Particles Swarm Optimization for Minimal Energy Consumption in Complex Networks Node Search

Detecting communities is an important multidisciplinary research discipline and is considered vital to understand the structure of complex networks. Deep autoencoders have been successfully proposed to solve the problem of community detection. However, existing models in the literature are trained based on gradient descent optimization with the backpropagation algorithm, which is known to converge to local minima and prove inefficient, especially in big data scenarios. To tackle these drawbacks, this work proposed a novel deep autoencoder with Particle Swarm Optimization (PSO) and continuation algorithms to reveal community structures in complex networks. The PSO and continuation algorithms were utilized to avoid the local minimum and premature convergence, and to reduce overall training execution time. Two objective functions were also employed in the proposed model: minimizing the cost function of the autoencoder, and maximizing the modularity function, which refers to the quality of the detected communities. This work also proposed other methods to work in the absence of continuation, and to enable premature convergence. Extensive empirical experiments on 11 publically-available real-world datasets demonstrated that the proposed method is effective and promising for deriving communities in complex networks, as well as outperforming state-of-the-art deep learning community detection algorithms.

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Consumption of Energy

10.1093/oxfordhb/9780199561216.013.0016 ◽

2012 ◽

Author(s):

David E. Nye

Keyword(s):

Energy Consumption ◽

Complex Networks ◽

Muscle Power ◽

Archival Work

Anthropologists working within a functionalist tradition considered energy to be a fundamental need, along with food, water, and shelter. In 1949, Leslie White argued that systems of energy were so fundamental that societies could be classified according to how much light, heat, and power they had mastered. The society with the greatest access to energy was the most advanced. The most primitive were those that controlled nothing more than their own muscle power. By the 1980s, however, historians began to see consumers as actors whose decisions shaped which products succeeded in the market. The notion that advertisers controlled consumption collapsed after Roland Marchand's archival work revealed that agencies continually responded to changes in public taste, forced to follow trends beyond their control. Before it was possible to think of energy as something to be effortlessly consumed, complex networks of power had to be built into the very structure of cities. This article discusses energy consumption, and considers the establishment and growth of factories, as well as the use of energy in public lighting and transportation.

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A Multi-Objective Particle Swarm Optimization for Trajectory Planning of Fruit Picking Manipulator

Agronomy ◽

10.3390/agronomy11112286 ◽

2021 ◽

Vol 11 (11) ◽

pp. 2286

Author(s):

Xiaoman Cao ◽

Hansheng Yan ◽

Zhengyan Huang ◽

Si Ai ◽

Yongjun Xu ◽

...

Keyword(s):

Particle Swarm Optimization ◽

Energy Consumption ◽

Evaluation Method ◽

Technical Problem ◽

Particle Swarm ◽

Optimal Solution ◽

Swarm Optimization ◽

Multi Objective ◽

Advantages And Disadvantages ◽

Optimal Evaluation

Stable, efficient and lossless fruit picking has always been a difficult problem, perplexing the development of fruit automatic picking technology. In order to effectively solve this technical problem, this paper establishes a multi-objective trajectory model of the manipulator and proposes an improved multi-objective particle swarm optimization algorithm (represented as GMOPSO). The algorithm combines the methods of mutation operator, annealing factor and feedback mechanism to improve the diversity of the population on the basis of meeting the stable motion, avoiding the local optimal solution and accelerating the convergence speed. By adopting the average optimal evaluation method, the robot arm motion trajectory has been testified to constructively fulfill the picking standards of stability, efficiency and lossless. The performance of the algorithm is verified by ZDT1~ZDT3 benchmark functions, and its competitive advantages and disadvantages with other multi-objective evolutionary algorithms are further elaborated. In this paper, the algorithm is simulated and verified by practical experiments with the optimization objectives of time, energy consumption and pulsation. The simulation results show that the solution set of the algorithm is close to the real Pareto frontier. The optimal solution obtained by the average optimal evaluation method is as follows: the time is 34.20 s, the energy consumption is 61.89 °/S2 and the pulsation is 72.18 °/S3. The actual test results show that the trajectory can effectively complete fruit picking, the average picking time is 25.5 s, and the success rate is 96.67%. The experimental results show that the trajectory of the manipulator obtained by GMOPSO algorithm can make the manipulator run smoothly and facilitates efficient, stable and nondestructive picking.

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A Novel Edge Rewire Mechanism Based on Multiobjective Optimization for Network Robustness Enhancement

Frontiers in Physics ◽

10.3389/fphy.2021.735998 ◽

2021 ◽

Vol 9 ◽

Author(s):

Zhaoxing Li ◽

Qionghai Liu ◽

Li Chen

Keyword(s):

Multiobjective Optimization ◽

Complex Networks ◽

Optimization Algorithm ◽

Real World ◽

Network Robustness ◽

Objective Functions ◽

Swarm Optimization ◽

Scale Free ◽

Scale Free Networks ◽

The Given

A complex network can crash down due to disturbances which significantly reduce the network’s robustness. It is of great significance to study on how to improve the robustness of complex networks. In the literature, the network rewire mechanism is one of the most widely adopted methods to improve the robustness of a given network. Existing network rewire mechanism improves the robustness of a given network by re-connecting its nodes but keeping the total number of edges or by adding more edges to the given network. In this work we propose a novel yet efficient network rewire mechanism which is based on multiobjective optimization. The proposed rewire mechanism simultaneously optimizes two objective functions, i.e., maximizing network robustness and minimizing edge rewire operations. We further develop a multiobjective discrete partite swarm optimization algorithm to solve the proposed mechanism. Compared to existing network rewire mechanisms, the developed mechanism has two advantages. First, the proposed mechanism does not require specific constraints on the rewire mechanism to the studied network, which makes it more feasible for applications. Second, the proposed mechanism can suggest a set of network rewire choices each of which can improve the robustness of a given network, which makes it be more helpful for decision makings. To validate the effectiveness of the proposed mechanism, we carry out experiments on computer-generated Erdős–Rényi and scale-free networks, as well as real-world complex networks. The results demonstrate that for each tested network, the proposed multiobjective optimization based edge rewire mechanism can recommend a set of edge rewire solutions to improve its robustness.

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Cluster Routing Algorithm for Ring Based Wireless Sensor Network Using Particle Swarm And Lion Swarm Optimization

10.21203/rs.3.rs-791689/v1 ◽

2021 ◽

Author(s):

Huangshui Hu ◽

Yuxin Guo ◽

Jinfeng Zhang ◽

Chunhua Yin ◽

Dong Gao

Keyword(s):

Wireless Sensor Networks ◽

Energy Consumption ◽

Sensor Networks ◽

Hot Spot ◽

Fitness Function ◽

Routing Algorithm ◽

Particle Swarm ◽

Relay Node ◽

Wireless Sensor ◽

Swarm Optimization

Abstract In order to solve the problem of hot spot caused by uneven energy consumption of nodes in Wireless Sensor Networks (WSNs) and reduce the network energy consumption, a novel cluster routing algorithm called CRPL for ring based wireless sensor networks using Particle Swarm Optimization (PSO) and Lion Swarm Optimization (LSO) is proposed in this paper. In CRPL, the optimal cluster head (CH) of each ring are selected by using LSO whose fitness function is composed of energy,number of neighbor nodes, number of cluster heads and distance. Moreover, PSO with a multi-objective fitness function considering distance, energy and cluster size is used to find the next hop relay node in the process of data transmission, and the optimal routing paths are obtained, so as to alleviate the hot spot problem as well as decrease the energy consumption in the routing process. The simulation results show that, compared with some existing optimization algorithms, CRPL has better effects in balancing the energy consumption of the network and prolonging the life cycle of the network.

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Eagle Strategy with Cauchy Mutation Particle Swarm Optimization for Energy Management in Cloud Computing

International Journal of Intelligent Engineering and Systems ◽

10.22266/ijies2020.1231.05 ◽

2020 ◽

Vol 13 (6) ◽

pp. 42-51

Author(s):

Archana Kollu ◽

◽

Sucharita Vadlamudi ◽

Keyword(s):

Particle Swarm Optimization ◽

Energy Consumption ◽

Energy Management ◽

Optimization Technique ◽

Service Level Agreement ◽

Particle Swarm ◽

Swarm Optimization ◽

Cloud Data ◽

Cauchy Mutation ◽

Eagle Strategy

Energy management of the cloud datacentre is a challenging task, especially when the cloud server receives a number of the user’s request simultaneously. This requires an efficient method to optimally allocate the resources to the users. Resource allocation in cloud data centers need to be done in optimized manner for conserving energy keeping in view of Service Level Agreement (SLA). We propose, Eagle Strategy (ES) based Modified Particle Swarm Optimization (ES-MPSO) to minimize the energy consumption and SLA violation. The Eagle Strategy method is applied due to its efficient local optimization technique. The Cauchy Mutation method which schedules the task effectively and minimize energy consumption, is applied to the proposed ES-MPSO method for improving the convergence performance. The simulation result shows that the energy consumption of ES-MPSO is 42J and Particle Swarm Optimization (PSO) is 51J. The proposed method ES-MPSO achieves higher efficiency compared to the PSO method in terms of energy management and SLA.

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A novel community detection method based on discrete particle swarm optimization algorithms in complex networks

2015 IEEE Congress on Evolutionary Computation (CEC) ◽

10.1109/cec.2015.7256889 ◽

2015 ◽

Cited By ~ 8

Author(s):

Cen Cao ◽

Qingjian Ni ◽

Yuqing Zhai

Keyword(s):

Particle Swarm Optimization ◽

Complex Networks ◽

Community Detection ◽

Detection Method ◽

Optimization Algorithms ◽

Particle Swarm ◽

Discrete Particle Swarm Optimization ◽

Swarm Optimization ◽

Discrete Particle

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Prolonging network lifetime and optimizing energy consumption using swarm optimization in mobile wireless sensor networks

Sensor Review ◽

10.1108/sr-08-2017-0157 ◽

2018 ◽

Vol 38 (4) ◽

pp. 534-541

Author(s):

Sangeetha M. ◽

Sabari A.

Keyword(s):

Energy Consumption ◽

Network Lifetime ◽

Sensor Nodes ◽

Wireless Sensor ◽

Mobile Nodes ◽

Mobile Wireless ◽

Swarm Optimization ◽

Content Type ◽

Mobile Wireless Sensor ◽

Practical Implications

Purpose This paper aims to provide prolonging network lifetime and optimizing energy consumption in mobile wireless sensor networks (MWSNs). Forming clusters of mobile nodes is a great task owing to their dynamic nature. Such clustering has to be performed with a higher consumption of energy. Perhaps sensor nodes might be supplied with batteries that cannot be recharged or replaced while in the field of operation. One optimistic approach to handle the issue of energy consumption is an efficient way of cluster organization using the particle swarm optimization (PSO) technique. Design/methodology/approach In this paper two improved versions of centralized PSO, namely, unequal clustering PSO (UC-PSO) and hybrid K-means clustering PSO (KC-PSO), are proposed, with a focus of achieving various aspects of clustering parameters such as energy consumption, network lifetime and packet delivery ratio to achieve energy-efficient and reliable communication in MWSNs. Findings Theoretical analysis and simulation results show that improved PSO algorithms provide a balanced energy consumption among the cluster heads and increase the network lifetime effectively. Research limitations/implications In this work, each sensor node transmits and receives packets at same energy level only. In this work, focus was on centralized clustering only. Practical implications To validate the proposed swarm optimization algorithm, a simulation-based performance analysis has been carried out using NS-2. In each scenario, a given number of sensors are randomly deployed and performed in a monitored area. In this work, simulations were carried out in a 100 × 100 m2 network consisting 200 nodes by using a network simulator under various parameters. The coordinate of base station is assumed to be 50 × 175. The energy consumption due to communication is calculated using the first-order radio model. It is considered that all nodes have batteries with initial energy of 2 J, and the sensing range is fixed at 20 m. The transmission range of each node is up to 25 m and node mobility is set to 10 m/s. Practical implications This proposed work utilizes the swarm behaviors and targets the improvement of mobile nodes’ lifetime and energy consumption. Originality/value PSO algorithms have been implemented for dynamic sensor nodes, which optimize the clustering and CH selection in MWSNs. A new fitness function is evaluated to improve the network lifetime, energy consumption, cluster formation, packet transmissions and cluster head selection.

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