( [1, ])…”

Section: B Finding the Optimal Chs Based On Loamentioning

confidence: 99%

Affinity Propagation and Chaotic Lion Swarm Optimization Based Clustering for Wireless Sensor Networks

Yu-Xin

Wang

et al. 2022

IEEE Access

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By grouping nodes with similar attributes into clusters, the energy efficiency and lifespan of wireless sensor networks(WSNs) can be improved effectively. However, the number of clusters needs to be set in advance, and the optimal cluster heads (CHs) are difficult to determine, which will undoubtedly reduce the network's overall performance. Therefore, a new clustering method using Affinity Propagation and Chaotic Lion swarm Optimization is proposed in this paper to form optimal clusters, which is called APCLO. In APCLO, Affinity Propagation (AP) is used to construct a cluster topology, forming the initial clustering according to the remaining energies of the nodes and the similarity of the distances between nodes. Moreover, the initial CHs are also determined simultaneously by AP. In order to eliminate the outliers from the initial CHs, Chaotic Lion Optimization(CLO) is presented to find the best CHs, in which a fitness function is set according to residual energy and distance to the BS, and chaotic map is used to speed up the convergence of CLO. Simulation results show that the APCLO protocol is superior to the comparison protocols in terms of energy consumption, network throughput, convergence speed, and lifetime. For network lifespan, it increases by 20.1%, 11.2%, and 13.5% respectively.

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“…Many researchers use heuristic algorithms to solve the optimal routing problem. Varshney et al [18] proposed a lightning-based lion optimization routing algorithm for minimizing the energy consumption in IWSNs. The algorithm determines the most suitable sensor placement method by considering the throughput, lifetime, delay, and coverage area of the sensor.…”

Section: Related Workmentioning

confidence: 99%

A Novel QoS Routing Energy Consumption Optimization Method Based on Clone Adaptive Whale Optimization Algorithm in IWSNs

et al. 2021

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Routing requests in industrial wireless sensor networks (IWSNs) are always restricted by QoS. Therefore, finding a high-quality routing path is a key problem. In this paper, a clone adaptive whale optimization algorithm (CAWOA) is designed for reducing the routing energy consumption of IWSNs with QoS constraints, and a novel clone operator is proposed. More importantly, CAWOA innovatively adopts a discrete binary-based routing coding method, which provides strong support for optimal routing schemes. In addition, a novel routing model of IWSNs combined with QoS constraints has been designed, which involves comprehensive consideration of bandwidth, delay, delay jitter, and packet loss rate. Subsequently, in a series of simulations, the proposed algorithm is compared with other heuristic-based routing algorithms, namely, whale optimization algorithm (WOA), simulated annealing (SA), particle swarm optimization (PSO), and genetic algorithm (GA). The simulation results suggest that the CAWOA-based routing algorithm outperforms other methods in terms of routing energy consumption, convergence speed, and optimization ability. Compared with GA, SA, PSO, and WOA under the conditions that the number of nodes is 120, the maximum delay is 120 ms, the maximum delay jitter is 25 ms, the maximum bandwidth is 9 Mbps, and the packet loss rate is 0.02; the energy consumption of CAWOA-based routing is reduced by 12%, 17%, 19%, and 7%, respectively.

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Lightning-Based Lion Optimization Algorithm for Monitoring the Pipelines Using Linear Wireless Sensor Network

Cited by 6 publications

References 23 publications

Assessment of coal supply chain under carbon trade policy by extended exergy accounting method

Assessment of coal supply chain under carbon trade policy by extended exergy accounting method

Affinity Propagation and Chaotic Lion Swarm Optimization Based Clustering for Wireless Sensor Networks

A Novel QoS Routing Energy Consumption Optimization Method Based on Clone Adaptive Whale Optimization Algorithm in IWSNs

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