Discrete Particle Swarm Optimization Research Articles

GA–PSO approach for optimising space‐vector PWM control sequence

Space-vector pulse width modulation (SVPWM) provides several degrees of freedom, which can be optimised to improve the harmonic performance of the three-phase inverter. Genetic algorithm (GA) and immune algorithm (IA) are the two classical probabilistic optimisation algorithms, which are simple in structure and do not need an accurate mathematical model. However, the optimisation accuracy and reliability are low when they optimise the high-dimensional non-linear problem, such as SVPWM control sequence of the three-phase inverter. To cope with these problems, a genetic algorithm–particle swarm optimisation (GA–PSO) is proposed here, which introduces the mutation of GA into discrete PSO. The global and local optimisation ability of the algorithm is greatly improved by the introduction of mutation operation. The results of MATLAB/SIMULINK simulation show that the weighted total harmonic distortion (WTHD) by the optimal SVPWM control sequence based on GA–PSO is 0.199%, which is much better than that of the PSO, IA, and GA. The average generation number of GA–PSO is only 1/500 of IAs. Further experimental data verify that the WTHD by the optimal SVPWM control sequence based on GA–PSO is lower than that of conventional SVPWM and IA.

Feature selection using a set based discrete particle swarm optimization and a novel feature subset evaluation criterion

Feature selection using a set based discrete particle swarm optimization and a novel feature subset evaluation criterion

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.

Flexible job-shop scheduling with tolerated time interval and limited starting time interval based on hybrid discrete PSO-SA: An application from a casting workshop

In this paper, we addressed two significant characteristics in practical casting production, namely tolerated time interval (TTI) and limited starting time interval (LimSTI). With the consideration of TTI and LimSTI, a multi-objective flexible job-shop scheduling model is constructed to minimize total overtime of TTI, total tardiness and maximum completion time. To solve this model, we present a hybrid discrete particle swarm optimization integrated with simulated annealing (HDPSO-SA) algorithm which is decomposed into global and local search phases. The global search engine based on discrete particle swarm optimization includes two enhancements: a new initialization method to improve the quality of initial population and a novel gBest selection approach based on extreme difference to speed up the convergence of algorithm. The local search engine is based on simulated annealing algorithm, where four neighborhood structures are designed under two different local search strategies to help the proposed algorithm jump over the trap of local optimal solution. Finally, computational results of a real-world case and simulation data expanded from benchmark problems indicate that our proposed algorithm is significant in terms of the quality of non-dominated solutions compared to other algorithms.

Particle swarm optimization and discrete artificial bee colony algorithms for solving production scheduling problems

This paper shows the use of Discrete Artificial Bee Colony (DABC) and Particle Swarm Optimization (PSO) algorithm for solving the job shop scheduling problem (JSSP) with the objective of minimizing makespan. The Job Shop Scheduling Problem is one of the most difficult problems, as it is classified as an NP-complete one. Stochastic search techniques such as swarm and evolutionary algorithms are used to find a good solution. Our objective is to evaluate the efficiency of DABC and PSO swarm algorithms on many tests of JSSP problems. DABC and PSO algorithms have been developed for solving real production scheduling problem too. The experiment results indicate that this problem can be effectively solved by PSO and DABC algorithms.

A Proposed Method for Mining Breast Cancer Pattern Using Particle Swarm Optimization

Breast cancer is one of the leading causes of death among women in many parts of the world. In this paper, we have developed an efficient hybrid data mining approach to separate from a population of patients who have and who do not have breast cancer. The proposed data mining approach has consisted of two phases. In first phase, the statistical method will be used to pre-process the data, which can eliminate the insignificant features. It can reduce the computational complexity and speed up the data mining process. In the second phase, we proposed a new data mining methodology, which based on the fundamental concept of the standard particle swarm optimization (PSO), namely discrete PSO. This phase aimed at creating a novel PSO in which each particle was coded in positive integer numbers and had a feasible system structure. Based on the obtained results, our proposed DPSO can improve the accuracy to 98.71%, sensitivity to 100%, and specificity to 98.21%. When compared with the previous research, the proposed hybrid approach shows the improvement in both accuracy and robustness. According to the high quality of our research results, the proposed DPSO data mining algorithm can be used as the reference for deciding on hospital and provide the reference for the researchers.

Discrete honey bees mating optimization (DHBMO) for solving environmental optimization problem using single and multi-objectives optimization

One of the recently proposed algorithms in the field of nature-inspired algorithm is the Honey Bees Mating Optimization (HBMO) algorithm by Haddad and Afshar. The HBMO algorithm simulates the marriage behaviour of bees. The solutions are incrementally constructed by the HBMO algorithm. Therefore, the HBMO algorithm is a swarm-based constructive optimization algorithm. However, no study has reported the modification of HBMO for feature selection. Therefore, we try to develop a discrete honey bees mating optimization (DHBMO) as the modification of HBMO for solving discrete optimization problem especially feature selection to improve the performance of machine vision for predicting water stress in plants. We test the algorithm to solve feature selection problem using single and multi-objectives optimization to optimize water usage in plants for environmental sustainability. The results showed DHBMO has better performance compared to the existed nature-inspired optimization algorithms such as genetic algorithms (GA), simulated annealing (SA) and discrete-particle swarm optimization (discrete-PSO).

A Multi-objective Swarm Intelligence Approach for Field Crews Patrol Optimization in Power Distribution Systems Restoration

A fault on a power distribution system may cause electricity interruption for several consumers, so a good restoration plan is required to decrease such interruptions duration and, consequently, assure the quality of service. Among the measures for service restoration, there is the dispatch of inspection and maintenance crews. The routing of these teams can be classified as a case of the multiple traveling salesman problem. Although involved in series of decision problems, the power distribution system maintenance crews routing is addressed, in the most part of the literature, as a single-objective problem, an instance of a multi-objective one, or as a multi-objective aggregating approach, which generates a single solution in an optimization run, in contrast with the set of equally good solutions, known as Pareto set, the result of a multi-objective problem. In this paper, a Pareto based multi-objective discrete particle swarm optimization approach was applied with the aim of reducing the patrol duration and also the total crews displacement. Wherein the concept of epsilon-dominance was used to update the set of non-dominated solutions, resulting in a good spreading and convergence of them. To promote an uniform exploration of the Pareto set, the selection of the local leaders of the archive was based on square root distance metrics. The Dijkstra algorithm was employed to find the shortest path between two consecutive points of the route of each team. As a result, a set of solutions were obtained for the routing of maintenance crews for power distribution system restoration.

A novel discrete particle swarm optimization algorithm for solving bayesian network structures learning problem

ABSTRACTBayesian network is an effective representation tool to describe the uncertainty of the knowledge in artificial intelligence. One important method to learning Bayesian network from data is to employ a search procedure to explore the space of networks and a scoring metric to evaluate each candidate structure. In this paper, a novel discrete particle swarm optimization algorithm has been designed to solve the problem of Bayesian network structures learning. The proposed algorithm not only maintains the search advantages of the classical particle swarm optimization but also matches the characteristics of Bayesian networks. Meanwhile, mutation and neighbor searching operators have been used to overcome the drawback of premature convergence and balance the exploration and exploitation abilities of the particle swarm optimization. The experimental results on benchmark networks illustrate the feasibility and effectiveness of the proposed algorithm, and the comparative experiments indicate that our algorithm is highly competitive compared to other algorithms.

Topology-Oriented Virtual Network Embedding Approach for Data Centers

© 2018 IEEE. Currently, data centers have become an attractive candidate for users that require IT resources in the form of virtual networks to run their applications. Optimal mapping of the virtual network on the top of the substrate network with resource constraint is called virtual network embedding (VNE) problem. Most of the VNE algorithms are general algorithms for random topology and not suitable for data centers due to particular topological characteristics. To solve the VNE problem in data centers, this paper develops a topology-oriented algorithm based on the Discrete Particle Swarm Optimization (DPSO). We first develop a maximum spanning algorithm to compute the ranking of virtual nodes based on, not only its bandwidth and degree, but also its connectivity in the entire virtual network. Then, the virtual networks are embedded onto the substrate network according to the connectivity ranking result by a DPSO-based algorithm, in which we also propose a topological heuristic information of substrate network and combine it into the particle search process for boosting convergence speed and revenue/cost ratio of substrate network. The evaluation results show that the proposed algorithm can improve the optimization performance of VNE by comparing with a few existing algorithms.

A novel least squares twin parametric insensitive support vector regression

The recently proposed twin parametric insensitive support vector regression, denoted by TPISVR, gets perforce regression performance and is suitable for many cases, especially when the noise is heteroscedastic. However, in the TPISVR, it solves two dual quadratic programming problems (QPPs). Moreover, compared with support vector regression (SVR), TPISVR has at least four regularisation parameters that need regulating, which would affect its practical applications. In this paper, we increase the efficiency of TPISVR from two aspects. First, by introducing the least squares method, we propose a novel least squares twin parametric insensitive support vector regression, called LSTPISVR for short. LSTPISVR attempts to solve two modified primal problems of TPISVR, instead of two dual problems usually solved. Compared with the traditional solution method, LSTPISVR can improve the training speed without loss of generalisation. Second, a discrete binary particle swarm optimisation (BPSO) algorithm is introduced to do the parameter selection. Computational results on several synthetic as well as benchmark datasets confirm the great improvements on the training process of our LSTPISVR.

Construction of Quasi-Cyclic Low Density Parity Check Codes for Magnetic Induction Communication

The magnetic induction (MI) communication has been proved to be an effective method in the wireless underground sensor networks (WUSN). MI communication system based on quasi-cyclic low density parity check (QC-LDPC) codes is expected to improve the transmission performance in WUSN for its good performance and hardware friendliness. In this paper, we aim to construct an optimized QC-LDPC code with low encoding complexity for MI communication system. Firstly, we present a novel method, magnetic induction - protograph extrinsic information transfer (MI-PEXIT) algorithm, which can evaluate the performance of the QC-LDPC codes and predict the distance threshold for successfully transmission of the direct MI communication. Then, we combine the discrete particle swarm optimization (PSO) algorithm with the MI-PEXIT algorithm, and propose a novel scheme DPMIP method to optimize the QC-LDPC codes for MI communication system. Furthermore, two groups of QC-LDPC codes with low encoding complexity are constructed by the proposed DPMIP algorithm, which have different code parameters and different coil parameters. Simulation results validate the superiority of the proposed QC-LDPC codes for MI communication in WUSN, and the proposed codes also can achieve a good trade-off between the complexity and the performance.

Solving the planning and scheduling problem simultaneously in a hospital with a bi-layer discrete particle swarm optimization.

The operating room is one of the most capital-intensive resources for a hospital. To achieve further improvements and to restrict cost increases, hospitals may need to operate more efficiently with the resources they already possess. The paper considers the joint problem of planning and scheduling patients in operating rooms on an operational level (weekly basis) with two objectives: maximizing the overall patients' satisfaction and minimizing the cost of overtime in operating rooms as well as the daily cost of operating rooms and recovery beds, which is NP-hard. The decision problem is solved using a bi-layer discrete particle swarm optimization, introducing a repair mechanism for infeasible solutions, specific operators like crossover, insertion and exchange. Moreover, a gap finding scheduling heuristic is designed to solve the surgical case sequencing problem. We first compare the performance of the proposed solution method to that of Fei et al. for three instances separately, using data of a Chinese hospital. Next, the efficient Pareto solutions for the joint problem are presented. The results show that the bi-layer discrete particle swarm optimization can solve the operating room scheduling efficiently and effectively.

Blind Channel and Data Estimation Using Fuzzy Logic-Empowered Opposite Learning-Based Mutant Particle Swarm Optimization.

Multiple-input and multiple-output (MIMO) technology is one of the latest technologies to enhance the capacity of the channel as well as the service quality of the communication system. By using the MIMO technology at the physical layer, the estimation of the data and the channel is performed based on the principle of maximum likelihood. For this purpose, the continuous and discrete fuzzy logic-empowered opposite learning-based mutant particle swarm optimization (FL-OLMPSO) algorithm is used over the Rayleigh fading channel in three levels. The data and the channel populations are prepared during the first level of the algorithm, while the channel parameters are estimated in the second level of the algorithm by using the continuous FL-OLMPSO. After determining the channel parameters, the transmitted symbols are evaluated in the 3rd level of the algorithm by using the channel parameters along with the discrete FL-OLMPSO. To enhance the convergence rate of the FL-OLMPSO algorithm, the velocity factor is updated using fuzzy logic. In this article, two variants, FL-total OLMPSO (FL-TOLMPSO) and FL-partial OLMPSO (FL-POLMPSO) of FL-OLMPSO, are proposed. The simulation results of proposed techniques show desirable results regarding MMCE, MMSE, and BER as compared to conventional opposite learning mutant PSO (TOLMPSO and POLMPSO) techniques.

Cost-Driven Scheduling for Deadline-Based Workflow Across Multiple Clouds

With the development of cloud computing, the coexistence of multiple cloud service providers appears in the current cloud market. Due to heterogeneous instance types, different bandwidths and various price models among multiple clouds, it is a challenging issue to schedule a deadline-constrained scientific workflow across multiple clouds. Existing research for workflow scheduling are mostly in the traditional distributed computing environment (such as grid), and only a few primal contributions are made in the cloud environment. This paper proposes a scheduling strategy for a deadline-constrained scientific workflow across multiple clouds. In order to minimize the execution cost of the workflow while meeting its deadline, our strategy utilizes the discrete particle swarm optimization technique, and adopts randomly two-point crossover operator and randomly single point mutation operator of the genetic algorithm. Besides, the strategy optimizes the performance for both computation cost and data transfer cost across multiple clouds. Our strategy is evaluated through well-known workflows, and experimental results show that it performs better than other state-of-the-art strategies.

A bi-level programming for transportation services procurement based on combinatorial auction with fuzzy random parameters

Purpose The purpose of this paper is to study a transportation service procurement bid construction problem from a less than a full truckload perspective. It seeks to establish stochastic mixed integer programming to allow for the proper bundle of loads to be chosen based on price, which could improve the likelihood that carrier can earn its maximum utility. Design/methodology/approach The authors proposes a bi-level programming that integrates the bid selection and winner determination and a discrete particle swarm optimization (PSO) solution algorithm is then developed, and a numerical simulation is used to make model and algorithm analysis. Findings The algorithm comparison shows that although GA could find a little more Pareto solutions than PSO, it takes a longer time and the quality of these solutions is not dominant. The model analysis shows that compared with traditional approach, our model could promote the likelihood of winning bids and the decision effectiveness of the whole system because it considers the reaction of the shipper. Originality/value The highlights of this paper are considering the likelihood of winning the business and describing the conflicting and cooperative relationship between the carrier and the shipper by using a stochastic mixed integer programming, which has been rarely examined in previous research.

Multiprocessor task scheduling problem using hybrid discrete particle swarm optimization

Task Scheduling is a complex combinatorial optimization problem and known to be an NP hard. It is an important challenging issue in multiprocessor computing systems. Discrete Particle Swarm Optimization (DPSO) is a newly developed swarm intelligence technique for solving discrete optimization problems efficiently. In DPSO, each particle should limit its communication with the previous best solution and the best solutions of its neighbors. This learning restriction may reduce the diversity of the algorithm and also the possibility of occurring premature convergence problem. In order to address these issues, the proposed work presents a hybrid version of DPSO which is a combination of DPSO and Cyber Swarm Algorithm (CSA). The efficiency of the proposed algorithm is evaluated based on a set of benchmark instances and the performance criteria such as makespan, mean flow time and reliability cost.

Analysis of the Dynamic Changes of the Baiyangdian Lake Surface Based on a Complex Water Extraction Method

Lakes have an important role in human life and the ecological environment, but they are easily affected by human activity and climate change, especially around urban areas. Hence, it is critical to extract water with a high precision method and monitor long-term sequence dynamic changes in lakes. As the greatest natural lake of the Beijing-Tianjin-Hebei region, Baiyangdian Lake has a significant function in human life, socio-economic development, and regional ecological balance. This lake area has shown large changes due to human activity and climate change. The change monitoring process of the water surface is of great significance in providing support for the management and protection of the lake. The Spectrum Matching based on Discrete Particle Swarm Optimization (SMDPSO) method is a new, robust, and low-cost method for water extraction, that has obvious advantages in extracting complex water surfaces. In this paper, the SMDPSO method was used to extract the water surface of Baiyangdian Lake by Landsat images from 1984 to 2018. This method has a good effect on complex water surface extraction with vegetation, shadows, and so forth, and the Landsat images have higher resolution and longer time series. The main contents and results of this paper are as follows: (1) We verified the applicability of the SMDPSO method in the Baiyangdian Lake using visual interpretation and correlation analysis. The relative errors between observed and extracted results were all less than 5% in spring, summer, and fall, and the correlation coefficient between the water area and water level was 0.96. (2) According to seasonal verification and comparison of the extraction results, the SMDPSO method was used to extract the water surface area of Baiyangdian Lake during spring of the years 1984–2018. Water area changes of Baiyangdian Lake can be divided into four periods: Dry period (1984–1988), degraded period (1989–2000), stable period (2000–2008), and recovery period (2008–2018). The water area reached a maximum of 280 km2 in 1989 and a minimum of 44 km2 in 2002. (3) The possible causes of the changes in the water area of Baiyangdian Lake were also analyzed. The changes were caused by climate and human activities during the first and second periods, but mainly human activities during the third and fourth periods. In fact, effective policies combined with water conservancy projects were directly conducive to improving or even recovering the water and ecological environment of Baiyangdian Lake. Considering its importance for the benign development of the Beijing-Tianjin-Hebei Region and the construction of the Xiong’an New Area, a policy is necessary to ensure that the lake’s ecological environment will not be destroyed under the premise of economic development.

A Hybrid Discrete Grey Wolf Optimizer to Solve Weapon Target Assignment Problems

We propose a hybrid discrete grey wolf optimizer (HDGWO) in this paper to solve the weapon target assignment (WTA) problem, a kind of nonlinear integer programming problems. To make the original grey wolf optimizer (GWO), which was only developed for problems with a continuous solution space, available in the context, we first modify it by adopting a decimal integer encoding method to represent solutions (wolves) and presenting a modular position update method to update solutions in the discrete solution space. By this means, we acquire a discrete grey wolf optimizer (DGWO) and then through combining it with a local search algorithm (LSA), we obtain the HDGWO. Moreover, we also introduce specific domain knowledge into both the encoding method and the local search algorithm to compress the feasible solution space. Finally, we examine the feasibility of the HDGWO and the scalability of the HDGWO, respectively, by adopting it to solve a benchmark case and ten large-scale WTA problems. All of the running results are compared with those of a discrete particle swarm optimization (DPSO), a genetic algorithm with greedy eugenics (GAWGE), and an adaptive immune genetic algorithm (AIGA). The detailed analysis proves the feasibility of the HDGWO in solving the benchmark case and demonstrates its scalability in solving large-scale WTA problems.

Development of Discrete-Cockroach Algorithm (DCA) for Feature Selection Optimization

One of the recently proposed algorithms in the field of bio-inspired algorithm is the Hungry Roach Infestation Optimization (HRIO) algorithm. Haven has developed optimization algorithms HRIO that is inspired by recent discoveries in the social behaviour of cockroaches. Result showed that HRIO was effective at finding the global optima of a suite of test functions. However, there is no researcher who has observed HRIO for solving discrete problems. Therefore, we try to develop a discrete-cockroach algorithm (DCA) as the modification of HRIO for solving discrete optimization problem. We test the algorithm to solve bio-computation problem using single and multi-objectives optimization. The results showed DCA has better performance compared to the existed bio-inspired optimization algorithms such as genetic algorithms (GA) and discrete-particle swarm optimization (discrete-PSO).