2021 IEEE Symposium Series on Computational Intelligence (SSCI), 2021
Cuckoo search (CS) has proved its worth and is one among the most competitive algorithm for numer... more Cuckoo search (CS) has proved its worth and is one among the most competitive algorithm for numerical optimization. In order to improve its exploitation properties, this paper presents the hybridization of CS with a recently introduced naked mole-rat algorithm (NMRA). The major modification include $i$) new techniques based on barebones for global and NMRA for local search respectively are devised, ii) simulated annealing based mating factor for enhanced exploitation iii) an oscillating switch probability to balance between exploration and exploitation, and iv) shrinking population size reduction is used to minimize the computational burden. Apart from that, division of generations and population is also employed. The proposed mutation adaptive CS with MNRA (MaCN) is tested on CEC 2017 and CEC 2021 numerical benchmarks. From the experimental and statistical results, it can be said that MaCN is highly competitive with respect to MVMO, SaDN, JADE, SHADE, CV1.0, CSsin and CVnew algorithms.
2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), 2017
Grey wolf optimization (GWO) algorithm is a recent addition to the field of swarm intelligent alg... more Grey wolf optimization (GWO) algorithm is a recent addition to the field of swarm intelligent algorithms. The algorithm is based on the hunting pattern and leadership quality of grey wolfs present in nature. In this paper, to improve the working capabilities of GWO, a new version of GWO namely enhanced GWO (EGWO) has been proposed. The proposed version has been tested on standard benchmark problems to prove its competitiveness with respect to standard state-of-art algorithms. Experimental results show that EGWO is highly competitive and provide better convergence with respect to bat algorithm (BA), flower pollination algorithm (FPA), firefly algorithm (FA), bat flower pollinator (BFP) and GWO. Further convergence profiles validate the superior performance of EGWO.
Naked mole-rat algorithm (NMRA) is a new swarm intelligence technique based on the mating pattern... more Naked mole-rat algorithm (NMRA) is a new swarm intelligence technique based on the mating patterns of NMRs present in nature. The algorithm though is very simple and linear in nature but suffers from poor exploration during the initial stages and poor exploitation towards the end. Thus to overcome these problems and estimate the effect of basic parameters of NMRA, six new inertia weight strategies and five new mutation operators have been employed. After careful investigation, a new Levy mutated NMRA (LNMRA) is proposed. The new algorithm employs combined properties of inertia weights and mutation operators altogether. For performance evaluation, the proposed algorithms are subjected to variable initial population and dimension sizes and testing is done on CEC 2005, CEC 2014 benchmark problems and real world optimization problem of dual band-notched ultra-wideband (UWB) antenna design. Experimental and statistical results show that the proposed LNMRA is better with respect to other ...
2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), 2017
Linear antenna arrays design is complex electromagnetic problem. In this work, LAA is designed us... more Linear antenna arrays design is complex electromagnetic problem. In this work, LAA is designed using bat flower pollination (BFP) to obtain required radiation pattern having minimum possible side lobe level (SLL). The bat flower pollination is a combination of two algorithms namely bat algorithm and flower pollination algorithm. The two algorithms have been combined so as to avoid the local minima and provide superior performance. The BFP algorithm has been used to optimize three different equally spaced LAA. The results have been compared with the popular algorithms like particle swarm optimization (PSO), biogeography based optimization (BBO), flower pollination algorithm (FPA) and others.
Abstract Salp swarm algorithm (SSA) based on the swarming behaviour of salps found in ocean, is a... more Abstract Salp swarm algorithm (SSA) based on the swarming behaviour of salps found in ocean, is a very competitive algorithm and has proved its worth as an excellent problem optimizer. Though SSA is a very challenging algorithm but it suffers from the problem of poor exploitation, local optima stagnation and unbalanced exploration and exploitation operations. Thus in order to mitigate these problems and improve the working properties, seven new versions of SSA are proposed in present work. All the new versions employ new set of mutation properties along with some common properties. The common properties of all the algorithms include division of generations, adaptive switching and adaptive population strategy. Overall, the proposed algorithms are self-adaptive in nature along with some added mutation properties. For performance evaluation, the proposed algorithms are subjected to variable initial population and dimension sizes. The best among the proposed is then tested on CEC 2005, CEC 2015 benchmark problems and real world problems from CEC 2011 benchmarks. Experimental and statistical results show that the proposed mutation clock SSA (MSSA) is best among all the algorithms under comparison.
With the advancement of communication and sensor technologies, it has become possible to develop ... more With the advancement of communication and sensor technologies, it has become possible to develop low-cost circuitry to sense and transmit the state of surroundings. Wireless networks of such circuitry, namely wireless sensor networks (WSNs), can be used in a multitude of applications like healthcare, intelligent sectors, environmental sensing, and military defense. The crucial problem of WSN is the reliable exchange of data between different sensors and efficient communication with the data collection center. Clustering is the most appropriate approach to prolong the performance parameters of WSN. To overcome the limitations in clustering algorithms such as reduced cluster head (CH) lifetime; an effective CH selection algorithm, optimized routing protocol, and trust management are required to design an effective WSN solution. In this paper, a Cuckoo search optimization algorithm using a fuzzy type-2 logic-based clustering strategy is suggested to extend the level of confidence and hence network lifespan. In intra-cluster communication, a threshold-based data transmission algorithm is used and a multi-hop routing scheme for inter-cluster communication is employed to decrease dissipated energy from CHs far away from BS. Simulation outcomes indicate that the proposed strategy outperforms other communication techniques in the context of the successful elimination of malicious nodes along with energy consumption, stability period, and network lifetime.
This work proposes a new swarm intelligent nature-inspired algorithm called naked mole-rat (NMR) ... more This work proposes a new swarm intelligent nature-inspired algorithm called naked mole-rat (NMR) algorithm. This NMR algorithm mimics the mating patterns of NMRs present in nature. Two types of NMRs called workers and breeders are found to depict these patterns. Workers work continuously in the endeavor to become breeders, while breeders compete among themselves to mate with the queen. Those breeders who become sterile are pushed back to the worker’s group, and the fittest worker becomes a new breeder. This phenomenon has been adapted to develop the NMR algorithm. The algorithm has been benchmarked on 27 well-known test functions, and its performance is evaluated by a comparative study with particle swarm optimization (PSO), grey wolf optimization (GWO), whale optimization algorithm (WOA), differential evolution (DE), gravitational search algorithm (GSA), fast evolutionary programming (FEP), bat algorithm (BA), flower pollination algorithm (FPA), and firefly algorithm (FA). The experimental results and statistical analysis prove that NMR algorithm is very competitive as compared to other state-of-the-art algorithms. The matlab code for NMR algorithm is avaliable at https://github.com/rohitsalgotra/Naked-Mole-Rat-Algorithm.
Grey wolf optimization (GWO) is a recently developed nature-inspired global optimization method w... more Grey wolf optimization (GWO) is a recently developed nature-inspired global optimization method which mimics the social behaviour and hunting mechanism of grey wolves. Though the algorithm is very competitive and has been applied to various fields of research, it has poor exploration capability and suffers from local optima stagnation. So, in order to improve the explorative abilities of GWO, an extended version of grey wolf optimization (GWO-E) algorithm is presented. This newly proposed algorithm consists of two modifications: Firstly, it is able to explore new areas in the search space because of diverse positions assigned to the leaders. This helps in increasing the exploration and avoids local optima stagnation problem. Secondly, an opposition-based learning method has been used in the initial half of iterations to provide diversity among the search agents. The proposed approach has been tested on standard benchmarking functions for different population and dimension sizes to prove its effectiveness over other state-of-the-art algorithms. Experimental results show that the GWO-E algorithm performs better than GWO, bat algorithm, bat flower pollinator, chicken swarm optimization, differential evolution, firefly algorithm, flower pollination algorithm (FPA) and grasshopper optimization algorithm. Statistical testing of GWO-E has been done to prove its significance over other popular algorithms. Further, as a real-world application, the GWO-E is used to design non-uniform linear antenna array (LAA) for minimum possible sidelobe level and null control. Performance of GWO-E for the synthesis of LAA is evaluated by considering the several different case studies of LAA that exists in the literature, and the results are compared with the results of other popular meta-heuristic algorithms like genetic algorithm, ant lion algorithm, FPA, cat swarm optimization, GWO and many more. Numerical results further show the superior performance of GWO-E over original GWO and other popular algorithms.
Wireless sensor network (WSN) is a cost-effective networking solution for information updating in... more Wireless sensor network (WSN) is a cost-effective networking solution for information updating in the coverage radius or in the sensing region. To record a real-time event, a large number of sensor nodes (SNs) need to be arranged systematically, such that information collection is possible for a longer span of time. But, the hurdle faced by WSN is the limited resources of SNs. Hence, there is a high demand to design and implement an energy-efficient scheme to prolong the performance parameters of WSN. Clustering-based routing is the most suitable approach to support for load balancing, fault tolerance, and reliable communication to prolong performance parameters of WSN. These performance parameters are achieved at the cost of reduced lifetime of cluster head (CH). To overcome such limitations in clustering based hierarchical approach, efficient CH selection algorithm, and optimized routing algorithm are essential to design efficient solution for larger scale networks. In this paper, fuzzy extended grey wolf optimization algorithm based threshold-sensitive energy-efficient clustering protocol is proposed to prolong the stability period of the network. Analysis and simulation results show that the proposed algorithm significantly outperforms competitive clustering algorithms in the context of energy consumption, stability period and system lifetime.
Whale optimization algorithm (WOA) is a recently developed swarm intelligence-based algorithm whi... more Whale optimization algorithm (WOA) is a recently developed swarm intelligence-based algorithm which is inspired from the social behavior of humpback whale. This algorithm mimics the bubble-net hunting strategy of whales and has been applied to optimization problems. But the algorithm suffers from the problem of poor exploration and local optima stagnation. In this paper, three different modified algorithms of WOA have been proposed to improve its explorative ability. The modified versions are based on the concepts of opposition-based learning, exponentially decreasing parameters and elimination or re-initialization of worst particles. These properties have been added to improve the explorative properties of WOA by maintaining diversity among the search agents. The proposed algorithms have been tested on CEC2005 benchmark problems for variable population and dimension sizes. Statistical testing and scalability testing of the best algorithm have been carried out to prove its significance over other algorithms such as with well-known algorithms such as bat algorithm, bat flower pollinator, differential evolution, firefly algorithm, flower pollination algorithm. It has been found from the experimental results that the performance of all the proposed versions is better than the original WOA. Here, opposition- and exponential-based WOA is the best among all the proposed variants. Statistical testing and convergence profiles further validate the results.
Due to advancement in the technology and need for machine-to-machine connectivity, wireless senso... more Due to advancement in the technology and need for machine-to-machine connectivity, wireless sensor network (WSN) overplays the role compared to other wireless networks. In this context, different applications based on WSNs need to be executed efficiently in terms of energy and communication. To achieve this, there is a need to collaborate among various devices at various levels. This can be achieved by the grouping of these devices, that is, through the clustering. Clustering-based routing is the most suitable approach to support for load balancing, fault tolerance and reliable communication to prolong performance parameters of WSN. These performance parameters are achieved at the cost of reduced lifetime of cluster head (CH). To overcome such limitations in clustering-based hierarchical approach, efficient CH selection algorithm and optimized routing algorithm are essential to design efficient solution for larger scale networks. In this paper, fuzzy-enhanced flower pollination algorithm-based threshold-sensitive energy-efficient clustering protocol is proposed to prolong the stability period of the network. Analysis and simulation results show that the proposed algorithm significantly outperforms competitive clustering algorithms in the context of energy consumption, stability period and system lifetime.
The widespread use of wireless sensor devices and their advancements in terms of size, deployment... more The widespread use of wireless sensor devices and their advancements in terms of size, deployment cost and user friendly interface have given rise to many applications of wireless sensor networks (WSNs). WSNs need to utilize routing protocols to forward data samples from event regions to sink via minimum cost links. Clustering is a commonly used data aggregation method in which nodes are organized into groups in order to reduce the energy consumption. However, in clustering protocols, CH has to bear an additional load for coordinating various activities within the cluster. Therefore, proper CH selection and their load balancing using efficient routing protocol is a critical aspect for the long run operation of WSN. In this paper, a tree based clustering approach named threshold-sensitive energy-efficient tree-based routing protocol is proposed using enhanced flower pollination algorithm to extend the operational lifetime of the network. Analysis and simulation results show that the proposed algorithm significantly outperforms competitive clustering algorithms in terms of energy consumption, stability period and system lifetime.
2021 IEEE Symposium Series on Computational Intelligence (SSCI), 2021
Cuckoo search (CS) has proved its worth and is one among the most competitive algorithm for numer... more Cuckoo search (CS) has proved its worth and is one among the most competitive algorithm for numerical optimization. In order to improve its exploitation properties, this paper presents the hybridization of CS with a recently introduced naked mole-rat algorithm (NMRA). The major modification include $i$) new techniques based on barebones for global and NMRA for local search respectively are devised, ii) simulated annealing based mating factor for enhanced exploitation iii) an oscillating switch probability to balance between exploration and exploitation, and iv) shrinking population size reduction is used to minimize the computational burden. Apart from that, division of generations and population is also employed. The proposed mutation adaptive CS with MNRA (MaCN) is tested on CEC 2017 and CEC 2021 numerical benchmarks. From the experimental and statistical results, it can be said that MaCN is highly competitive with respect to MVMO, SaDN, JADE, SHADE, CV1.0, CSsin and CVnew algorithms.
2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), 2017
Grey wolf optimization (GWO) algorithm is a recent addition to the field of swarm intelligent alg... more Grey wolf optimization (GWO) algorithm is a recent addition to the field of swarm intelligent algorithms. The algorithm is based on the hunting pattern and leadership quality of grey wolfs present in nature. In this paper, to improve the working capabilities of GWO, a new version of GWO namely enhanced GWO (EGWO) has been proposed. The proposed version has been tested on standard benchmark problems to prove its competitiveness with respect to standard state-of-art algorithms. Experimental results show that EGWO is highly competitive and provide better convergence with respect to bat algorithm (BA), flower pollination algorithm (FPA), firefly algorithm (FA), bat flower pollinator (BFP) and GWO. Further convergence profiles validate the superior performance of EGWO.
Naked mole-rat algorithm (NMRA) is a new swarm intelligence technique based on the mating pattern... more Naked mole-rat algorithm (NMRA) is a new swarm intelligence technique based on the mating patterns of NMRs present in nature. The algorithm though is very simple and linear in nature but suffers from poor exploration during the initial stages and poor exploitation towards the end. Thus to overcome these problems and estimate the effect of basic parameters of NMRA, six new inertia weight strategies and five new mutation operators have been employed. After careful investigation, a new Levy mutated NMRA (LNMRA) is proposed. The new algorithm employs combined properties of inertia weights and mutation operators altogether. For performance evaluation, the proposed algorithms are subjected to variable initial population and dimension sizes and testing is done on CEC 2005, CEC 2014 benchmark problems and real world optimization problem of dual band-notched ultra-wideband (UWB) antenna design. Experimental and statistical results show that the proposed LNMRA is better with respect to other ...
2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), 2017
Linear antenna arrays design is complex electromagnetic problem. In this work, LAA is designed us... more Linear antenna arrays design is complex electromagnetic problem. In this work, LAA is designed using bat flower pollination (BFP) to obtain required radiation pattern having minimum possible side lobe level (SLL). The bat flower pollination is a combination of two algorithms namely bat algorithm and flower pollination algorithm. The two algorithms have been combined so as to avoid the local minima and provide superior performance. The BFP algorithm has been used to optimize three different equally spaced LAA. The results have been compared with the popular algorithms like particle swarm optimization (PSO), biogeography based optimization (BBO), flower pollination algorithm (FPA) and others.
Abstract Salp swarm algorithm (SSA) based on the swarming behaviour of salps found in ocean, is a... more Abstract Salp swarm algorithm (SSA) based on the swarming behaviour of salps found in ocean, is a very competitive algorithm and has proved its worth as an excellent problem optimizer. Though SSA is a very challenging algorithm but it suffers from the problem of poor exploitation, local optima stagnation and unbalanced exploration and exploitation operations. Thus in order to mitigate these problems and improve the working properties, seven new versions of SSA are proposed in present work. All the new versions employ new set of mutation properties along with some common properties. The common properties of all the algorithms include division of generations, adaptive switching and adaptive population strategy. Overall, the proposed algorithms are self-adaptive in nature along with some added mutation properties. For performance evaluation, the proposed algorithms are subjected to variable initial population and dimension sizes. The best among the proposed is then tested on CEC 2005, CEC 2015 benchmark problems and real world problems from CEC 2011 benchmarks. Experimental and statistical results show that the proposed mutation clock SSA (MSSA) is best among all the algorithms under comparison.
With the advancement of communication and sensor technologies, it has become possible to develop ... more With the advancement of communication and sensor technologies, it has become possible to develop low-cost circuitry to sense and transmit the state of surroundings. Wireless networks of such circuitry, namely wireless sensor networks (WSNs), can be used in a multitude of applications like healthcare, intelligent sectors, environmental sensing, and military defense. The crucial problem of WSN is the reliable exchange of data between different sensors and efficient communication with the data collection center. Clustering is the most appropriate approach to prolong the performance parameters of WSN. To overcome the limitations in clustering algorithms such as reduced cluster head (CH) lifetime; an effective CH selection algorithm, optimized routing protocol, and trust management are required to design an effective WSN solution. In this paper, a Cuckoo search optimization algorithm using a fuzzy type-2 logic-based clustering strategy is suggested to extend the level of confidence and hence network lifespan. In intra-cluster communication, a threshold-based data transmission algorithm is used and a multi-hop routing scheme for inter-cluster communication is employed to decrease dissipated energy from CHs far away from BS. Simulation outcomes indicate that the proposed strategy outperforms other communication techniques in the context of the successful elimination of malicious nodes along with energy consumption, stability period, and network lifetime.
This work proposes a new swarm intelligent nature-inspired algorithm called naked mole-rat (NMR) ... more This work proposes a new swarm intelligent nature-inspired algorithm called naked mole-rat (NMR) algorithm. This NMR algorithm mimics the mating patterns of NMRs present in nature. Two types of NMRs called workers and breeders are found to depict these patterns. Workers work continuously in the endeavor to become breeders, while breeders compete among themselves to mate with the queen. Those breeders who become sterile are pushed back to the worker’s group, and the fittest worker becomes a new breeder. This phenomenon has been adapted to develop the NMR algorithm. The algorithm has been benchmarked on 27 well-known test functions, and its performance is evaluated by a comparative study with particle swarm optimization (PSO), grey wolf optimization (GWO), whale optimization algorithm (WOA), differential evolution (DE), gravitational search algorithm (GSA), fast evolutionary programming (FEP), bat algorithm (BA), flower pollination algorithm (FPA), and firefly algorithm (FA). The experimental results and statistical analysis prove that NMR algorithm is very competitive as compared to other state-of-the-art algorithms. The matlab code for NMR algorithm is avaliable at https://github.com/rohitsalgotra/Naked-Mole-Rat-Algorithm.
Grey wolf optimization (GWO) is a recently developed nature-inspired global optimization method w... more Grey wolf optimization (GWO) is a recently developed nature-inspired global optimization method which mimics the social behaviour and hunting mechanism of grey wolves. Though the algorithm is very competitive and has been applied to various fields of research, it has poor exploration capability and suffers from local optima stagnation. So, in order to improve the explorative abilities of GWO, an extended version of grey wolf optimization (GWO-E) algorithm is presented. This newly proposed algorithm consists of two modifications: Firstly, it is able to explore new areas in the search space because of diverse positions assigned to the leaders. This helps in increasing the exploration and avoids local optima stagnation problem. Secondly, an opposition-based learning method has been used in the initial half of iterations to provide diversity among the search agents. The proposed approach has been tested on standard benchmarking functions for different population and dimension sizes to prove its effectiveness over other state-of-the-art algorithms. Experimental results show that the GWO-E algorithm performs better than GWO, bat algorithm, bat flower pollinator, chicken swarm optimization, differential evolution, firefly algorithm, flower pollination algorithm (FPA) and grasshopper optimization algorithm. Statistical testing of GWO-E has been done to prove its significance over other popular algorithms. Further, as a real-world application, the GWO-E is used to design non-uniform linear antenna array (LAA) for minimum possible sidelobe level and null control. Performance of GWO-E for the synthesis of LAA is evaluated by considering the several different case studies of LAA that exists in the literature, and the results are compared with the results of other popular meta-heuristic algorithms like genetic algorithm, ant lion algorithm, FPA, cat swarm optimization, GWO and many more. Numerical results further show the superior performance of GWO-E over original GWO and other popular algorithms.
Wireless sensor network (WSN) is a cost-effective networking solution for information updating in... more Wireless sensor network (WSN) is a cost-effective networking solution for information updating in the coverage radius or in the sensing region. To record a real-time event, a large number of sensor nodes (SNs) need to be arranged systematically, such that information collection is possible for a longer span of time. But, the hurdle faced by WSN is the limited resources of SNs. Hence, there is a high demand to design and implement an energy-efficient scheme to prolong the performance parameters of WSN. Clustering-based routing is the most suitable approach to support for load balancing, fault tolerance, and reliable communication to prolong performance parameters of WSN. These performance parameters are achieved at the cost of reduced lifetime of cluster head (CH). To overcome such limitations in clustering based hierarchical approach, efficient CH selection algorithm, and optimized routing algorithm are essential to design efficient solution for larger scale networks. In this paper, fuzzy extended grey wolf optimization algorithm based threshold-sensitive energy-efficient clustering protocol is proposed to prolong the stability period of the network. Analysis and simulation results show that the proposed algorithm significantly outperforms competitive clustering algorithms in the context of energy consumption, stability period and system lifetime.
Whale optimization algorithm (WOA) is a recently developed swarm intelligence-based algorithm whi... more Whale optimization algorithm (WOA) is a recently developed swarm intelligence-based algorithm which is inspired from the social behavior of humpback whale. This algorithm mimics the bubble-net hunting strategy of whales and has been applied to optimization problems. But the algorithm suffers from the problem of poor exploration and local optima stagnation. In this paper, three different modified algorithms of WOA have been proposed to improve its explorative ability. The modified versions are based on the concepts of opposition-based learning, exponentially decreasing parameters and elimination or re-initialization of worst particles. These properties have been added to improve the explorative properties of WOA by maintaining diversity among the search agents. The proposed algorithms have been tested on CEC2005 benchmark problems for variable population and dimension sizes. Statistical testing and scalability testing of the best algorithm have been carried out to prove its significance over other algorithms such as with well-known algorithms such as bat algorithm, bat flower pollinator, differential evolution, firefly algorithm, flower pollination algorithm. It has been found from the experimental results that the performance of all the proposed versions is better than the original WOA. Here, opposition- and exponential-based WOA is the best among all the proposed variants. Statistical testing and convergence profiles further validate the results.
Due to advancement in the technology and need for machine-to-machine connectivity, wireless senso... more Due to advancement in the technology and need for machine-to-machine connectivity, wireless sensor network (WSN) overplays the role compared to other wireless networks. In this context, different applications based on WSNs need to be executed efficiently in terms of energy and communication. To achieve this, there is a need to collaborate among various devices at various levels. This can be achieved by the grouping of these devices, that is, through the clustering. Clustering-based routing is the most suitable approach to support for load balancing, fault tolerance and reliable communication to prolong performance parameters of WSN. These performance parameters are achieved at the cost of reduced lifetime of cluster head (CH). To overcome such limitations in clustering-based hierarchical approach, efficient CH selection algorithm and optimized routing algorithm are essential to design efficient solution for larger scale networks. In this paper, fuzzy-enhanced flower pollination algorithm-based threshold-sensitive energy-efficient clustering protocol is proposed to prolong the stability period of the network. Analysis and simulation results show that the proposed algorithm significantly outperforms competitive clustering algorithms in the context of energy consumption, stability period and system lifetime.
The widespread use of wireless sensor devices and their advancements in terms of size, deployment... more The widespread use of wireless sensor devices and their advancements in terms of size, deployment cost and user friendly interface have given rise to many applications of wireless sensor networks (WSNs). WSNs need to utilize routing protocols to forward data samples from event regions to sink via minimum cost links. Clustering is a commonly used data aggregation method in which nodes are organized into groups in order to reduce the energy consumption. However, in clustering protocols, CH has to bear an additional load for coordinating various activities within the cluster. Therefore, proper CH selection and their load balancing using efficient routing protocol is a critical aspect for the long run operation of WSN. In this paper, a tree based clustering approach named threshold-sensitive energy-efficient tree-based routing protocol is proposed using enhanced flower pollination algorithm to extend the operational lifetime of the network. Analysis and simulation results show that the proposed algorithm significantly outperforms competitive clustering algorithms in terms of energy consumption, stability period and system lifetime.
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