Discrete Particle Swarm Optimization Research Articles

A hybrid localization model using node segmentation and improved particle swarm optimization with obstacle-awareness for wireless sensor networks

Other than energy consumption, precision is of the utmost importance in node localization. Various wireless-sensor-network applications require the accurate information of sensor nodes’ locations. For instance, an enemy intrusion detection system (e.g., geo-fencing) needs accurate sensor nodes’ locations to detect where intruding enemies are located. As practical examples, forest fire, landslide, and water quality monitoring systems require the early identification of root causes’ exact locations before they can widely spread. In general, range-based localization techniques often yield higher accuracies because the localization estimation can be directly derived from the distance between hops and can leverage received signal strength indicator (RSSI) values but require model approximation of various hops and distances as in range-free localization techniques. However, the important factor that affects the accuracies is sensor node positioning, especially when sensor nodes (SNs) are spread across areas filled with obstructions causing less localization accuracy. Due to the diffraction caused by obstructions, the approximate distances between pairs of anchor nodes and unknown nodes using RSSI can differ substantially from the actual values. This research, therefore, aims to improve sensor node localization in situations where SNs are in areas with obstructions. We propose a novel technique, node segmentation with improved particle swarm optimization (NS-IPSO) that divides SNs into segments to improve the accuracy of the estimated distances between pairs of anchor nodes and unknown nodes. First, we determine candidate sensor nodes that could potentially be used to segment anchor nodes in the area. Such sensor nodes (STs) are those on the shortest paths between anchor nodes that appear more often than the average appearances of all sensor nodes. Then, segment nodes (SMs, sensor nodes for segmenting the anchor nodes) are selected from all the other STs based on certain specified conditions. To further improve the localization precision, we enhance the fitness function for each anchor node by taking into account the number of hops between each anchor node and unknown nodes. Furthermore, we enhance particle swarm optimization (PSO) by considering only particles that do not change positions to possibly reduce the chance of the local optimal trap. In this research, we test our proposed scheme's performance considering three forms of sensor node positioning: C-shape, H-shape, and S-shape. The simulation results show that the proposed scheme achieves higher accuracy in comparison with the recent state-of-the-art methods, i.e., hybrid discrete PSO (HDPSO), Hybrid PSO, approximate distances node localization (ADNL), the weight-search localization algorithm (WSLA), and min-max PSO techniques, particularly the situation where sensor nodes are in areas with obstacles.

A discrete cooperatively coevolving particle swarm optimization algorithm for combinatorial double auctions

A combinatorial double auction is a type of double-side auction which makes buyers and sellers trade goods more conveniently than multiple combinatorial auctions. In this paper, we consider the combinatorial double auction problem in which there are transaction costs, supply constraints and surplus constraints. We formulate the WDP of combinatorial double auction problem as an integer programming problem formulation. The winner determination problem (WDP) in combinatorial double auctions poses a challenge due to computational complexity. Particle swarm optimization (PSO) is a well-known approach to deal with complex optimization problems. In the existing literature, different variants of PSO algorithms have been proposed. However, there still lacks a comparative study on effectiveness of applying different variants of PSO algorithms in combinatorial double auctions. As standard discrete PSO (DPSO) algorithm suffers from premature convergence problem, we adopt a coevolution approach to develop a discrete cooperatively coevolving particle swarm optimization (DCCPSO) algorithm that can scale with the problem. The effectiveness of the proposed algorithm is verified by comparing the results with several variants of PSO algorithms and Differential Evolution algorithms through simulation. Simulation results indicate that the proposed DCCPSO algorithm significantly outperforms these variants of PSO algorithms and Differential Evolution algorithms in most test cases of combinatorial double auctions.

An efficient discrete PSO coupled with a fast local search heuristic for the DNA fragment assembly problem

This paper focuses on Particle Swarm Optimization (PSO) applied to the DNA fragment assembly problem. Existing PSO algorithms for this permutation-based combinatorial problem use the Smaller Position Value (SPV) rule to transform continuous vectors into permutations of integers. However, this approach has limitations and is not suitable for this NP-hard problem. Here we propose a new discrete PSO that works directly in the search space of permutations and effectively addresses the fragment assembly problem. In our proposal, the fact that relative ordering of DNA fragments is most indicative of assembly accuracy is exploited in the particle update mechanism. This is implemented through a new operator called Probabilistic Edge Recombination (PER). This operator builds a new position through the probabilistic recombination of edges (adjacency relations) between fragments from the current position, the personal best, and the group best. Additionally, we design variants of the proposed PSO algorithm by applying heuristic information and/or local search. With this aim, we develop a new fast variant of the best state-of-the-art local search algorithm for the assembly problem. Extensive experiments have been conducted to demonstrate the efficiency and effectiveness of the algorithms used. In comparison with the state-of-the-art assembly techniques, our algorithms achieve a better performance.

Optimization of m-MDPDPTW Using the Continuous and Discrete PSO

Optimization of m-MDPDPTW Using the Continuous and Discrete PSO

An Improved Multiobjective Discrete Particle Swarm Optimization for Hyperspectral Endmember Extraction

Endmember extraction (EE) is a significant task in hyperspectral unmixing. From a multiobjective optimization perspective, this task is extremely challenging because objectives often conflict with each other. Currently, a multiobjective discrete particle swarm optimization algorithm (MODPSO) is applied to handle the multiobjective optimization EE problem such as the root-mean-square error (RMSE) and the volume maximization (VM). However, in MODPSO, the minimization of RMSE by unconstrained least squares (Ucls) may lack accuracy, the update of velocity by the predefined random selection probability ${p}$ can also affect the exploration and exploitation, and it may lose good solution in the process of the update of particles when the particles are randomly chosen in the nondominated relationship. To address these issues, we present an improved MODPSO (IMODPSO) for hyperspectral EE. IMODPSO employs nonnegative constrained least squares (Ncls) to enhance the accuracy of RMSE. Moreover, IMODPSO eliminates the effects of probability ${p}$ and combines the restart mechanism to achieve a balance of the exploration and exploitation. In addition, IMODPSO utilizes the archive strategy to reserve good nondominated particles to strengthen the population diversity. The experiments have been conducted on three real hyperspectral images and the results have demonstrated that IMODPSO obtains best performances for EE.

Selective discrete particle swarm optimization for the team orienteering problem with time windows and partial scores

This paper introduces the Team Orienteering Problem with Time Windows and Partial Scores (TOPTW-PS), which is an extension of the Team Orienteering Problem with Time Windows (TOPTW). In the context of the TOPTW-PS, each node is associated with a set of scores with respect to a set of attributes. The objective of TOPTW-PS is to find a set of routes that maximizes the total score collected from a subset of attributes when visiting the nodes subject to the time budget and the time window at each visited node. We develop a mathematical model and propose a discrete version of the Particle Swarm Optimization (PSO), namely, the Selective-Discrete PSO (S-DPSO), to solve TOPTW-PS. The proposed S-DPSO uses four different movement schemes to move a particle from its current position. The best movement scheme is selected to determine the next position of the particle. To evaluate the performance of the proposed S-DPSO algorithm, we first test S-DPSO on two variants of Orienteering Problem, namely, Team Orienteering Problem (TOP) and TOPTW. Experimental results show that S-DPSO performs well in solving benchmark instances of TOP and TOPTW. In general, S-DPSO is comparable to the state-of-the-art algorithms for these problems. We also apply the S-DPSO to solve 168 newly generated TOPTW-PS instances and conclude that the proposed S-DPSO can produce high-quality TOPTW-PS solutions.

The Discrete Particle Swarm Optimization Algorithms for Permutation Flowshop Scheduling Problem

In this paper, two types of discrete particle swarm optimization (DPSO) algorithms are presented to solve the Permutation Flow Shop Scheduling Problem (PFSP). We used criteria to minimize total earliness and total tardiness. The main contribution of this study was that a new position update method is developed based on the discrete domain because PFSP is represented as discrete job permutations. In addition, this article also comes with a simple case study to ensure that both the proposed algorithm can solve the problem well in a short computational time. Hybrid Discrete Particle Swarm Optimization (HDPSO) has a better performance than the Modified Particle Swarm Optimization (MPSO). The HDPSO produced the optimal solution. However, it has a slightly longer computation time. Besides, the population size and maximum iteration impact the quality of solutions produced by HDPSO and MPSO algorithms.

The Discrete Particle Swarm Optimization Algorithms For Permutation Flowshop Scheduling Problem

In this paper, two types of discrete particle swarm optimization (DPSO) algorithms are presented to solve the Permutation Flow Shop Scheduling Problem (PFSP). We used criteria to minimize total earliness and total tardiness. The main contribution of this study is a new position update method is developed based on the discrete domain because PFSP is represented as discrete job permutations. In addition, this article also comes with a simple case study to ensure that both proposed algorithm can solve the problem well in the short computational time. The result of Hybrid Discrete Particle Swarm Optimization (HDPSO) has a better performance than the Modified Particle Swarm Optimization (MPSO). The HDPSO produced the optimal solution. However, it has a slightly longer computation time. Besides the population size and maximum iteration have any impact on the quality of solutions produced by HDPSO and MPSO algorithms

Mining Reusable Software Components from Object-Oriented Source Code using Discrete PSO and Modeling Them as Java Beans

Object-based software development in the IT-industry during the last decade has focused on reusing the already developed elements. Current software development models and technologies promote software reuse by utilizing component-oriented programming. Component-oriented programming provides more effective reuse as compared to its object-oriented language counterpart. This is because a component provides more abstractness in terms of the functionality in contrast to the object-oriented software element (e.g. class/ interface). The component-based development is highly dependent on the availability of the component library that contains well-defined and already tested functionality in terms of the available components. However, the suitable availability of such component library is still a problem. The lack of a suitable component library is tackled either by designing new components from scratch or by identifying them from the existing software systems. These identified components must be transformed as per component models, such as a Java Bean, in order to increase software reuse and faster software development, and little research has been carried out in this direction. Although many approaches have been proposed to identify components from existing systems and build a component library. But, such approaches lack in transforming the identified components as per specifications of a component model. Further identification of reusable elements also needs to be more accurate and efficient. Accuracy is dependent on the structural and semantic properties of a component. A component can consist of a single or a group of individual elements of the software, and thus identifying components from a set of all elements becomes an exponentially growing problem. Such problems can be more efficiently solved using metaheuristic algorithms. Therefore, this paper targets identifying accurately the components from a singular existing software system and transform those identified components to the Java Bean component model standard. The component identification approach of this paper is based on clustering the underlying software system by utilizing a metaheuristic search-based PSO algorithm. The clusters are identified as a group containing 2/ 3/ 4 software elements. Moreover, the used fitness function is based on the structural (based on usage for member variables) and semantic relations belonging to a software. Since the PSO algorithm is generally applied to continuous problems, whereas our formulated component identification problem is discrete (due to the representation of particles) in nature. Therefore, we have also provided a mechanism through which the PSO algorithm is modified to become applicable to discrete problems. From the obtained clusters (components), the interfaces of the components are identified by identifying independent chains in call-graphs of the underlying components. Finally, these components are transformed as per the Java Bean component model specifications. The proposed approach is validated experimentally on 7 open-source object-oriented Java software systems and results are compared with competitive approaches. The obtained results confirm the correctness of the transformed component and hence their reusability.

Runtime analysis of discrete particle swarm optimization algorithms: A survey

Abstract A discrete particle swarm optimization (PSO) algorithm is a randomized search heuristic for discrete optimization problems. A fundamental question about randomized search heuristics is how long it takes, in expectation, until an optimal solution is found. We give an overview of recent developments related to this question for discrete PSO algorithms. In particular, we give a comparison of known upper and lower bounds of expected runtimes and briefly discuss the techniques used to obtain these bounds.

Learning Path Model based on Revised Bloom’s Taxonomy and Domain Ontologies using Discrete Particle Swarm Optimization

Learning Path Model based on Revised Bloom’s Taxonomy and Domain Ontologies using Discrete Particle Swarm Optimization

An Improved Quantum-Behaved Particle Swarm Optimization for Endmember Extraction

Endmember extraction (EE) plays an important role in the quantitative analysis of hyperspectral images, as the main step in the decomposition of mixed pixels. At present, scholars have proposed many EE algorithms based on the linear spectral mixture model and the convex geometry principle, such as the pixel purity index (PPI) and the vertex component analysis (VCA). At the same time, many intelligent optimization algorithms, such as the particle swarm optimization (PSO) and the discrete PSO (DPSO), have been applied to EE, which can get promising results for real images. However, PSO and DPSO have theoretical limitations and cannot guarantee the global convergence. The problem of premature convergence will reduce the accuracy of the EE result. The quantum-behaved PSO (QPSO) can theoretically guarantee the convergence of the algorithm by combining the quantum mechanics into the PSO. In order to increase the accuracy of the algorithm, this paper proposes an improved QPSO (IQPSO) algorithm for EE. IQPSO has made innovations in population coding and initialization methods. Besides, the collaborative approach for updating the optimal positions of particles can help to solve the difficulties caused by high dimensions. The experimental results show that IQPSO can extract endmembers efficiently and effectively.

Economic Dispatch of Power System with Wind Power and Energy Storage Based on Discrete Particle Swarm Optimization

Economic Dispatch of Power System with Wind Power and Energy Storage Based on Discrete Particle Swarm Optimization

Solving Optimization of a Mine Gas Sensor Layout Based on a Hybrid GA-DBPSO Algorithm

To expand the monitoring range of the coal mine gas monitoring subsystem and achieve the timely early-warning of the local gas transfinite accident, the covering model of sensor was established. It can realize the function of using the least number of gas sensors to monitor the change of gas concentration in the whole mine. The objective function and constraint conditions of the established model were determined. A hybrid GA-DBPSO algorithm combining the genetic algorithm with the discrete binary particle swarm optimization algorithm was proposed to solve the gas sensor location set covering model. This research result was applied to investigate the gas sensor layout of Baoxin Coal Mine in China, and gas sensor layout schemes were obtained under the condition of different gas sensor shortest alarm time. The relationship between the shortest alarm time and the number of additional gas sensor was given, which can provide guidance and reference for the enterprises managers to make decisions on layout scheme of gas sensors.

A Self-Adaptive Discrete PSO Algorithm with Heterogeneous Parameter Values for Dynamic TSP.

This paper presents a discrete particle swarm optimization (DPSO) algorithm with heterogeneous (non-uniform) parameter values for solving the dynamic traveling salesman problem (DTSP). The DTSP can be modeled as a sequence of static sub-problems, each of which is an instance of the TSP. In the proposed DPSO algorithm, the information gathered while solving a sub-problem is retained in the form of a pheromone matrix and used by the algorithm while solving the next sub-problem. We present a method for automatically setting the values of the key DPSO parameters (except for the parameters directly related to the computation time and size of a problem).We show that the diversity of parameters values has a positive effect on the quality of the generated results. Furthermore, the population in the proposed algorithm has a higher level of entropy. We compare the performance of the proposed heterogeneous DPSO with two ant colony optimization (ACO) algorithms. The proposed algorithm outperforms the base DPSO and is competitive with the ACO.

An Effective Optimization Algorithm for Application Mapping in Network-on-Chip Designs

The application mapping problem is an NP-hard combinatorial optimization problem in network-on-chip (NoC) design. Applications of size (n > 30) cannot be solved optimally by an exact algorithm in reasonable time, and the evolutionary algorithms have drawn the attention of NoC researchers. In this paper, we propose a new effective optimization method based on the discrete particle swarm optimization framework, which includes the novel principles for representation, velocity computing, and position-updating of the particles. In our proposed method, particles are allowed to swing between elite and regular pools, and a simple local search procedure is applied on elite particles to exploit the promising solutions. Extensive computational studies using standard benchmark instances and task graphs for free (TGFF) random instances reveal that the proposed optimization algorithm is able to attain the best results, and thus competes very favorably with the previously proposed heuristic approaches. A stability analysis and the two-sided Wilcoxon rank sum tests are also presented to shed light on the robust behavior of the algorithm.

Studies of thermal deformation and shape control of a space planar phased array antenna

Large planar phased array antenna in space will inevitably occur deformation due to the space thermal environment, which could severely influence the pointing accuracy of the antenna. This paper focuses on the studies of thermal load and shape adjustment method of a planar phased array antenna structure. The finite element method is adopted both to develop the structural model and to obtain the steady temperature field of the antenna structure, based on which a novel shape adjustment method that utilizes cables as actuators is proposed. This method transforms the control problem into the optimization problem of the actuators placement. The optimal placements of actuators and the corresponding control forces are determined by using discrete PSO method and quadratic optimization method. A 100 meter scale antenna structure is presented as example to validate the effectiveness of the proposed method numerically. Simulation results indicate that this method could successfully control the thermally induced deformation of the structure and maintain antenna's shape accuracy.

Neural-like encoding particle swarm optimization for periodic vehicle routing problems

The periodic vehicle routing problem (PVRP) is an important problem in the logistics field and involves finding the solutions to two sub-problems, namely (1) determining the optimal customer service mode; and (2) establishing the optimal vehicle routing schedule in accordance with the pre-determined customer service mode. However, existing solutions for the PVRP consider only the vehicle routing problem. In other words, they simply assume that the optimal customer service mode is known in advance. Accordingly, the present study proposes a dual particle swarm optimization (PSO) framework for solving both sub-problems simultaneously. In particular, a discrete PSO (DPSO) algorithm is applied on the first level, in the outer layer, to establish the optimal service mode for each customer, and a neural-like DPSO (NDPSO) is then applied in the inner layer to determine (1) the optimal assignment of the depot vehicles to the customers which are to be serviced each day based on the customer service mode established in the outer layer (i.e., the “customer-vehicle correspondence” problem), and (2) the sequence of customer visits to be paid by each vehicle each day (i.e., the “optimal vehicle routing” problem). For both PSOs, a sweep heuristic is applied to generate diverse initial solutions for the particle search process. In addition, an alternative depot savings algorithm is proposed to avoid the route crossing phenomenon inherent in conventional vehicle routing algorithms. The performance of the inner layer NDPSO is evaluated by comparing the solutions for six common PVRPs with the best known solutions (BKS) presented in the literature given a prior knowledge of the optimal customer service mode in every case. The performance of the NDPSO is further investigated with and without the alternative depot savings algorithm and with and without the use of a local search mechanism to enhance the quality of the PSO solutions, respectively. Finally, the feasibility of the full DPSO and NDPSO framework is confirmed by comparing the PVRP solutions with the BKS reported in previous studies. In general, the results confirm the validity of the proposed dual-PSO framework as a tool for finding optimal solutions to both the customer service mode problem and the optimal vehicle routing problem in typical PVRPs.

GPU-based DPSO algorithm for structural optimization of Pt-Co bimetallic nanoparticles

Alloy nanoparticles (NPs) are potential candidates for catalysts in fuel cells applications, and their physical properties are associated with the corresponding stable structures. In this work, a GPU-based discrete particle swarm optimization (DPSO) method is designed to investigate the stable structure of Pt-Co alloy NPs. Register and an optimal block design are used to further increase the acceleration ratio. Comparative experiments on CPU and GPU show that the GPU-based DPSO algorithm possesses superior computational performance, and Register has a great influence on the acceleration ratio. Analyses on the stable structures of Pt-Co NPs demonstrate that Co atoms preferentially locate at vertex and edges of surface, yet Pt atoms exhibit a strong surface and sub-surface segregation only for their composition over 65%.

Set based particle swarm optimization for the feature selection problem

Selecting the correct features when training a classification algorithm, has a significant impact on the performance of the classifier. More features provide more information, but can lead to overfitting and utilizing features that are redundant, irrelevant or too noisy. The feature selection problem (FSP) is concerned with identifying those features, from the entire set of features, that lead to the best possible classification. This article evaluates the performance of the set based particle swarm optimization (SBPSO) algorithm on the FSP. SBPSO was specifically developed to solve discrete-valued optimization problems that can be formulated as set-based problems. A wrapper based SBPSO algorithm based on a k-nearest neighbor classifier is proposed in this paper. The SBPSO wrapper algorithm was compared to three other discrete PSO wrapper algorithms on a large number of datasets of different sizes and outperformed, with statistical significance, the other algorithms on the FSP. The SBPSO algorithm can thus be considered an effective tool for solving the FSP.