Discrete Particle Swarm Optimization Research Articles

Influence maximization based on discrete particle swarm optimization on multilayer network

Influence maximization (IM) aims to strategically select influential users to maximize information propagation in social networks. Most of the existing studies focus on IM in single-layer networks. However, we have observed that individuals often engage in multiple social platforms to fulfill various social needs. To make better use of this observation, we consider an extended problem of how to maximize influence spread in multilayer networks. The Multilayer Influence Maximization (MLIM) problem is different from the IM problem because information propagation behaves differently in multilayer networks compared to single-layer networks: users influenced on one layer may trigger the propagation of information on another layer. Our work successfully models the information propagation process as a Multilayer Independent Cascade model in multilayer networks. Based on the characteristics of this model, we introduce an approximation function called Multilayer Expected Diffusion Value (MLEDV) for it. However, the NP-hardness of the MLIM problem has posed significant challenges to our work. To tackle the issue, we devise a novel algorithm based on Discrete Particle Swarm Optimization. Our algorithm consists of two stages: 1) the candidate node selection, where we devise a novel centrality metric called Random connectivity Centrality to select candidate nodes, which assesses the importance of nodes from a connectivity perspective. 2)the seed selection, where we employ a discrete particle swarm algorithm to find seed nodes from the candidate nodes. We use MLEDV as a fitness function to measure the spreading power of candidate solutions in our algorithm. Additionally, we introduce a Neighborhood Optimization strategy to increase the convergence of the algorithm. We conduct experiments on four real-world networks and two self-built networks and demonstrate that our algorithms are effective for the MLIM problem.

A Novel Set-Based Discrete Particle Swarm Optimization for Wastewater Treatment Process Effluent Scheduling.

With the escalating severity of environmental pollution caused by effluent, the wastewater treatment process (WWTP) has gained significant attention. The wastewater treatment efficiency and effluent quality are significantly impacted by effluent scheduling that adjusts the hydraulic retention time. However, the sequential batch and continuous nature of the effluent pose challenges, resulting in complex scheduling models with strong constraints that are difficult to tackle using existing scheduling methods. To optimize maximum completion time and effluent quality simultaneously, this article proposes a restructured set-based discrete particle swarm optimization (RS-DPSO) algorithm to address the WWTP effluent scheduling problem (WWTP-ESP). First, an effective encoding and decoding method is designed to effectively map solutions to feasible schedules using temporal and spatial information. Second, a restructured set-based discrete particle swarm algorithm is introduced to enhance the searching ability in discrete solution space via restructuring the solution set. Third, a constraint handling strategy based on violation degree ranking is designed to reduce the waste of computational resources. Fourth, a Sobel filter based local search is proposed to guide particle search direction to enhance search efficiency ability. The RS-DPSO provides a novel method for solving WWTP-ESP problems with complex discrete solution space. The comparative experiments indicate that the novel designs are effective and the proposed algorithm has superior performance over existing algorithms in solving the WWTP-ESP.

Vibration-attitude integrated control of large membrane diffraction space telescope

The development of telescopes with large apertures is driven by the increasing demand for higher imaging resolution and coverage. Large membrane diffraction space telescopes are particularly attractive due to their lightweight nature, ease of folding and unfolding, and high-precision imaging capabilities. However, their substantial size and flexibility make them susceptible to long-duration, low-frequency vibrations during attitude maneuvers, which degrade attitude and imaging accuracy and risk instrument damage. Consequently, an urgent need exists for a high-precision integrated control scheme. In this paper, the integrated control of vibration and attitude in a large space telescope is studied using cable actuators and control moment gyroscopes (CMGs). Since cables can only withstand limited tension, the unilateral and constrained characteristics of the control input are taken into account during the control design process. Firstly, a rigid-flexible coupling dynamic model of the space telescope is established using the velocity variation principle with hybrid coordinates. Additionally, a two-body astrodynamic model is developed to assess the impact of gravity gradient torque. Next, combining the computational torque method and H∞ control theory, an integrated control scheme is proposed, which achieves vibration and attitude control during maneuvers. Then, this paper optimizes the cable actuator positions via the discrete particle swarm optimization (PSO) algorithm and plans various attitude maneuver paths. Finally, numerical simulations are adopted to demonstrate the effectiveness of the control scheme. The results show that this integrated control scheme swiftly suppresses structural vibrations during attitude maneuvers, enabling high-precision attitude control of the space telescope.

Retraction Note: Rigorous reduction of partial shading condition in grid connected solar PV system using discrete time-based PSO controller

Retraction Note: Rigorous reduction of partial shading condition in grid connected solar PV system using discrete time-based PSO controller

The integration of En route flow optimization, complex network clustering, and rule-based approach to airspace sub-sectorization for enhanced air traffic monitoring

This study proposes the use of a novel integrated framework for 2D en route airspace sub-sectorization. The integrated framework combines the multi-commodity flow optimization approach, complex network clustering approach, and Minimum Bounding Geometry (MBG)-coupled Rule-based Approach for boundary design. A decomposition-based discrete particle swarm optimization (DPSO) is used to solve the clustering problem. The output of the flow optimization is used as a guiding standard for the DPSO. Experimentations were performed using the Indian airspace sector to validate the framework and DPSO was run for different maximum number of generations (maxgen). The findings reveal that the multi-commodity flow approach captures system-wide flow operations. Clustering results corresponding to maxgen=100 and maxgen=150 perform best in terms of equitable and balanced distribution of cluster size and traffic load. The MBG-coupled Rule-based Approach leads to compact and convex sub-sector boundary design. Major implications of this research include dynamic adaptability of the integrated framework, increased sensitivity of sector design to network evolution, and a computationally tractable framework. The higher controllability of the proposed framework also offers an increased acceptance among practitioners.

Mechanical performance simulation and optimal design of carbon fiber composite B-pillar

This study primarily concentrates on the layer optimization and structural refinement of carbon fiber composite B-pillars. In the initial phase, an initial layer configuration for the carbon fiber composite material B-pillar was devised, followed by comprehensive finite element numerical simulations. The weight of the B-pillar was effectively diminished by 44.9% compared to its metal counterpart, while maintaining consistent performance across diverse operational conditions. Subsequently, an advanced layer thickness optimization model was formulated, incorporating the innovative ‘super layer’ concept. Integration of simulation software, such as ISIGHT and ABAQUS, facilitated the determination of optimal layer thickness and ratio for the composite material. The layer order was systematically optimized through the application of a discrete particle swarm optimization algorithm based on exchange order, adeptly addressing issues related to discontinuous design variables and potential combinations. The incorporation of a ply drop-off structure laying scheme was derived, resulting in an impressive weight reduction of 57.5%. In the final phase, adherence to NCAP side collision testing standards enabled a comprehensive evaluation, where in various indicators including deformation mode, intrusion amount, and intrusion speed were employed. The results substantiate that the composite B-pillar exhibits equivalent side impact resistance to the original metal B-pillar, ensuring robust passenger protection.

Optimization of the Shunt Currents and Pressure Losses of a VRFB by Applying a Discrete PSO Algorithm

This paper presents an extensive study on the electrochemical, shunt currents, and hydraulic modeling of a vanadium redox flow battery of m stacks and n cells per stack. The shunt currents model of the battery has been developed through the use of Kirchoff’s laws, taking into account the different design cases that can occur and enumerating the equations of nodes and meshes specifying them so that the software implementation can be performed in a direct way. The hydraulic model has been developed by numerical methods. These models are put to work simultaneously in order to simulate the behavior of a VRFB battery during charging and discharging, obtaining the pressure losses and shunt currents that occur in the battery. Using these models, and by using a PSO-type optimization algorithm, specifically designed for discrete variables, the battery design is optimized in order to minimize the round-trip efficiency losses due to pressure losses and shunt currents. In the optimization of the battery design, value is given to the number of stacks in which the total number of cells in the battery is distributed and the dimensions of the piping relative to both the stacks and the cells.

Discrete Particle Swarm Optimization for Coordinated Robust Configuration of Photovoltaic Energy Storage in Distribution Network

Discrete Particle Swarm Optimization for Coordinated Robust Configuration of Photovoltaic Energy Storage in Distribution Network

A unit commitment method of pumped storage in renewable energy system via a twofold particle swarm optimization algorithm

Abstract The problem of unit commitment of pumped storage power stations (UCPS) is rarely discussed in existing works of multi-energy hybrid systems scheduling. It goes against accurately evaluating the operation performance and reliability of the pumped storage unit (PSU). This study proposes a co-optimization scheduling method for a Wind-Photovoltaic-Pumped storage system based on a twofold particle swarm optimization (TPSO). At the system layer, a standard PSO algorithm is used to optimize the output of the pumped storage plant (PSP). At the PSP layer, an improved discrete PSO based on three discrete variables is used to solve the UCPS problem. It uses the constraint patching and the micro-increment rate method, respectively, with detailed consideration of the constraints on PSP and the characteristics of the PSU. Finally, the results of a case study show that refined UCPS can reduce the regulation intensity indicators such as operating duration by more than 25%, with better consistency with the characteristics of units. This work could provide a guideline for improving the operation benefits and reliability of the PSP.

Autonomous Mission Planning for Spacecraft On-orbit Service Based on Hybrid Variant Particle Swarm Optimization

For the complex on-orbit service (OOS) mission planning problem, an improved hybrid variant particle swarm optimization (PSO) method was proposed to improve the fuel utilization efficiency of spacecraft. Firstly, considering the time and fuel constraints, a model for OOS mission planning under the scenario of multiple target spacecrafts is established. The Lambert orbit maneuver method is used and the problem is transformed into a large-scale hybrid nonlinear integer programming problem. Next, an improved hybrid variant PSO algorithm is designed to optimize the fuel performance cost, and its simple structure and lightweight operation facilitate autonomous planning, considering the limited computing power of the onboard computer. The hybrid algorithm is composed of discrete particle swarm optimization (DPSO) and PSO, so it has a strong ability to search for the optimal service sequence and orbit maneuver time simultaneously. Moreover, the adaptive and variant operators are used to improve the searching ability and avoid falling into local optimal solutions. Finally, simulation experiments show that the proposed method can quickly give optimal results, reducing fuel consumption effectively in the OOS mission and improving the OOS capability of the spacecraft.

Research on airport apron planning strategy in emergency situations

In events of local war or highly infectious epidemics, airports face the problem of how to park numerous non-missioned aircraft on aprons. In this study, the apron planning problem in emergency situations (APPES) was considered to replan existing parking stands and taxiways, thus determining the numbers and locations of different aircraft types. A mixed-integer programming formulation for the APPES was developed to maximise the total revenue for the apron, where the aircraft was planned to be parked based on factors such as the safety distance and aircraft taxiing method. A two-stage approach based on the discrete particle swarm optimisation algorithm and bottom-left algorithm was designed to solve the APPES. Random instances of different sizes were generated, and the experimental results demonstrated the effectiveness of the proposed approach. The two-stage approach could find optimal solutions to the APPES for 19 of the 20 small instances and could well approximate optimal solutions for the medium and large instances within a reasonable timeframe. Parking priority factor and aircraft taxiing methods sensitivity analyses were also designed to develop a strategy that can adhere to the preferences of decision makers or increase the total revenue.

A BPSO based collaborative management platform for multi-source data security in power grids

By designing a digital power grid multi-source data security collaborative management platform, the system configuration problem of the OMS system and the power grid management platform for the main distribution network of the power grid is solved. A design method for the digital power grid multi-source data security collaborative management platform based on discrete particle swarm optimization algorithm is proposed. Based on the design concept of SOA, realize the overall design framework of the platform according to the design method of multi-layer technical system in the business presentation layer, business process and composition layer, service layer, component layer and resource layer, realize the basic layer design of the system management platform through the basic application platform design scheme of XML configuration, implement the query, processing and output representation of the grid’s multivariate data using B/S architecture protocol, and use the Spring Framework The platform software architecture is implemented using J2EE technology and multi module component design scheme. The discrete particle swarm optimization algorithm is used for the fusion and scheduling of multi-source data in the digital power grid. The interface design and functional construction of the power grid management platform are implemented in the OMS system of the power grid main distribution network, and the logical model of the transformation project is constructed to achieve platform optimization and construction. Tests have shown that the designed digital power grid multi-source data security collaborative management platform has good human-machine interaction, strong data fusion scheduling ability, reduced resource and subsystem coupling, and supports the flexibility of physical deployment and maintenance.

An enhanced discrete particle swarm optimization for structural k-Anonymity in social networks

Recently, social network usage has exhibited explosive growth, leading to a huge amount of users’ private data available. The main challenge in releasing social network data publicly is the protection of the users’ privacy while preserving its utility for third parties. Accordingly, several social network privacy-preserving methods have been introduced, where anonymization is the most common approach. Structural k-anonymity is a widely used anonymization model to mask the structure of social networks by clustering the edges and nodes into super-edges and super-nodes. However, it comes at the cost of losing structural information, which is measured by a criterion called structural information loss (SIL). This study introduces an enhanced discrete particle swarm optimization (EDPSO) algorithm, which effectively minimizes the SIL within the clustering process of the structural k-anonymity model, leading to a high-utility anonymized network. In this regard, we propose a vector-based solution representation that can be efficiently exploited by the EDPSO. Moreover, a novel position updating heuristic is suggested for the EDPSO, which adaptively tunes the operators’ selection probabilities. This happens based on each operator’s performance both in the current iteration and their history regarding the number and the average amount of fitness improvements in the previous iterations. We also propose two fortified versions of the EDPSO algorithm (EDPSOVNS and EDPSOSA) by employing two new network-specific local search strategies to enhance the exploration, exploitation, and convergence rate of the process. Simulation results on the nine real-world networks demonstrate the superiority of the suggested algorithms in terms of the fitness value, reliability, and convergence rate over other analyzed approaches found in the literature.

Scheduling optimization of flexible flow shop with buffer capacity limitation based on an improved discrete particle swarm optimization algorithm

To address the challenge of production scheduling in flexible flow shops with buffer capacity limitations, a mathematical model is established to minimize the maximum completion time and maximize equipment utilization. To efficiently solve the problem, an improved discrete particle swarm optimization algorithm with multi-population reduction (MPREDPSO) is developed. It integrates an elite retention strategy, which significantly contributes to its efficiency and robustness. The algorithm stands out for its ability to effectively navigate through potential local optima, a common pitfall in optimization problems, through a Euclid algorithm coupled with a sophisticated gene backtracking mechanism. The performance of MPREDPSO is rigorously tested and proven through comparative analyses. Its effectiveness is proven through comparisons with other algorithms, namely the discrete particle swarm algorithm, genetic algorithm, whale algorithm, grey wolf optimizer and whale swarm algorithm. In addition, MPREDPSO’s practical utility is demonstrated in a high-temperature ham sausage production workshop, showcasing its industrial application capabilities.

Multi-Objective Dynamic Reconstruction of Distributed Energy Distribution Networks Based on Stochastic Probability Models and Optimized Beetle Antennae Search

In the dynamic optimization problem of the distribution network, a dynamic reconstruction method based on a stochastic probability model and optimized beetle antennae search is proposed. By implementing dynamic reconstruction of distributed energy distribution networks, the dynamic regulation and optimization capabilities of the distribution network can be improved. In this study, a random probability model is used to describe the uncertainty in the power grid. The beetle antennae search is used for dynamic multi-objective optimization. The performance of the beetle antennae search is improved by combining it with the simulated annealing algorithm. According to the results, the optimization success rate of the model was 98.7%. Compared with the discrete binary particle swarm optimization algorithm and bacterial foraging optimization algorithm, it was 9.3% and 26.1% faster, respectively. For practical applications, this model could effectively reduce power grid transmission losses, with a reduction range of 16.7–18.6%. Meanwhile, the charging and discharging loads were effectively reduced, with a reduction range of 16.2–19.7%. Therefore, this method has significant optimization effects on actual power grid operation. This research achievement contributes to the further development of dynamic reconstruction technology for distribution networks, improving the operational efficiency and stability of the power grid. This has important practical significance for achieving green and intelligent operation of the power system.

Multi-day tourism recommendations for urban tourists considering hotel selection: A heuristic optimization approach

With the development of tourism, digital technology is increasingly being applied in the design of tourist routes. This study takes into account that tourists are experience-driven in tourism activities and hotel selections. In this study, the tourist trip design problem with hotel selection is formulated based on bi-objective optimization with total utility of the points of interest maximization and the average utility of the hotels maximization, and a three-step hybrid algorithm combined with discrete particle swarm optimization, an adaptive differential evolution with an optional external archive, and a local search is designed to identify the optimal route. To examine the performance of the designed algorithm, a numerical experiment was conducted. The results of Wilcoxon rank sum tests verified that the proposed algorithm performed distinctly better than extant approaches. Moreover, the results also indicate that the two main innovative mechanisms about initialization and hybrid evolution play a critical role in improving the algorithm's efficiency for the tourist trip design problem with hotel selection.

Mathematical modelling and a discrete cuckoo search particle swarm optimization algorithm for mixed model sequencing problem with interval task times

Mathematical modelling and a discrete cuckoo search particle swarm optimization algorithm for mixed model sequencing problem with interval task times

Enhanced PAPR reduction in DCO-OFDM using multi-point constellations and DPSO optimization

DC-biased optical OFDM (DCO-OFDM) is a commonly used method of OFDM in visible light communication (VLC). Unfortunately, VLC systems that use OFDM often experience a high peak-to-average power ratio (PAPR). To address this issue, this study proposes a novel method called the multi-point constellation method (MPC) to reduce PAPR in DCO-OFDM. The MPC method involves adding extra alternative constellation points around the existing points and using the discrete particle swarm optimization (DPSO) algorithm to select the constellation points with the lowest PAPR. The proposed MPC method is also combined with selective mapping (SLM), a well-known PAPR reduction technique in the literature. Simulation results show that the proposed MPC method outperforms the SLM method in reducing PAPR in 4-QAM and 16-QAM modulations when used in combination with SLM. Furthermore, increasing the number of iterations and particles in the DPSO algorithm improves the PAPR reduction performance of the proposed method even further.

A dynamic incentive mechanism for data sharing in manufacturing industry

Data sharing is a critical component in a blockchain traceability platform. Therefore, creating a reasonable incentive mechanism to ensure that all enterprises participate in data sharing is vital for blockchain platforms. Currently, many researchers employ evolutionary game theory to analyze problems related to data sharing. However, evolutionary game theory typically assumes that the population composed of enterprises is mixed uniformly. Enterprises in the manufacturing industry are not uniformly mixed, as they tend to have specific connections with each other due to the size of enterprises and volume of business. Therefore, a networked evolutionary game is introduced to solve this problem. Firstly, an incentive model for enterprises sharing data is established. Then, a scale-free network is employed to simulate the connections between enterprises. To comprehensively consider the individual and group benefits of enterprises in the game, this study designs a strategy update rule for networked evolutionary game based on Discrete Particle Swarm Optimization and Variable Neighborhood Descent algorithm. To tackle the challenge of determining reasonable incentive values in networked evolutionary games, this study proposes a dynamic incentive mechanism based on the Q-Learning algorithm. Finally, the experiments indicate that this method can successfully facilitate the stable involvement of enterprises in data sharing.

Balanced allocation of teaching information resources based on discrete particle swarm optimisation algorithm

Balanced allocation of teaching information resources based on discrete particle swarm optimisation algorithm