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As the distributed generators (DGs) are connected to active distribution network (ADN), it strengthens the communication interest between the customs and DGs and can facilitate energy integration. This article proposes hybrid pricing for DG configuration by taking advantage of fixed pricing and dynamic pricing. The time sequence scenario of wind-photovoltaic-load power can be got by k-means, which can balance the calculation burden with multiple scenarios. The planning model is built as the optimal objective for minimal investment in operation and maintenance of DGs, reduction of network loss, and decrease of voltage deviation. An improved simulated annealing particle swarm optimization algorithm is also proposed by refining the initialized population based on the niche fitness, introducing inertia weight with chaotic disturbance and accelerating local search with learning factor of dynamic parameter. The effectiveness and rationality of the proposed methodology are verified by simulation in the IEEE 69-bus system. [ABSTRACT FROM AUTHOR]

In view of the low level of gymnastics facilities monitoring and intelligent management of gymnastics venues, which cannot effectively manage gymnastics venues in real time, this paper proposes a method of gymnastics facilities monitoring and intelligent optimization distribution of gymnastics venues in the Internet of Things. This paper will build an information monitoring model, introduce a particle swarm optimization algorithm to participate in the location layout, and explore the actual effect of gymnastics and the Internet of Things. The research results show that (1) the system can measure the mechanical error of facilities and ensure that the controllable fault tolerance rate is less than 1%. (2) The quality of system monitoring is evaluated in accuracy, time, delay, and satisfaction, and the results are basically satisfactory, but the accuracy and time still need to be improved. (3) Using the evaluation system of sports facilities to test the temperature, humidity, facility pressure, and energy consumption suitable for gymnastics and to verify the injury tendency of athletes. When the damage tendency is between 70% and 100%, the actual damage rate is 1. (4) The speed of the PSO algorithm is faster than other methods, which is used to optimize the layout of gymnastics venues and has a certain role in promoting the construction of gymnastics venues. The system model designed in this paper performs well in gymnastics and needs to be further improved and optimized. [ABSTRACT FROM AUTHOR]

Various scientific studies have explored the causes of violent behaviour from different perspectives, with psychological tests, in particular, applied to the analysis of crime factors. The relationship between bi-factors has also been extensively studied including the link between age and crime. In reality, many factors interact to contribute to criminal behaviour and as such there is a need to have a greater level of insight into its complex nature. In this article we analyse violent crime information systems containing data on psychological, environmental and genetic factors. Our approach combines elements of rough set theory with fuzzy logic and particle swarm optimisation to yield an algorithm and methodology that can effectively extract multi-knowledge from information systems. The experimental results show that our approach outperforms alternative genetic algorithm and dynamic reduct-based techniques for reduct identification and has the added advantage of identifying multiple reducts and hence multi-knowledge (rules). Identified rules are consistent with classical statistical analysis of violent crime data and also reveal new insights into the interaction between several factors. As such, the results are helpful in improving our understanding of the factors contributing to violent crime and in highlighting the existence of hidden and intangible relationships between crime factors. [ABSTRACT FROM PUBLISHER]

Location area planning (LAP) is a combinatorial optimization grouping problem for the cellular mobile network. We propose a novel grouping biogeography-based optimization (GBBO), which has suitable migration and mutation with generating good initial habitats to partition the optimal number of location areas. The migration is to move the whole cells of location area (LA) with a randomly selected cell between habitats for emigration and immigration, while the adjacent cell mutation is carried out between LAs within one habitat. These group migration and mutation mechanisms are available to maintain the grouping conditions. This proposed GBBO helps us to obtain the optimal number of location areas and the corresponding configuration of the partitioned network. We also illustrate the GBBO approach using the small, medium, and large size problems to compare with artificial bee colony, particle swarm optimization, and previous LAP methods. The experimental results show that our novel GBBO is robust to find the best configurations of LAP with much less computation time comparing with other considered methods. [ABSTRACT FROM AUTHOR]

Peer-to-peer (P2P) topology has a significant influence on the performance, search efficiency and functionality, and scalability of the application. In this paper, we investigate a multi-swarm approach to the problem of neighbor selection (NS) in P2P networks. Particle swarm share some common characteristics with P2P in the dynamic socially environment. Each particle encodes the upper half of the peer-connection matrix through the undirected graph, which reduces the search space dimension. The portion of the adjustment to the velocity influenced by the individual's cognition, the group cognition from multi-swarms, and the social cognition from the whole swarm, makes an important influence on the particles' ergodic and synergetic performance. We also attempt to theoretically prove that the multi-swarm optimization algorithm converges with a probability of 1 towards the global optima. The performance of our approach is evaluated and compared with other two different algorithms. The results indicate that it usually required shorter time to obtain better results than the other considered methods, specially for large scale problems. [ABSTRACT FROM AUTHOR]

Abstract: Usually it is difficult to solve the control problem of a complex nonlinear system. In this paper, we present an effective control method based on adaptive PID neural network and particle swarm optimization (PSO) algorithm. PSO algorithm is introduced to initialize the neural network for improving the convergent speed and preventing weights trapping into local optima. To adapt the initially uncertain and varying parameters in the control system, we introduce an improved gradient descent method to adjust the network parameters. The stability of our controller is analyzed according to the Lyapunov method. The simulation of complex nonlinear multiple-input and multiple-output (MIMO) system is presented with strong coupling. Empirical results illustrate that the proposed controller can obtain good precision with shorter time compared with the other considered methods. [Copyright &y& Elsevier]

Abstract: Packing and layout problems have wide applications in engineering practice. However, they belong to NP (non-deterministic polynomial)-complete problems. In this paper, we introduce human intelligence into the computational intelligent algorithms, namely particle swarm optimization (PSO) and immune algorithms (IA). A novel human–computer cooperative PSO-based immune algorithm (HCPSO-IA) is proposed, in which the initial population consists of the initial artificial individuals supplied by human and the initial algorithm individuals are generated by a chaotic strategy. Some new artificial individuals are introduced to replace the inferior individuals of the population. HCPSO-IA benefits by giving free rein to the talents of designers and computers, and contributes to solving complex layout design problems. The experimental results illustrate that the proposed algorithm is feasible and effective. [Copyright &y& Elsevier]