Discrete Particle Swarm Optimization Research Articles

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

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.

Improved Algorithm of Partial Transmit Sequence Based on Discrete Particle Swarm Optimization

Orthogonal frequency division multiplexing (OFDM) in 5G has many advantages; however, one of the disadvantages is that the superposition of a large number of subcarriers leads to a high peak-to-average power ratio (PAPR) of the transmit signal. A high PAPR results in high-power amplifier distortion and performance degradation. The partial transmit sequence (PTS) algorithm is commonly used for PAPR reduction. It enumerates all combinations of phase factors, weighs the signal using each phase factor combination, and finds the set of phase factors that minimizes the PAPR value of the OFDM signal. The advantage of the PTS is that it determines the optimal solution through enumeration; however, its major drawback is the higher complexity caused by the use of enumeration. Some studies have introduced the discrete particle swarm optimization (DPSO) algorithm instead of enumeration to determine the optimal solution of the PTS algorithm. As an excellent optimization method, the DPSO algorithm represents each individual as a solution during the optimization. Through iterative updates of the initial population, individuals in the population continuously move closer to the optimal solution. This approach significantly reduces complexity compared with the exhaustive enumeration used in the traditional PTS algorithm. However, the disadvantage of the general DPSO algorithm is that it can result in premature and early convergence, which leads to degradation of the PAPR reduction performance. In this study, we propose an improved method based on the general DPSO-based PTS algorithm, and the improved algorithm MDPSO-PTS adopts dynamic time-varying learning factors, which can find the optimal combination of phase factors more efficiently. The MDPSO-PTS algorithm expands the search space when seeking the optimal combination of phase factors. This avoids the drawback of premature convergence commonly observed in general DPSO-PTS algorithms, preventing early consideration of local optima as global optima. A comparative simulation of the improved MDPSO-PTS algorithm with the general DPSO-PTS algorithm shows that the improved algorithm has stronger PAPR reduction, whereas the complexity remains basically unchanged. A comparative simulation with the traditional PTS algorithm shows a significant reduction in complexity, with only a slight, acceptable loss of reduction performance.

Optimizing the Isolation Forest Algorithm for Identifying Abnormal Behaviors of Students in Education Management Big Data

With the changes in educational models, applying computer algorithms and artificial intelligence technologies to data analysis in universities has become a research hotspot in the field of intelligent education. In response to the increasing amount of student data in universities, this study proposes to use an optimized isolated forest algorithm for recognizing features to detect abnormal student behavior concealed in big data for educational management. Firstly, it uses logistic regression algorithm to update the calculation method of isolated forest weights, and then uses residual statistics to eliminate redundant forests. Finally, it utilizes discrete particle swarm optimization to optimize the isolated forest algorithm. On this basis, improvements have also been made to the traditional gated loop unit network. It merges the two improved algorithm models and builds an anomaly detection model for collecting college student education data. The experiment shows that the optimized isolated forest algorithm has a recognition accuracy of 0.986 and a training time of 1 second. The recognition accuracy of the improved gated loop unit network is 0.965, and the training time is 0.16 seconds. In summary, the constructed model can effectively identify abnormal data of college students, thereby helping educators to detect students' problems in time and helping students to improve their learning status.

A crossover-based multi-objective discrete particle swarm optimization model for solving multi-modal routing problems

Passengers must use a set of modes and vehicles to reach their destination in complicated urban structures. Choosing an optimal route is a complicated optimization problem for these passengers. This study proposes a multi-objective optimization algorithm to solve the routing problem in the urban multi-modal network. The multi-modal network problem considered in this study is a transportation network with subway, Bus Rapid Transit (BRT), taxi, and walking modes. The objective functions determine the optimized route by considering route length, traffic, comfort, and safety. We develop a Crossover-Based Multi-Objective Discrete Particle Swarm Optimization (CBMODPSO) to solve the problem. CBMODPSO has been improved using mutation and crossover operators. Multi-Objective Artificial Bee Colony (MOABC), Multi-Objective Ant Colony Optimization (MOACO), Multi-Objective Biogeography-Based Optimization (MOBBO), Multi-Objective Gray Wolf Optimization (MOGWO), and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) algorithms are used to evaluate and compare the results from the CBMODPSO algorithm. In addition, CBMODPSO results are compared with previous research results. We show the improved algorithm is more repeatable than other algorithms. The CBMODPSO algorithm has a faster convergence rate and is able to get to an optimal solution in a smaller generation number and much less time. The CBMODPSO algorithm is implemented in about one-thirties of the MOBBO duration. Meanwhile, it has a reproducibility of almost twice the MOGWO algorithm.

Parcel consolidation approach and routing algorithm for last-mile delivery by unmanned aerial vehicles

The last-mile delivery by unmanned aerial vehicles (UAVs), emerging from the online grocery retailing industry, has attracted much attention and interest from the scientific and industrial communities. In reality, the online retailing platform receives grocery orders and promises ultrafast delivery service to the customers via a two-echelon logistics and distribution network. The uncertain arrivals of the orders have a nontrivial negative effect on the performance of the whole delivery network, which has not been well studied in the literature. This paper investigates the parcel consolidation policy and the UAVs routes for the last-mile delivery from a transshipment site in a distribution network to the final customers. First, this study proposes a nonmyopia consolidation policy considering the upcoming grocery parcels with random arrival times to minimize the total delivery cost including the possible penalty cost due to the delivery delay to the customer. To make the problem tractable in the real-time computational setting, an approximation method based on Bayesian estimation is proposed to reduce a large number of random arrival scenarios of upcoming parcels to an expected scenario. The problem is then approached with a deterministic model under the expected scenario. Subsequently, a discrete particle swarm optimization (DPSO) algorithm is developed to solve the model. In the algorithm, a novel decoding method is designed to evaluate the particles with respect to the constraints of the load and battery capacities of a UAV and a neighborhood search based on reinforcement learning is developed to improve the quality of the particles in the searching process. The experimental results based on a case study validate the performance of the proposed parcel consolidation approach and the UAV routing algorithm. Some findings are given based on the variations of benchmarks with different distributions of customer locations. The approaches and insights in this paper could be used as a reference for last-mile delivery by UAVs.

Assembly Sequence and Assembly Path Planning of Robot Automation Products Based on Discrete Particle Swarm Optimization

The product assembly process in industrial production is a time-consuming and labour-intensive link, so the use of robots for intelligent assembly can achieve high-efficiency operations to a large extent. During the intelligent assembly process, robots need to carry out three key processes: product information modelling, assembly route planning and assembly sequence planning. The experiment examines assembly sequence planning and assembly route planning. In light of the deficiencies of the current assembly sequence planning technique, we propose a new approach, which is difficult to calculate, the experiment proposes to introduce the DPSO algorithm into the assembly sequence planning model. Meanwhile, to prevent the model from entering local optimal, the experiment adds disturbance operation on the basis of DPSO. That is, a part is randomly selected to be inserted into another position in the disassembly sequence and at the same time, the rest of the parts are moved to form a new disassembly sequence to update the particle's location. In addition to the assembly sequence planning, the experiment aims to improve the planning path obtained by the existing RRT algorithm, which is not smooth enough, that is, to simplify the installation path by reducing the change of direction. The simulation experiment findings demonstrate that the addition of the disturbance to DPSO algorithm successfully prevents the model from entering a local optimum state. The moving distance of the parts before path optimization is 52.0012mm and the number of moving direction changes is 8 times, while the moving distance of the parts after path optimization is reduced to 42.8094mm and the moving direction is changed only 2 times.

Layout optimization for underwater nozzle array of air-lifted artificial upwelling system based on discrete particle swarm algorithm

Artificial upwelling (AU) is widely recognized as a geoengineering tool to enhance oceanic fertility and stimulate the earth's capability of self-healing. Several air-lifted AU systems have been established to promote seaweed growth and facilitate carbon sequestration, for instance, in Norway and China. However, few researchers have addressed the critical issue of optimizing the layout of the underwater nozzle array (UNA), a key component of the air-lifted AU system. In this paper, we propose a novel method to optimize the layout of UNA by introducing and improving the discrete particle swarm optimization (DPSO) algorithm to guide the optimal nozzle positions. Furthermore, we develop the nutrient transport efficiency model to evaluate the performance of the AU system and apply it to investigate a typical AU system, namely the one in Aoshan Bay, China. Our results demonstrate that the proposed DPSO algorithm outperforms the conventional particle swarm optimization algorithm and the nutrient transport efficiency under the optimized UNA layouts was improved by an average of 14.5% to 35.0% compared to randomly generated layouts. These findings suggest that our optimization method could serve as a scientific reference for the engineering design of AU.

Fault Reconfiguration in Distribution Networks Based on Improved Discrete Multimodal Multi-Objective Particle Swarm Optimization Algorithm.

Distribution network reconfiguration involves altering the topology structure of distribution networks by adjusting the switch states, which plays an important role in the smart grid since it can effectively isolate faults, reduce the power loss, and improve the system stability. However, the fault reconfiguration of the distribution network is often regarded as a single-objective or multi-objective optimization problem, and its multimodality is often ignored in existing studies. Therefore, the obtained solutions may be unsuitable or infeasible when the environment changes. To improve the availability and robustness of the solutions, an improved discrete multimodal multi-objective particle swarm optimization (IDMMPSO) algorithm is proposed to solve the fault reconfiguration problem of the distribution network. To demonstrate the performance of the proposed IDMMPSO algorithm, the IEEE33-bus distribution system is used in the experiment. Moreover, the proposed algorithm is compared with other competitors. Experimental results show that the proposed algorithm can provide different equivalent solutions for decision-makers in solving the fault reconfiguration problem of the distribution network.

Node Vulnerability-Aware co-deployment of D-PMUs and FTUs for active distribution networks

This study proposes a co-deployment strategy of distribution-level phasor measurement units (D-PMUs) and feeder terminal units (FTUs) for active distribution networks (ADNs) considering node vulnerability to efficiently monitor and perceive the operational state of ADNs. The proposed measurement deployment strategy preferentially configures measurement devices with high precision and superior monitoring functions on vulnerable nodes while ensuring state estimation (SE) accuracy and cost economy. Based on complex network theory and risk assessment, a vulnerability index reflecting the “bridge” characteristics of nodes was defined. Considering the system monitorability, SE accuracy, and measurement cost, a measurement co-deployment strategy based on a combination weighting method was developed. An improved discrete particle swarm optimization algorithm was proposed to solve the co-deployment model by establishing a new particle coding format, improving the dynamic inertia weight and learning factors, and introducing chaos theory. Simulation results based on the IEEE 33-bus and 119-bus distribution systems verify the effectiveness and show that the proposed strategy not only improves system monitorability but also enhances SE robustness.

Joint coded caching and BS sleeping strategy to reduce energy consumption in 6G edge networks

In the coming sixth-generation mobile communication era, the intensive deployment of Internet of Things (IoT) devices and cellular networks is an irresistible trend, leading to system energy consumption and network traffic increasing sharply. Fortunately, edge caching as a promising technology to reduce system energy consumption and transmission latency is attracting wide attention. Although simply deploying cache in edge network and merely shutting down the idle base stations (BSs) during the idle periods can save certain energy to a certain extent, in this case, the contents with important mission cached in idle BSs cannot be accessed by users that will affect users’ experience. In this paper, we employ coded caching encoded by maximum distance separable (MDS) codes at the network edge, and we propose a joint coded caching and BS sleeping strategy, which utilizes the reconstruction feature of MDS codes to alleviate the impact of BS sleeping. Furthermore, the problem of minimizing energy consumption is studied, and we also design a discrete particle swarm optimization (DPSO) algorithm that is suitable to solve this mixed integer nonlinear programming problem. Simulation results reveal that energy consumption of the joint coded caching and BS sleeping strategy can be significantly decreased over 15.2% when compared with the current state-of-art strategy. Meanwhile, our proposed strategy can also improve the cache hit rate up to a maximum 11.1% compared with the existing strategies.