Adaptive Discrete Particle Swarm Optimization Research Articles

Design and implementation of a novel real‐time unmanned aerial vehicle localization scheme based on received signal strength

AbstractTo accurately localize unmanned aerial vehicles (UAVs) is one of the key issues to deal with the security threats caused by UAVs. Thus, this article proposes a UAV localization scheme that utilizes the area grid quantization and transmit power statistical calibration techniques, in which the location and transmit power of the UAV are unknown. Firstly, the adaptive discrete particle swarm optimization (ADPSO) algorithm is used to find the global optimal solution to the localization problem. A grid quantization method is proposed to discretize the value space of the traditional PSO algorithm to reduce the estimated localization time. An adaptive factor is also introduced to adjust the value of power for each iteration in the discrete particle swarm optimization (DPSO) algorithm in order to find the global optimal solution efficiently. Secondly, the fixed transmit power range is adopted to calibrate the estimated value of transmit power and the corresponding location. The prototype system comprises four anchor nodes based on a universal software radio peripheral radio frequency (USRP RF). Finally, field experiments are carried out to demonstrate the effectiveness of the proposed localization scheme.

Fuzzy Theory-Based Data Placement for Scientific Workflows in Hybrid Cloud Environments

In hybrid cloud environments, reasonable data placement strategies are critical to the efficient execution of scientific workflows. Due to various loads, bandwidth fluctuations, and network congestions between different data centers as well as the dynamics of hybrid cloud environments, the data transmission time is uncertain. Thus, it poses huge challenges to the efficient data placement for scientific workflows. However, most of the traditional solutions for data placement focus on deterministic cloud environments, which lead to the excessive data transmission time of scientific workflows. To address this problem, we propose an adaptive discrete particle swarm optimization algorithm based on the fuzzy theory and genetic algorithm operators (DPSO-FGA) to minimize the fuzzy data transmission time of scientific workflows. The DPSO-FGA can rationally place the scientific workflow data while meeting the requirements of data privacy and the capacity limitations of data centers. Simulation results show that the DPSO-FGA can effectively reduce the fuzzy data transmission time of scientific workflows in hybrid cloud environments.

An adaptive discrete particle swarm optimization for influence maximization based on network community structure

As an important research field of social network analysis, influence maximization problem is targeted at selecting a small group of influential nodes such that the spread of influence triggered by the seed nodes will be maximum under a given propagation model. It is yet filled with challenging research topics to develop effective and efficient algorithms for the problem especially in large-scale social networks. In this paper, an adaptive discrete particle swarm optimization (ADPSO) is proposed based on network topology for influence maximization in community networks. According to the framework of ADPSO, community structures are detected by label propagation algorithm in the first stage, then dynamic encoding mechanism for particle individuals and discrete evolutionary rules for the swarm are conceived based on network community structure for the meta-heuristic optimization algorithm to identify the allocated number of influential nodes within different communities. To expand the seed nodes reasonably, a local influence preferential strategy is presented to allocate the number of candidate nodes to each community according to its marginal gain. The experimental results on six social networks demonstrate that the proposed ADPSO can achieve comparable influence spread to CELF in an efficient way.

A new hybrid evolutionary algorithm based on new fuzzy adaptive PSO and NM algorithms for Distribution Feeder Reconfiguration

Network reconfiguration for loss reduction in distribution system is a very important way to save the electrical energy. This paper proposes a new hybrid evolutionary algorithm to solve the Distribution Feeder Reconfiguration problem (DFR). The algorithm is based on combination of a New Fuzzy Adaptive Particle Swarm Optimization (NFAPSO) and Nelder–Mead simplex search method (NM) called NFAPSO–NM. In the proposed algorithm, a new fuzzy adaptive particle swarm optimization includes two parts. The first part is Fuzzy Adaptive Binary Particle Swarm Optimization (FABPSO) that determines the status of tie switches (open or close) and second part is Fuzzy Adaptive Discrete Particle Swarm Optimization (FADPSO) that determines the sectionalizing switch number. In other side, due to the results of binary PSO(BPSO) and discrete PSO(DPSO) algorithms highly depends on the values of their parameters such as the inertia weight and learning factors, a fuzzy system is employed to adaptively adjust the parameters during the search process. Moreover, the Nelder–Mead simplex search method is combined with the NFAPSO algorithm to improve its performance. Finally, the proposed algorithm is tested on two distribution test feeders. The results of simulation show that the proposed method is very powerful and guarantees to obtain the global optimization.

Hybrid Fuzzy Adaptive Particle Swarm Optimization and Differential Evolution Algorithm for Distribution Feeder Reconfiguration

Network reconfiguration for loss reduction in a distribution system is a very important way to save the electrical energy. This article proposes a hybrid evolutionary algorithm to solve the distribution feeder reconfiguration problem. The algorithm combines a fuzzy adaptive particle swarm optimization with a differential evolution algorithm, called the fuzzy adaptive particle swarm optimization–differential evolution. The fuzzy adaptive particle swarm optimization includes two parts. The first part is fuzzy adaptive binary particle swarm optimization, which determines the status of tie switches (open or closed), and the second part is fuzzy adaptive discrete particle swarm optimization, which determines the sectionalizing switch number. In the proposed algorithm, due to the results of binary particle swarm optimization and discrete particle swarm optimization algorithms that highly depend on the values of their parameters (such as the inertia weight and learning factors), a fuzzy system is employed to adaptively adjust the parameters during the search process. The differential evolution algorithm is combined with the fuzzy adaptive particle swarm optimization algorithm to improve its performance. The proposed algorithm is tested on two distribution test feeders. The results of simulation show that the proposed method is very powerful and is guaranteed to obtain the global optimization.