The BPSO Based Complex Splitting of Context-Aware Recommendation

Harmony search (HS), inspired by the music improvisation process, is a new meta-heuristic optimization method and has been used to tackle various optimization problems in discrete and continuous space successfully. However, the standard HS algorithm is not suitable for settling discrete binary problems. To extend HS to solve the binary-coded problems effectively, a novel discrete binary harmony search (DBHS) algorithm is proposed in this paper. A new pitch adjustment rule is developed to enhance the optimization ability of DBHS. Then parameter studies are performed to investigate the properties of DBHS, and the recommended parameter values are given. The results of numerical experiments demonstrate that the proposed DBHS is valid and outperforms the discrete binary particle swarm optimization algorithm and the standard HS.

Reducing the volatility in the operation of public transport is still an important consideration when seeking to maintain efficient and stable operations. In this study, stations and intersections are considered as a whole, and a self-coordinated holding strategy is developed based on the fluctuation of passenger flows and the characteristics of signals during off-peak periods. On this basis, a collaborative optimization is carried out in combination with signal priority and speed control. The mathematical model is established with the goal of minimizing passenger travel and enterprise operation costs. A section of the K2 line in Ganzhou City is selected for a case study. The improved discrete binary particle swarm optimization algorithm (DPSO) is then used for the calculations. The simulation and comparison of the arrival time distribution, headway deviation, and travel time index before and after the optimization show that reliability, stability, and efficiency have been improved by the optimization, which verifies the effectiveness of the optimization method.

Interruptible load management plays an important role in maintaining the reliability and security of power systems and reducing price spike of power markets, thus has been widely used in power systems. Interruptible loads dispatch problem with multi-period and multi-interruptible-load is usually a multi-objective, combinatorial optimization problem, and its solution method needs to be carefully investigated. Application of intelligent optimization algorithms to the multi-period interruptible load dispatch problem is discussed in this paper. Multi objectives, such as minimizing the interruption payments and interruption frequency, can be included in the proposed interruptible load dispatch model. The characteristics of different interruptible loads are also considered. Numerical simulations using discrete binary particle swarm optimization (BPSO) and the genetic algorithm (GA) show that the BPSO can obtain better solutions than that of the GA. Thus the BPSO presents better potentials in solving the interruptible load dispatch problem.

In emotion recognition, many irrelevant and redundant features will affect recognition results, so feature selection is necessary.Aimed at emotion physiological signal feature selection, this paper proposed with improved discrete binary particle swarm optimization(BPSO) to increase the correct classification rate of emotion state.When recognizing four emotional states with nearest classifier by four physiological signals, the whole correct recognition rate is up to 85%.Experimental results demonstrate that the BPSO is an effective way to emotion physiological signals feature selection.

The booming cloud computing, together with edge computing, lays the computing resource foundation for larger and more granular simulation. To achieve better execution of the simulation in cloud and edge environments, it is necessary to assign simulation components to cloud computing centers and edge computing nodes appropriately. This is an optimization problem and a particle swarm optimization(PSO) algorithm is proposed for this purpose. However, deployment problems are a kind of discrete optimization problem and PSO algorithm was originally designed to solve continuous optimization problems, thus, various discretization methods had been proposed to modify original PSO algorithm to handle discrete problems. Unsatisfactorily, many discrete PSO algorithms suffer from random movements of particles due to improper use of the guidance information obtained from the global best particle and personal best particle. To counter this challenge, we proposed a selection-based discrete PSO algorithm(SPSO) and conducted simulation experiments on MATLAB platform. The results showed that SPSO got better performance in simulation components allocation problems compared with NSGA-II and other pure discrete PSO algorithms.

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.

A high call blocking rate is a consequence of an inefficient utilization of system resources, which is often caused by a load imbalance in the network. Load imbalances are common in wireless networks with a large number of cellular users. This paper investigates a load-balancing scheme for mobile networks that optimizes cellular performance with constraints of physical resource limits and users quality of service demands. In order to efficiently utilize the system resources, an intelligent distributed antenna system (IDAS) fed by a multi base transceiver station (BTS) has the ability to distribute the system resources over a given geographic area. To enable load balancing among distributed antenna modules we dynamically allocate the remote antenna modules to the BTSs using an intelligent algorithm. A self-optimizing network for an IDAS is formulated as an integer based linear constrained optimization problem, which tries to balance the load among the BTSs. A discrete particle swarm optimization (DPSO) algorithm as an evolutionary algorithm is proposed to solve the optimization problem. The computational results of the DPSO algorithm demonstrate optimum performance for small-scale networks and near-optimum performance for large-scale networks. The DPSO algorithm is faster with marginally less complexity than an exhaustive search algorithm.

A novel discrete particle swarm optimization algorithm for meta-task assignment in heterogeneous computing systems

The wireless mesh network is a new emerging technology exploiting the advantages of multiple radios and multiple channels. In this paper we have studied the channel assignment problem which is to assign channels to links in the network, and aimed at minimizing the network interference so as to improve the network throughput. The channel assignment problem is proved to be NP-hard. In this paper, we have developed a new approach based on the particle swarm optimization (PSO) algorithm and a heuristic algorithm to solve channel assignment problem in multi-radio multi-channel wireless mesh networks. Meanwhile, the crossover operation is employed in the discrete particle swarm optimization (DPSO) algorithm to handle the discrete channel assignment problem. Experimental results have shown that the rate of interference in the DPSO algorithm descends by 67.67% compared with the Tabu-based algorithm when there are 12 channels and 5 radios. And the heuristic algorithm can also yield good solutions with good quality in both dense and sparse networks.

A binary discrete particle swarm optimization satellite selection algorithm with a queen informant for Multi-GNSS continuous positioning

A multi-objective discrete particle swarm optimization (MODPSO) algorithm is useful in accurately identifying communities in a network by avoiding the pitfalls of modularity optimized discrete PSO algorithms. Inertia weights in a PSO can be used to guide the flight of particles in PSO by modifying step size of the particles. In this paper, we present a new adaptive inertia weight based MODPSO and compare it with other good inertia weight approaches by applying them to three real-world datasets. Our algorithm demonstrates consistently the best results among various inertia weight strategies in three real-world datasets with maximum Q (modularity score) values of 0.457, 0.527728 and 0.60457 for Zachary’s Karate Club, Bottlenose Dolphins and American College Football datasets, respectively. Adaptive inertia weight strategy is able to perform consistently by adaptively determining the step size of the velocity update equation. To the best of our knowledge, this is the first such attempt to explore the adaptive inertia weight technique with MODPSO in the field of community detection in complex networks.

In recent years a number of works have been done to extend Particle Swarm Optimization (PSO) to solve multi-objective optimization problems, but a few of them can be used to tackle binary-coded problems. In this paper, a novel modified multi-objective binary PSO (MMBPSO) algorithm is proposed for the better multi-objective optimization performance. A modified updating strategy is developed which is simpler and easier to implement compared with standard discrete binary PSO. The mutation operator and dissipation operator are introduced to improve the search ability and keep the diversity of algorithm. The experimental results on a set of multi-objective benchmark functions demonstrate that the proposed MBBPSO is a competitive multi-objective optimizer and outperforms the standard binary PSO algorithm in terms of convergence and diversity.

Coordinated controller tuning of the boiler turbine unit is a challenging task due to the nonlinear and coupling characteristics of the system. In this paper, a new variant of binary particle swarm optimization (PSO) algorithm, called probability based binary PSO (PBPSO), is presented to tune the parameters of a coordinated controller. The simulation results show that PBPSO can effectively optimize the control parameters and achieves better control performance than those based on standard discrete binary PSO, modified binary PSO, and standard continuous PSO.

In this paper, we propose a formulation of the unit commitment problem based on the profit under the deregulated electricity market(PBUC). We express the unit commitment problem as a mixed integer nonlinear optimization problem in which the objective is to maximize profits for generation company and the decisions are required to meet all kinds of operating constraints. Under the assumption of competitive market and price forecasting, we developed an improved discrete binary particle swarm optimization(PSO) and standard value PSO to solve this PBUC problem iteratively. A generation company with 10 generating units is used to demonstrate the effectiveness of the proposed approach. Simulation results are compared with those obtained from reference method.

BackgroundReconstructing ancestral genomes is one of the central problems presented in genome rearrangement analysis since finding the most likely true ancestor is of significant importance in phylogenetic reconstruction. Large scale genome rearrangements can provide essential insights into evolutionary processes. However, when the genomes are large and distant, classical median solvers have failed to adequately address these challenges due to the exponential increase of the search space. Consequently, solving ancestral genome inference problems constitutes a task of paramount importance that continues to challenge the current methods used in this area, whose difficulty is further increased by the ongoing rapid accumulation of whole-genome data.ResultsIn response to these challenges, we provide two contributions for ancestral genome inference. First, an improved discrete quantum-behaved particle swarm optimization algorithm (IDQPSO) by averaging two of the fitness values is proposed to address the discrete search space. Second, we incorporate DCJ sorting into the IDQPSO (IDQPSO-Median). In comparison with the other methods, when the genomes are large and distant, IDQPSO-Median has the lowest median score, the highest adjacency accuracy, and the closest distance to the true ancestor. In addition, we have integrated our IDQPSO-Median approach with the GRAPPA framework. Our experiments show that this new phylogenetic method is very accurate and effective by using IDQPSO-Median.ConclusionsOur experimental results demonstrate the advantages of IDQPSO-Median approach over the other methods when the genomes are large and distant. When our experimental results are evaluated in a comprehensive manner, it is clear that the IDQPSO-Median approach we propose achieves better scalability compared to existing algorithms. Moreover, our experimental results by using simulated and real datasets confirm that the IDQPSO-Median, when integrated with the GRAPPA framework, outperforms other heuristics in terms of accuracy, while also continuing to infer phylogenies that were equivalent or close to the true trees within 5 days of computation, which is far beyond the difficulty level that can be handled by GRAPPA.