Discrete Particle Swarm Optimization Method Research Articles

A multi-objective discrete particle swarm optimization method for particle routing in distributed particle filters

Distributed particle filters (PFs) have received extensive attention because of its excellent performance. Generally, it needs to transmit particles to multiple processing units (PUs) in order to improve the performance. However, how to balance communication costs and computation costs in particle routing is still an open problem. This paper presents a multi-objective discrete particle swarm optimization (PSO) algorithm to solve the problem. In the algorithm, the particle routing problem in distributed PFs is modeled as a multi-objective constrained optimization model for the first time. Following that, an improved hybrid discrete multi-objective PSO is proposed. Two new operators designed based on the problem’s characteristics, that is, a local search strategy based on molecular force and a constraint processing mechanism with greedy search, are developed to improve the performance of the proposed algorithm. By comparing with three commonly used methods on classical particle filters problems, experimental results show that the proposed algorithm is a highly competitive approach, and it can provide multiple high-quality Pareto optimal solutions for decision-makers to meet their different needs.

Application of particle swarm optimization for optimal setting of Phase Shifting Transformers to minimize unscheduled active power flows

Today’s interconnected power systems require increased flexibility in control to ensure the capability for maintaining power flows at acceptable limits. Recently, this issue has become increasingly important, mainly due to the significant unscheduled flows that can violate system security. In response to this challenge, the transmission system operators introduced various measures to control unscheduled flows. The most effective are Phase Shifting Transformers (PSTs). A higher number of PSTs operating in the transmission grid increases the total control capabilities. Since the achieved control effects can be both strengthened and weakened because of various actions of individual PSTs, coordinating the settings of the PSTs will achieve the best results. This paper addresses a new, intelligent approach by using a combination of two methods: the discrete particle swarm optimization method for the optimal setting of the individual PSTs and the power flow method for calculating the value of the optimization objective. The case studies consider the IEEE 118-bus test system as well as a model of the real network of Central Eastern Europe. In both cases, the minimization of unscheduled flows was adopted as the objective. As a result of PSTs settings optimization, a significant reduction of unscheduled flows was obtained.

Investigation of the parameters of discrete particle swarm optimization method for problem of finding of optimal tile sizes in program loops

Investigation of the parameters of discrete particle swarm optimization method for problem of finding of optimal tile sizes in program loops

A discrete particle swarm optimization method for assignment of supermarket resources to urban residential communities under the situation of epidemic control

When contagious diseases hit a city, such as MERS, SARS, and COVID-19, the problem arises as how to assign the limited supermarket resources to urban residential communities for government measures. In this study, in order to solve the assignment problem from supermarket resources to urban residential communities under the situation of the epidemic control, the discrete multi-objective particle swarm algorithm can be improved by introducing some new strategies, and the probability matrix can be used to simulate the many-to-many assignment relationship between residential communities and supermarkets. The ultimate purpose of this research is to achieve an optimal way to balance the two conflicting objectives, i.e. minimization of the cross-infection risk and maximization of the service coverage rate. Also, the optimization considers the accessible distance limit and the service capacity constraints of supermarkets for the feasible scheme. For this aim, we redefine the subtraction operator, add operator and multiply operator to generate the Pareto optimal solutions, and introduce a new study strategy based on the idea of differential evolution in the particle swarm algorithm (PSO-DE). In this work, we take the COVID-19 epidemic outbreak in Wuhan city of China as an example in the experiment. The simulation results are compared with the Genetic Algorithm (GA), Simulated Annealing (SA), Ant Colony Algorithm (ACO) and the Particle Swarm Optimization with Roulette Wheel Selection (PSO-R), and these results have been shown that the algorithm PSO-DE proposed in this work has a better optimization performance in both objectives.

Integrated Decisions on Online Product Image Configuration and Inventory Planning Using DPSO

Integrated decisions on merchandise image display and inventory planning are closely related to operational performance of online stores. A visual-attention-dependent demand (VADD) model has been developed to support online stores make the decisions. In the face of evolving products, customer needs, and competitors in an e-commerce environment, the benefits of using VADD model depend on how fast the model runs on the computer. As a result, a discrete particle swarm optimization (DPSO) method is employed to solve the VADD model. To verify the usability and effectiveness of DPSO method, it was compared with the existing methods for large-scale, medium-scale, and small-scale problems. The comparison results show that both GA and DPSO method perform well in terms of the approximation rate, but the DPSO method takes less time than the GA method. A sensitivity is conducted to determine the model parameters that influence the above comparison result.

Dynamic Resource Allocation for LTE-Based Vehicle-to-Infrastructure Networks

This paper studies the dynamic resource allocation (DRA) problem for LTE-based vehicle-to-infrastructure networks, where the goal is to minimize the total power consumption (TPC) in the downlink, subject to both power constraints and rate requirements. Under time-varying channel conditions, the TPC minimization takes the form of a discrete-time sequence of NP-hard combinational optimization problems. To solve these sequential problems, we propose a novel two-stage algorithm, named as DRA and precoding algorithm (DRA-Pre). In the first stage, the resource allocation problem (i.e., pairing of vehicle users to roadside units, and subcarrier allocation) is solved by applying the multi-value discrete particle swarm optimization method. This approach takes advantage of the channel correlation by exploiting the relationship between resource allocation solutions in adjacent time slots, which can improve the TPC performance. In the second stage, the precoding design problem is solved by a low-complexity algorithm, where the original problem is split into two subproblems, i.e., a rate max–min subproblem and a TPC minimization subproblem. Simulation results show that the proposed algorithm converges rapidly and significantly outperforms benchmark approaches in terms of TPC.

Parallel mining of contextual outlier using sparse subspace

In this paper, we present a parallel computing solution for an existing outlier mining method (LOMA)–a local outlier detection technique. As datasets increase radically in size, there is a pressing demand to develop highly scalable outlier detection algorithms that leverages modern distributed and parallel computing infrastructures. It is challenging to devise parallel outlier detection techniques, because sharing and accessing global data across multiple computing nodes inevitably impose I/O overheads. To address this concern, we design a parallel LOMA computing framework called PLOMA, which is composed of three modules, namely, parallel data reduction, local sparse-subspace construction, and sparse-subspace validation. The parallel data reduction module exhibits superb efficiency by the virtue of the sampling technology to compute k nearest neighbor. At the heart of PLOMA is a local sparse-subspace construction module, which extends the discrete particle swarm optimization method to search local sparse subspaces on multiple datanodes in parallel. The validation module allows PLOMA to maintain high mining accuracy by verifying the correctness of the local sparse subspaces obtained by the construction module. Furthermore, PLOMA produces contextual information serving as insightful explanations on detected outliers, thereby achieving high interpretability. We apply PLOMA algorithm to the astronomical spectral data for discovering peculiar celestial objects. Our comprehensive experimental study using a 24-nodes Hadoop cluster confirms that PLOMA achieves high performance in terms of extensibility, scalability, and interpretability.

Discrete particle swarm optimization method for the large-scale discrete time–cost trade-off problem

Despite many research studies have concentrated on designing heuristic and meta-heuristic methods for the discrete time–cost trade-off problem (DTCTP), very little success has been achieved in solving large-scale instances. This paper presents a discrete particle swarm optimization (DPSO) to achieve an effective method for the large-scale DTCTP. The proposed DPSO is based on the novel principles for representation, initialization and position-updating of the particles, and brings several benefits for solving the DTCTP, such as an adequate representation of the discrete search space, and enhanced optimization capabilities due to improved quality of the initial swarm. The computational experiment results reveal that the new method outperforms the state-of-the-art methods, both in terms of the solution quality and computation time, especially for medium and large-scale problems. High quality solutions with minor deviations from the global optima are achieved within seconds, for the first time for instances including up to 630 activities. The main contribution of the proposed particle swarm optimization method is that it provides high quality solutions for the time–cost optimization of large size projects within seconds, and enables optimal planning of real-life-size projects.

Solving Lattice Protein Folding Problems by Discrete Particle Swarm Optimization

Using computer programs to predict protein structures from a mass of protein sequences is promising for discovering the relationship between the protein construction and their functions. In the area of computational protein structure analysis, the hydrophobic-polar (HP) model is one of the most commonly applied models. The protein folding problem based on HP model has been shown as NP-hard, to handle such an NP-hard problem, this paper proposes a discrete particle swarm optimization algorithm (DPSO HP ) to solve various 2D and 3D HP lattice models-based protein folding problems. The discrete particle swarm optimization method used in DPSO HP is based on the set concept and the possibility theory from a set-based PSO (S-PSO). A selection strategy incorporating heuristic information and possibilities is adopted in DPSO HP . A particle’s positions in the algorithm are defined as a set of elements and the velocities of a particle are defined as a set of elements associated with possibilities. The experimental results on a series of 2D and 3D protein sequences show that DPSO HP is promising and performs better than various competitive state-of-the-art evolutionary algorithms.

A novel frequency selective surface of hybrid-element type with sharply decreased stop-band

Frequency selective radome is one of the most important applications of frequency selective surface (FSS). In order to obtain better stealth performance, a novel element FSS, based on a regular slot element FSS, is presented in this paper. The novel element consists of a slot element in the center and at least two slot strips placed on the periodic boundary. We call such FSS the “hybrid-element type FSS” because it exhibits characteristics of both slot type and patch type FSS. Simulation and optimization work is carried out by using a period moment method and a discrete particle swarm optimization method based on the application requirements of a missile radome. Simulation results show that the hybrid-element type FSS has much steeper transition section between pass-band and stop-band, and much lower transmittance in stop-band when compared with the corresponding slot type FSS. The new FSS also has much lower insertion loss in pass-band, much thinner thickness, much simple structure and fabrication process when compared with the ordinary two-layer FSS. Equivalent sample plate is fabricated using printed circuit method and tested using the free space method. Good fit between simulation and testing results verify the accuracy and feasibility of this novel FSS design. The hybrid-element type FSS is especially suitable for the stealth radome when woking frequencies of both sides are very close. It provides a simple and feasible approach for developing frequency selective radome.

A Swarm Intelligence Method Combined to Evolutionary Game Theory Applied to the Resources Allocation Problem

This paper addresses an allocation problem and proposes a solution using a swarm intelligence method. The application of swarm intelligence has to be discrete. This allocation problem can be modelled as a multi-objective optimization problem where the authors minimize the time and the distance of the total travel in a logistic context. This study uses a hybrid Discrete Particle Swarm Optimization (DPSO) method combined to Evolutionary Game Theory (EGT). One of the main implementation issues of DPSO is the choice of inertial, individual, and social coefficients. In order to resolve this problem, those coefficients are optimised by using a dynamical approach based on EGT. The strategies are either to keep going with only inertia, only with individual, or only with social coefficients. Since the optimal strategy is usually a mixture of the three, the fitness of the swarm can be maximized when an optimal rate for each coefficient is obtained. Evolutionary game theory studies the behaviour of large populations of agents who repeatedly engage in strategic interactions. Changes in behaviour in these populations are driven by natural selection via differences in birth and death rates. To test this algorithm, the authors create a problem whose solution is already known. This study checks whether this adapted DPSO method succeeds in providing an optimal solution for general allocation problems.

Voltage regulator placement in radial distribution system using plant growth simulation algorithm

This paper presents selection of optimal locations and sizes of voltage regulators in radial distribution system using Plant Growth Simulation Algorithm (PGSA). Voltage has always been considered as an integral part of the power system response. There are several factors, which contribute to voltage collapse such as increased loading on distribution feeders, reactive power constraints, on load tap changer dynamics and load characteristics. The proposed method for voltage regulator placement is suitable for large radial distribution network. The VR problem consists of two sub problems, that of optimal placement and optimal choice of tap setting. The proposed method deals with initial selection of voltage regulator buses by using power loss indices (PLI). The candidate node identification technique and Plant Growth Simulation Algorithm (PGSA) are used for optimal location and number along with tap setting of the voltage regulators that maintain a smooth voltage profile throughout the network. The aim of this paper is to obtain optimal voltage control with voltage regulators and then to decrease the total cost and losses, to obtain the maximum net savings. The effectiveness of the proposed method is illustrated with 33 bus and 69 bus radial distribution systems and these results are compared with Discrete Particle Swarm Optimization method. International Journal of Engineering, Science and Technology, Vol. 2, No. 6, 2010, pp. 207-217

A discrete particle swarm optimization method for feature selection in binary classification problems

This paper investigates the feature subset selection problem for the binary classification problem using logistic regression model. We developed a modified discrete particle swarm optimization (PSO) algorithm for the feature subset selection problem. This approach embodies an adaptive feature selection procedure which dynamically accounts for the relevance and dependence of the features included the feature subset. We compare the proposed methodology with the tabu search and scatter search algorithms using publicly available datasets. The results show that the proposed discrete PSO algorithm is competitive in terms of both classification accuracy and computational performance.

Modified Particle Swarm Optimization Algorithm for Adaptively Configuring Globally Optimal Classification and Regression Trees

The configuration of classification and regression trees (CART) used to include tree-growing by greedy recursive partitioning, which selects the splitting parameters (i.e., splitting variables and values) involved in tree, and tree-pruning, which aims to obtain a final tree of right size. This method is successful for most applications; however, it presents some well-known limitations and drawbacks, such as, less comprehensibility, inclination to overfitting, and suboptima. In the present study, the modified discrete particle swarm optimization method was invoked to adaptively configure the globally optimal CART (MPSOCART) via simultaneously selecting the optimal splitting parameters in CART and the appropriate structure of CART. A new objective function was formulated to decide the appropriate CART architecture and the optimum splitting parameters. The proposed MPSOCART was applied to predict the bioactivities of flavonoid derivatives and inhibitory activities of inhibitors of epidermal growth factor receptor tyrosine kinase, compared with partial least-squares and CART induced by greedy recursive partitioning. The comparison revealed that MPSO was a useful tool for inducing a globally optimal CART, which converges fast to the optimal solution and avoid overfitting in great extent.