Hybrid Discrete Particle Swarm Optimization Research Articles

Hybrid Discrete Particle Swarm Optimization Algorithm with Genetic Operators for Target Coverage Problem in Directional Wireless Sensor Networks

For a sensing network comprising multiple directional sensors, maximizing the number of covered targets but minimizing sensor energy use is a challenging problem. Directional sensors that can rotate to modify their sensing directions can be used to increase coverage and decrease the number of activated sensors. Solving this target coverage problem requires creating an optimized schedule where (1) the number of covered targets is maximized and (2) the number of activated directional sensors is minimized. Herein, we used a discrete particle swarm optimization algorithm (DPSO) combined with genetic operators of the genetic algorithm (GA) to compute feasible and quasioptimal schedules for directional sensors and to determine the sensing orientations among the directional sensors. We simulated the hybrid DPSO with GA operators and compared its performance to a conventional greedy algorithm and two evolutionary algorithms, GA and DPSO. Our findings show that the hybrid scheme outperforms the greedy, GA, and DPSO algorithms up to 45%, 5%, and 9%, respectively, in terms of maximization of covered targets and minimization of active sensors under different perspectives. Finally, the simulation results revealed that the hybrid DPSO with GA produced schedules and orientations consistently superior to those produced when only DPSO was used, those produced when only GA was used, and those produced when the conventional greedy algorithm was used.

Hybridization of particle swarm optimization and simulated annealing for maximum partition problem

The maximum partition problem (MPP) is to divide the set of nodes in directed weighted graph into two disjoint subsets such that the sum of weights of edges crossing from one subset of the partition to the other is maximized. The MPP is NP-hard. Hence, this paper presents a hybrid discrete particle swarm optimization (DPSO) simulated annealing (SA) algorithm to solve MPP. The proposed algorithm first applies DPSO to the problem until improvement in fitness slows down (stagnates). Then, the algorithm uses SA augmented with a heuristic method to improve the fitness of the solution obtained from DPSO. Experiments on randomly generated graphs of different size show that the proposed algorithm produces better partitions than conventional DPSO. Additionally, the proposed algorithm converges to near optimal solutions faster than conventional DPSO.

Hybridization of particle swarm optimization and simulated annealing for maximum partition problem

The maximum partition problem (MPP) is to divide the set of nodes in directed weighted graph into two disjoint subsets such that the sum of weights of edges crossing from one subset of the partition to the other is maximized. The MPP is NP-hard. Hence, this paper presents a hybrid discrete particle swarm optimization (DPSO) simulated annealing (SA) algorithm to solve MPP. The proposed algorithm first applies DPSO to the problem until improvement in fitness slows down (stagnates). Then, the algorithm uses SA augmented with a heuristic method to improve the fitness of the solution obtained from DPSO. Experiments on randomly generated graphs of different size show that the proposed algorithm produces better partitions than conventional DPSO. Additionally, the proposed algorithm converges to near optimal solutions faster than conventional DPSO.

A hybrid meta-heuristic algorithm for scientific workflow scheduling in heterogeneous distributed computing systems

Cloud computing has attracted great attentions in research community because of its ubiquitous, unlimited computing resources, low cost, and flexibility owing to virtualization technology. This paper presents a hybrid meta-heuristic algorithm to solve parallelizable scientific workflows on elastic cloud platforms since applying a single approach cannot yield optimal solution in such complicated problems. Scientific workflows are modeled in the form of directed acyclic graph (DAG) in which there exists data dependency between sub-tasks. In the cloud marketplace, each provider delivers variable virtual machine (VM) configurations which lead different performance. Generally, parallelizable task scheduling on parallel computing machines to obtain minimum total execution time, makespan, belongs to NP-Hard problem. To deal with the combinatorial problem, the hybrid discrete particle swarm optimization (HDPSO) algorithm is presented that has three main phases. At the first phase a random algorithm following by novel theorems is applied to produce swarm members; it is as input of presented new discrete particle swarm optimization (DPSO) algorithm in the second phase. To avoid getting stuck in sub-optimal trap and to balance between exploration and exploitation, local search improvement is randomly combined in DPSO by calling Hill Climbing technique at the third phase to enhance overall performance. Second and third phases are iterated till the termination criteria is met. The average results reported from different executions of intensive settings on 12 scientific datasets proved our hybrid meta-heuristic has the amount of 10.67, 14.48, and 3 percentage dominance in terms of SLR, SpeedUp, and efficiency respectively against other existing meta-heuristics.

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.

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

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.

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.

Bike-Sharing Static Rebalancing by Considering the Collection of Bicycles in Need of Repair

Bike-sharing systems, which are used in many cities worldwide, need to maintain a balance between the availability of bicycles and the availability of unoccupied bicycle slots. This paper presents an investigation of the net flow of each bike-sharing station in Jersey City. The data was recorded at 1-minute intervals. The sum of the initial bicycle number and the minimum net flow value was determined to be the demand for static rebalancing, and this led to the proposal of a bike-sharing demand prediction method based on autoregressive integrated moving average models. Considering that the existence of bicycles in a state of disrepair may adversely affect demand prediction and routine planning, we present an integer linear programming formulation to model bike-sharing static rebalancing. The proposed formulation takes into account the problem introduced by the need to collect bicycles in need of repair. A hybrid Discrete Particle Swarm Optimization (DPSO) algorithm was proposed to solve the model, which incorporates a reduced variable neighborhood search (RVNS) functionality together with DPSO to improve the global optimization performance. The effectiveness of the algorithms was verified by a detailed numerical example.

A hybrid DPSO with Levy flight for scheduling MIMO radar tasks

In this paper, an optimization model for the multiple-input and multiple-output (MIMO) radar task scheduling is established, and a hybrid discrete particle swarm optimization (DPSO) algorithm with Levy flight is proposed for a solution to the model. The optimization model takes the task internal structure, the characteristics of task scheduling in the MIMO radar and the three task scheduling principles into consideration. The hybrid DPSO integrates a heuristic task interleaving algorithm for the task schedulability analysis of candidate scheduling schemes (particles) with a DPSO with Levy flight for exploring the best solution. The heuristic task interleaving algorithm not only exploits the wait interval to interleave subtasks, but also incorporates transmit intervals and overlaps receive intervals in order to make a maximum utilization of the radar timeline. The DPSO is combined with Levy flight to escape from local optima by utilizing the long jump property. In addition, the chaos initialization and the linearly decreasing inertia weight are designed to enhance the exploration ability and the exploitation ability. The simulation results verify the outperformance of the proposed algorithm compared with the existing ones.

Optimal load shedding by a new binary PSO

Purpose The voltage stability is a basic principle in the power system operation. Different short circuits, load growth, generation shortage, and other faults which disturb the voltage stability are serious threats to the system security. The voltage instability causes dispersal of a power system into sub-systems, and leads to blackout as well as heavy damages of the system equipments. Optimum load shedding during contingency situations is one of the most important issues in power system security analysis. Design/methodology/approach In this paper, a New Binary Particle Swarm Optimization technique (NB-PSO) is proposed for solving the integer-valued modeling of under-voltage load shedding (UVLS) problem. The updating mechanisms for the position and velocity of binary particles are amended in the proposed NB-PSO by using a new velocity definition, which has an excellent efficiency for solving complex binary optimization problems. Findings The effectiveness and capability of the proposed NB-PSO optimization algorithm were illustrated according to the simulation results of applying it to the IEEE 118-bus test system. In addition, the performance of the proposed NB-PSO based method was compared with other optimization algorithms, such as the Binary Particle Swarm Optimization (BPSO) and Hybrid Discrete Particle Swarm Optimization (HDPSO) techniques. It was perceived that the NB-PSO performs superior than the BPSO and HDPSO for determining the best location and the minimum level of load shedding in order to prevent voltage instability. Originality/value The proposed NB-PSO has novel modifications and techniques to enhance both exploration and exploitation capabilities to find the optimal feasible solution. The simulation results confirmed the effectiveness of the proposed method in determining the best location and the minimum amount of load shedding for voltage collapse prevention.

A hybrid discrete particle swarm optimization for dual-resource constrained job shop scheduling with resource flexibility

In this paper, a novel hybrid discrete particle swarm optimization algorithm is proposed to solve the dual-resource constrained job shop scheduling problem with resource flexibility. Particles are represented based on a three-dimension chromosome coding scheme of operation sequence and resources allocation. Firstly, a mixed population initialization method is used for the particles. Then a discrete particle swarm optimization is designed as the global search process by taking the dual-resources feature into account. Moreover, an improved simulated annealing with variable neighborhoods structure is introduced to improve the local searching ability for the proposed algorithm. Finally, experimental results are given to show the effectiveness of the proposed algorithm.

Hybrid Discrete Particle Swarm Optimization for Task Scheduling in Grid Computing

Computational Grid is a high performance computing environment that participating machines resources are used through software layer as transparent and reliable. Task assignment problem in Grid Computing is a NP-Complete problem that has been studied by several researchers. The most common objective functions of task scheduling problems are Makespan and Flowtime. This paper gives a classification of meta-heuristic scheduling algorithms in distributed computing that are applicable to grid environment and addresses scheduling problem of independent tasks on Computational Grids. A Hybrid Discrete Particle Swarm Optimization and Min-min algorithm (HDPSO) is presented to reduce overall Completion Time of task.

On-Orbit Servicing Task Allocation for Multi-Spacecrafts Using HDPSO

By analyzing the key indicators which contain the value of the target spacecrafts, the attrition of servicing spacecrafts and consumption of time and fuel, a mathematical model is formulated. And a hybrid discrete particle swarm optimization (HDPSO) algorithm is proposed. Simulation results show that the algorithm can efficiently solve the multi-spacecrafts task allocation problem under multiple constraints.

Solving the balance problem of massively multiplayer online role-playing games using coevolutionary programming

In massively multiplayer online role-playing games (MMORPGs), each race holds some attributes and skills. Each skill contains several abilities such as physical damage and hit rate. All those attributes and abilities are functions of the character's level, which are called Ability-Increasing Functions (AIFs). A well-balanced MMORPG is characterized by having a set of well-balanced AIFs. In this paper, we propose a coevolutionary design method, including integration with the modified probabilistic incremental program evolution (PIPE) and the cooperative coevolutionary algorithm (CCEA), to solve the balance problem of MMORPGs. Moreover, we construct a simplest turn-based game model and perform a series of experiments based on it. The results indicate that the proposed method is able to obtain a set of well-balanced AIFs more efficiently, compared with the simple genetic algorithm (SGA), the simulated annealing algorithm (SAA) and the hybrid discrete particle swarm optimization (HDPSO) algorithm. The results also show that the performance of PIPE has been significantly improved through the modification works.

Facility Allocation based on Hybrid Discrete PSO under Emergency

Facility Allocation based on Hybrid Discrete PSO under Emergency

Delay-area trade-off for MPRM circuits based on hybrid discrete particle swarm optimization

Polarity optimization for mixed polarity Reed—Muller (MPRM) circuits is a combinatorial issue. Based on the study on discrete particle swarm optimization (DPSO) and mixed polarity, the corresponding relation between particle and mixed polarity is established, and the delay-area trade-off of large-scale MPRM circuits is proposed. Firstly, mutation operation and elitist strategy in genetic algorithm are incorporated into DPSO to further develop a hybrid DPSO (HDPSO). Then the best polarity for delay and area trade-off is searched for large-scale MPRM circuits by combining the HDPSO and a delay estimation model. Finally, the proposed algorithm is testified by MCNC Benchmarks. Experimental results show that HDPSO achieves a better convergence than DPSO in terms of search capability for large-scale MPRM circuits.

Hybrid discrete particle swarm optimization for multi-objective flexible job-shop scheduling problem

Flexible job-shop problem has been widely addressed in literature. Due to its complexity, it is still under consideration for research. This paper addresses flexible job-shop scheduling problem (FJSP) with three objectives to be minimized simultaneously: makespan, maximal machine workload, and total workload. Due to the discrete nature of the FJSP problem, conventional particle swarm optimization (PSO) fails to address this problem and therefore, a variant of PSO for discrete problems is presented. A hybrid discrete particle swarm optimization (DPSO) and simulated annealing (SA) algorithm is proposed to identify an approximation of the Pareto front for FJSP. In the proposed hybrid algorithm, DPSO is significant for global search and SA is used for local search. Furthermore, Pareto ranking and crowding distance method are incorporated to identify the fitness of particles in the proposed algorithm. The displacement of particles is redefined and a new strategy is presented to retain all non-dominated solutions during iterations. In the presented algorithm, pbest of particles are used to store the fixed number of non-dominated solutions instead of using an external archive. Experiments are performed to identify the performance of the proposed algorithm compared to some famous algorithms in literature. Two benchmark sets are presented to study the efficiency of the proposed algorithm. Computational results indicate that the proposed algorithm is significant in terms of the number and quality of non-dominated solutions compared to other algorithms in the literature.

Erratum to: A hybrid discrete particle swarm optimization algorithm for solving fuzzy job shop scheduling problem

Erratum to: A hybrid discrete particle swarm optimization algorithm for solving fuzzy job shop scheduling problem