Evolutionary Particle Swarm Optimization Research Articles

Enhancing Concurrent Emergency Response: Joint Scheduling of Emergency Vehicles on Freeways with Tailored Heuristic

Scheduling decisions for concurrent emergency response (CER) across multiple disaster sites presents numerous difficulties. The main challenge is to minimize human casualties while taking into account the rationality of resource allocation across different disaster sites. This paper establishes a joint scheduling model for emergency vehicles on freeways in the context of CER. The model aims to minimize the transportation time, dispatch cost, and casualty risk, by using the resource site scheduling scheme as the decision variable, addressing multiple disaster and resource sites. Specifically, a casualty risk function based on the rescue waiting time was designed to balance the competing needs among various disaster sites, enhance equitable resource allocation, and reduce the probability of casualties. To achieve global convergence in a high-dimensional solution space, a tailored heuristic algorithm called adaptive dual evolutionary particle swarm optimization (ADEPSO) is proposed. The numerical results show that the scheduling scheme proposed by the ADEPSO algorithm satisfies all constraints and demonstrates significant advantages in large-sized instances. Compared to the two basic algorithms, ADEPSO provides a more cost-effective scheme and reduces the average rescue waiting time. Moreover, integrating the casualty risk function significantly decreases the average rescue waiting time at both high- and low-priority disaster sites, thereby directly lowering the casualty risk.

Natural logarithm particle swarm optimization for loss reduction in an island power system

In an island power system, optimizing energy management is fundamental since there are renewable sources with their limitations. This management includes the allocation and capacity of energy sources to supply the loads. In this context, optimizing losses in the system contributes to improve the efficiency of this management. This paper proposes the losses optimization and energy management in the island power system. The authors propose the Natural Logarithm Particle Swarm Optimization to solve the problem and compare it with the Attractor Point Algorithm and Evolutionary Particle Swarm Optimization. And with that, we also propose a particle initialization for the studied particle-based algorithms to guarantee convergence in radial power systems. This is because the system configuration influences the response of the algorithm convergence. These techniques were applied to the IEEE-34 unbalanced radial island system.•Natural Logarithm Particle Swarm Optimization differs from classical PSO in that it does not calculate the velocity of the particles. Therefore, the method considers a cloud of particles with a natural logarithmic trajectory to solve the reduction of losses in a power system with a radial topology.•Natural Logarithmic Particle Swarm Optimization uses an initialization equation to minimize the initial estimation process, which is relevant to the convergence process.

Differential evolutionary particle swarm optimization with orthogonal learning for wind integrated optimal power flow

This study develops a novel variant of particle swarm optimization (PSO), which improves its balance of exploration and exploitation by modifying neighborhood topology, self-adaptive parameter strategies and deep search, namely differential evolutionary evolution PSO with orthogonal learning (OL), i.e., DEEPSO-OL in short. Evolutionary computing can explore the solution space efficiently because of its self-evolving attribute as iteration continues. The OL enhances its exploitation by focusing on deeper search for promising solutions. It utilizes the concept of orthogonal experimental design (OED) which predicts the best combination of control variables without exhaustive evaluation of all possible combinations. In addition, to avoid premature convergence in a local optimum, a stochastic star topology for particles is proposed. Such topology ensures just enough communication among the best performing particles, while encouraging them to explore other spaces. The efficacy of the algorithm is evaluated through real-world scenarios such as optimal power flow (OPF) and wind integrated OPF, which are hard to solve with classical mathematical methods. The proposed algorithm is run on a modified IEEE 30-bus test system and compared to the state-of-the-art evolutionary computing algorithms for a variety of cost objective functions with high levels of non-linearity and non-convexity. The DEEPSO-OL demonstrates its performance to generate more accurate feasible solutions and construct promising and efficient search method for real-world complex optimization problems.

Identification of High and Low Voltage Ride-Through Control Parameters for Electromechanical Transient Modeling of Photovoltaic Inverter

The electromechanical transient model of a photovoltaic (PV) inverter’s high and low voltage ride-through has complex operating circumstances and a large number of control parameters, which makes parameter adjustment difficult. Furthermore, it is frequently challenging to identify a single set of control parameters that can successfully handle a variety of operating conditions. The Improved Differential Evolution Particle Swarm Optimization (IDEPSO) algorithm is proposed in this paper to provide a control parameter identification technique for high and low voltage ride-through that addresses these problems. Taking a 320kW PV inverter of a certain company as the research object, based on the specified current control strategy of high and low voltage ride-though, the parameters to be identified were determined by analyzing the influence of model parameters. Secondly, To enhance the algorithm’s capability to solve multidimensional optimization problems, convergence speed, and global search ability, the Differential Evolution (DE) algorithm’s search mechanism is incorporated into the Particle Swarm Optimization (PSO) algorithm, along with a non-fixed gradient inertia weight strategy for the algorithm’s inertia weight and the elite retention idea for the cross factor. Then, the objective function of the IDEPSO algorithm was built based on the concept of minimum deviation between simulation data and various groups of test data, and the significance of various working conditions was distinguished by weight division to improve the robustness of identification parameters. Finally, the identification parameters are imported into the PSASP program type 2 photovoltaic power station model, and the interval division and deviation calculation of the test data and simulation data are carried out. It is confirmed that the identification parameters meet the standards of the maximum variation permitted in GB/T 32892-2016 and are appropriate for a variety of working scenarios.

Trajectory planning of vibration suppression for hybrid structure flexible manipulator based on differential evolution particle swarm optimization algorithm

A differential evolution particle swarm optimization algorithm is proposed to address the vibration problem of a hybrid structure flexible manipulator after motion and stop. Firstly, the errors in the modeling process of the flexible manipulator were corrected, and its state variables were decomposed to establish dynamic and kinematic models. A sliding mode controller was then designed to track the trajectory. To find the optimal trajectory, a mapping function was constructed, and non-uniform interpolation points were selected. A normal distribution function was used to select discrete interpolation points, and the cubic spline interpolation was used to fit the trajectory. To prevent the particle swarm optimization algorithm from falling into local optima, the mutation and crossover operators of the differential evolution algorithm are incorporated into the algorithm, thereby expanding the search range. The effectiveness of this improved method was verified by comparing it with traditional particle swarm optimization algorithms. This study provides a new trajectory planning approach for the motion control of a hybrid structured flexible manipulator and has important practical significance.

Exploring Hydrogen Storage Capacity in Metal-Organic Frameworks: A Bayesian Optimization Approach.

Metal-organic Frameworks (MOFs) can be employed for gas storage, capture, and sensing. Finding the MOF with the best adsorption property from a big database is usual for adsorption calculations. In high-throughput computational research, the expense of computing thermodynamic quantities limits the finding of MOFs for separations and storage.In this work, we demonstrate the usefulness of Bayesian optimization (BO) for estimating the H2 uptake capability of MOFs by using an existing dataset containing 98 000 real as well as hypothetical MOFs. It is demonstrated that in order to recover the best candidate MOFs, less than 0.027% of the database needs to be screened using the BO method. This allows future adsorption experiments on a small sample of MOFs to be undertaken with minimal experimental effort by effectively screening MOF databases. In addition, the presented BO can provide comprehensible material design insights, and the framework will be transferable to the setting of other target properties. We suggest using particle swarm optimisation (PSO), a swarm intelligence technique in artificial intelligence, to estimate MOFs' H2 uptake potential to achieve results comparable to BO. In addition, we have implemented the novel evolutionary-PSO (EPSO) method for comparison research and found some extremely interesting outcomes.

Differential Evolution Particle Swarm Optimization for Phase-Sensitivity Enhancement of Surface Plasmon Resonance Gas Sensor Based on MXene and Blue Phosphorene/Transition Metal Dichalcogenide Hybrid Structure.

A differential evolution particle swarm optimization (DEPSO) is presented for the design of a high-phase-sensitivity surface plasmon resonance (SPR) gas sensor. The gas sensor is based on a bilayer metal film with a hybrid structure of blue phosphorene (BlueP)/transition metal dichalcogenides (TMDCs) and MXene. Initially, a Ag-BlueP/TMDCs-Ag-MXene heterostructure is designed, and its performance is compared with that of the conventional layer-by-layer method and particle swarm optimization (PSO). The results indicate that optimizing the thickness of the layers in the gas sensor promotes phase sensitivity. Specifically, the phase sensitivity of the DEPSO is significantly higher than that of the PSO and the conventional method, while maintaining a lower reflectivity. The maximum phase sensitivity achieved is 1.866 × 106 deg/RIU with three layers of BlueP/WS2 and a monolayer of MXene. The distribution of the electric field is also illustrated, demonstrating that the optimized configuration allows for better detection of various gases. Due to its highly sensitive characteristics, the proposed design method based on the DEPSO can be applied to SPR gas sensors for environmental monitoring.

Optimum energy management strategy with NEM-ETOU for campus buildings installed with solar PV using EPSO

In campus facilities, classrooms and labs often use a large amount of energy due to their variable consumption pattern and lack of use management. Installing solar photovoltaic (PV) rooftop in the campus building through net-energy-metering (NEM) scheme has recently increased to reduce energy usage as well as generate income from the sales of excess solar energy to the grid. Meanwhile, the Malaysian government has introduced the Enhanced Time of Use (ETOU) tariff to encourage consumers to shift their electricity usage to reduce their bills through demand response. Despite offering different rates at various times of the day, the ETOU tariff may lead to an increase in electricity expenses if consumers do not effectively shift their consumption to lower rate hours and eventually using more during peak hours. Additionally, there has been lack of quantitative analysis conducted to determine how effective the compensation scheme provided by NEM. Therefore, considering a university building that holds a commercial electricity tariff, this study aims to develop an optimum energy management strategy integrating demand response and solar energy rooftop using improved Evolutionary- Particle Swarm Optimisation (EPSO). To determine the best load shifting strategy, the model was used to evaluate two Universiti Teknologi MARA (UiTM) campuses where NEM solar PV rooftop systems were installed. The analysis involved testing various weightage factors for controlling the loads, ranging from 5% to 20% under 2 different electricity tariffs offered in Malaysia. Results show the electricity cost under EPSO provides a significant cost reduction compared to PSO for both tariffs with a lesser computational time. Furthermore, EToU tariff with a weightage factor of 20% gives a higher energy and cost saving compared to fixed tariff. The findings from this research will assist the energy users in managing the load and generation from solar, whilst supporting the policymakers in designing effective compensation schemes.

Evolutionary PSO-based emergency monitoring geospatial edge service chain in the emergency communication network

ABSTRACT Emergency communication networks play a vital role in disaster monitoring, transmission, and application during disaster emergency response (DER), however, the performance and stability of edge nodes in the emergency communication networks are often weak due to limited communication and computation resources. This weakness directly affects the quality of service (QoS) of the geospatial edge service (GES) chains involved in emergency monitoring. Existing research predominantly addresses service compositions in stable environments, neglecting the aggregation of efficient and robust GES chains in emergency communication networks. This study proposes an evolutionary particle swarm optimization (EPSO)-based emergency monitoring GES chain in an emergency communication network. It includes a GES chain model of emergency environment monitoring for tailing areas, as well as the designs of the particle chromosome encoding method, fitness evaluation model, and particle chromosome swarm update operators of the EPSO-based GES chain. Finally, the study conducts emergency environment monitoring experiments for tailing areas using the proposed method. Experiments results demonstrate that the proposed method significantly enhances the efficiency, stability, and reliability of emergency monitoring GES chains in the emergency communication network. This is crucial to providing fast and reliable services for DER during natural disasters.

Micro Evolutionary Particle Swarm Optimization (MEPSO): A new modified metaheuristic

This work introduces the algorithm Micro Evolutionary Particle Swarm Optimization (MEPSO), which is based on the Particle Swarm Optimization algorithm (PSO). MEPSO’s main idea is to replace PSO’s update of velocity with evolutionary mutations and crossovers that occur probabilistically. MEPSO uses two control hyperparameters of the evolutionary operators, α and β, to improve convergence ability. These hyperparameters allow the algorithm to adapt to different problems. MEPSO algorithm has a crucial feature: the use of micropopulations. This results in reduced computational complexity, faster convergence rate, prevention of premature convergence, and easy implementation. The proposed approach provides the basis for further study of MEPSO’s suitability for dynamic and multiobjective optimization. We evaluated MEPSO and PSO against 14 chosen benchmark functions. PSO’s results revealed that it had an average success rate of 84% and a median computation time of 49.9 s. MEPSO, on the other hand, had a success rate of 81% and a computation time of 8 s. We found MEPSO to be faster when we performed a right-tail Wilcoxon Signed-Rank statistical test against the Mean Computation Time of 30 runs for each function for each algorithm.

Optimizing TOC and IOC units of directional overcurrent relays in mutually coupled circuits using evolutionary PSO: Requirements and modeling

The efficient coordination of directional overcurrent relays is a challenging problem. Recent papers in the literature have focused on developing efficient algorithms to solve this issue. However, there is a lack of reporting practical procedures that affects the mathematical formulation to ensure accurate outputs by any optimization algorithm. In this paper, we show a comprehensive procedure that considers mutually coupled circuits via Graph Theory, the requirements of short-circuit current calculation, contingencies to be considered as well as the setting of Instantaneous Overcurrent (IOC) and Time Overcurrent (TOC) units, both for phase and ground protection. The simulation has been carried out by using a real electric network and C++ language programming. Computational simulation has shown the performance of the method, which provides high-quality solutions, with adequate coordination of the selected protective devices that increase the efficiency in setting this type of protection with safety. Evolutionary Particle Swarm Optimization (EPSO) outperformed conventional Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) as the best metaheuristic for optimally minimizing the proposed problem, achieving faster convergence times for the phase overcurrent protection scenario (16.36 s vs. 22.27 s and 17.03 s, respectively), and also for the ground overcurrent protection scenario (19.87 s vs. 20.44 s and 20.11 s, respectively).

An improved filtering algorithm for indoor localization based on DE-PSO-BPNN

Among the indoor localization algorithms, the algorithm based on traditional Back Propagation Neural Network (BPNN) has the problems of slow convergence and easy to fall into local optimum. It is difficult to apply the algorithm in noisy environments. Therefore, in this paper, we propose a novel indoor localization algorithm where the whole localization process is divided into two parts: data preprocessing and localization output. Data preprocessing means using filtering algorithm to process the Received Signal Strength Indication (RSSI) sequence. It is considered that the initial value of the received sequence has a significant impact on the performance of Kalman Filter (KF). An improved Kalman Filtering algorithm (DBSCAN-KF) is proposed based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. First, the RSSI values that are seriously disturbed by noise in the sequence are removed using the DBSCAN algorithm, and then the RSSI sequences are processed using KF so that the RSSI values can be closer to the theoretical values. The localization output part is to reduce the localization error caused by the BPNN. In this paper, the Differential Evolution (DE) algorithm and Particle Swarm Optimization (PSO) algorithm are combined, and the Differential Evolution Particle Swarm Optimization (DE-PSO) algorithm is proposed. The BPNN weights and thresholds are optimized in parallel, which improves the speed and ability of global optimization search and further avoids the shortcomings of traditional BPNNs that are prone to fall into local optimization in the training process. Experimental results show that the BPNN localization algorithm based on DBSCAN-KF improves the average localization accuracy by 0.26m compared with the BPNN localization algorithm without filtering. After filtering, the localization algorithm based on DE-PSO improved BPNN (DE-PSO-BP) improves the average localization accuracy by about 24% compared with the localization algorithm based on DE-PSO-BP. The localization algorithm based on DE-PSO-BP improves the average localization accuracy by about 61% compared with the traditional BPNN.

Prediction of asphalt pavement performance based on DEPSO-BP neural network

With the application of machine learning rapidly gaining popularity in computer science and other fields, neural network techniques have successfully simulated the performance of in-service pavements as they are efficient in predicting and solving nonlinear relationships and in dealing with uncertain large-area pavement problems. In this paper, we address the problem of the optimal timing of preventive maintenance of asphalt pavements to accurately predict the condition index (pavement condition index, PCI) of highway asphalt pavements and develop a highly accurate, long-period, multifactor prediction model with the suitability of preventive maintenance at its core. The prediction model is called differential evolution particle swarm optimization back propagation (DEPSO-BP) neural network, and the input dimension of the prediction model is determined by gray correlation analysis (GCA), and DEPSO is used to improve the search efficiency of BP neural network and the asphalt pavement usage performance with parameter continuity prediction model. Finally, the Qinglan Highway (G22) PCI of Gansu Province, China, is selected for example validation, and the prediction results are compared with those of the four models. The results show that the multifactor prediction model based on DEPSO-BP neural network has good generalization ability. This model is important for improving the economic efficiency of road maintenance, and can be used in the long-cycle process to provide model reference and scientific basis for the subsequent road maintenance budget application and decision-making scheme.

Heterogeneous differential evolution particle swarm optimization with local search

To develop a high performance and widely applicable particle swarm optimization (PSO) algorithm, a heterogeneous differential evolution particle swarm optimization (HeDE-PSO) is proposed in this study. HeDE-PSO adopts two differential evolution (DE) mutants to construct different characteristics of learning exemplars for PSO, one DE mutant is for enhancing exploration and the other is for enhance exploitation. To further improve search accuracy in the late stage of optimization, the BFGS (Broyden–Fletcher–Goldfarb–Shanno) local search is employed. To assess the performance of HeDE-PSO, it is tested on the CEC2017 test suite and the industrial refrigeration system design problem. The test results are compared with seven recent PSO algorithms, JADE (adaptive differential evolution with optional external archive) and four meta-heuristics. The comparison results show that with two DE mutants to construct learning exemplars, HeDE-PSO can balance exploration and exploitation and obtains strong adaptability on different kinds of optimization problems. On 10-dimensional functions and 30-dimensional functions, HeDE-PSO is only outperformed by the most competitive PSO algorithm on seven and six functions, respectively. HeDE-PSO obtains the best performance on sixteen 10-dimensional functions and seventeen-30 dimensional functions. Moreover, HeDE-PSO outperforms other compared PSO algorithms on the industrial refrigeration system design problem.

Stochastic Project Scheduling Optimization for Multi-stage Prefabricated Building Construction with Reliability Application

This study investigates a project scheduling problem of prefabricated building (PB) construction in an uncertain environment. Different from the traditional scheduling models in PB construction, we consider a complex multi-stage cooperation system including the production, transportation and assembly (PTA) phases. In this system, both activity durations and resource amounts are stochastic variables. By applying the reliability theory to the stochastic scheduling model innovatively, we formulate a duration reliability model to maximize the probability of non-delayed project completion, within the resource constraints. As the proposed model is a non-deterministic polynomial hard (NP-hard) problem, a hybrid meta-heuristic differential evolution particle swarm optimization (DEPSO) algorithm is developed, which is utilized the mutation factor of the differential evolution (DE) algorithm in the framework of the particle swarm optimization (PSO). Finally, a real-life example of a PB construction project is used to explore the performance of the proposed DEPSO algorithm. The result shows that the hybrid algorithm DEPSO can better find the global optimal solution in the multi-dimensional optimization problem.

Evaluating the risk of uncertainty in smart grids with electric vehicles using an evolutionary swarm-intelligent algorithm

In the last years, distributed clean energy resources such as solar irradiation, wind generation and electric vehicles as dispatchable units have been targeted as alternatives to fossil fuels in supplying the high energy demand of our society. Managing these distributed energy resources is a complex challenge, due to its uncertain behavior. In order to maintain a stable and profitable functioning of electrical systems, the development of strategies and algorithms to deal with the risk associated with this uncertainty deserves proper attention. This work addresses a risk-based energy resource management (ERM) optimization problem that deals with the day-ahead management, considering the occurrence of extreme events in a risk-averse strategy. In a risk-averse strategy, the worst case scenario cost is evaluated through the use of conditional value-at-risk (CVaR) method. To solve this ERM problem, we proposed the use of an improved version of Canonical Differential Evolutionary Particle Swarm Optimization (C-DEEPSO). This modification relies on the use of an adaptive velocity and on local search operators to improve search capability. The results indicated that, compared to two algorithms based on swarm intelligence, the proposed improved C-DEEPSO is able to provide solutions that not only reduce costs (in terms of thousands of monetary units) but also protects the aggregator against extreme scenarios.

Development of Deep Generalized Radial Basis Function Network for Short-term Load Forecasting

In this paper, an efficient method is proposed for short-term load forecasting (STLF) in power systems. The proposed method integrates Generalized Radial Basis Function Network (GRBFN) of Artificial Neural Network (ANN) with Autoencoder of pretraining in Deep Neural Network (DNN). GRBFN is an extension of Radial Basis Function Network (RBFN) in a way that the parameters of the Gaussian function are determined by the learning process. Autoencoder plays an important role to reduce the number of input variables, which means the dimensionality reduction due to the feature extraction. The hybrid model of GRBFN and Autoencoder results in DNN to improve forecasting model accuracy. Also, Evolutionary Particle Swarm Optimization (EPSO) of Evolutionary Computation is employed to optimize the parameters of GRBFN. The proposed method is successfully applied to real data of short-term load forecasting.

A mutation operator self-adaptive differential evolution particle swarm optimization algorithm for USV navigation.

To keep the global search capability and robustness for unmanned surface vessel (USV) path planning, an improved differential evolution particle swarm optimization algorithm (DePSO) is proposed in this paper. In the optimization process, approach to optimal value in particle swarm optimization algorithm (PSO) and mutation, hybridization, selection operation in differential evolution algorithm (DE) are combined, and the mutation factor is self-adjusted. First, the particle population is initialized and the optimization objective is determined, the individual and global optimal values are updated. Then differential variation is conducted to produces new variables and cross over with the current individual, the scaling factor is adjusted adaptively with the number of iterations in the mutation process, particle population is updated according to the hybridization results. Finally, the convergence of the algorithm is determined according to the decision standard. Numerical simulation results show that, compared with conventional PSO and DE, the proposed algorithm can effectively reduce the path intersection points, and thus greatly shorten the overall path length.

DETERMINATION OF THE OPTIMUM LOAD PROFILE UNDER ENHANCED OF USE TARIFF (ETOU) SCHEME USING COMBINATION OF OPTIMIZATION ALGORITHMS AND SELF ORGANIZING MAPPING

Demand side management (DSM) has been conventionally adopted in many ways to efficiently managing the appropriate electricity loads. However, with the sophisticated design of the Time of Use (TOU) tariff to reflect electricity cost reduction, implementing proper Load Management (LM) strategies is challenging. To date, consumers still struggle to define a figure for the LM percentage to be involved in the demand response program. Due to that reason, this study proposes a method to find the best load profile reflecting the new tariff offered by using a combination of optimization algorithms such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Evolutionary PSO (EPSO), and Self-Organizing Mapping (SOM). The evaluation has been made to the manufacturing operation with the existing flat tariff to be transferred to the Enhanced Time of Use (ETOU). The test results show that the ability of the proposed combination method to define the optimal outputs such as energy consumption cost, maximum demand cost, load factor index, and building electricity economic responsive index. Meanwhile, the SOM algorithm has been used to classify the enormous numbers of those simulation results produced by algorithms while defining the best LM weightage. As the test results for the case study, it was found that the practical 6% LM weightage was able to reflect the optimal required load profile shifting to be applied by manufacturing operation. Thus, by determining the optimal load profile that suits the ETOU scheme, the consumers can enjoy cost benefits while supporting the demand response program concurrently.

A Three-Phase Artificial Orcas Algorithm for Continuous and Discrete Problems

In this paper, a new swarm intelligence algorithm based on orca behaviors is proposed for problem solving. The algorithm called artificial orca algorithm (AOA) consists of simulating the orca lifestyle and in particular the social organization, the echolocation mechanism, and some hunting techniques. The originality of the proposal is that for the first time a meta-heuristic simulates simultaneously several behaviors of just one animal species. AOA was adapted to discrete problems and applied on the maze game with four level of complexity. A bunch of substantial experiments were undertaken to set the algorithm parameters for this issue. The algorithm performance was assessed by considering the success rate, the run time, and the solution path size. Finally, for comparison purposes, the authors conducted a set of experiments on state-of-the-art evolutionary algorithms, namely ACO, BA, BSO, EHO, PSO, and WOA. The overall obtained results clearly show the superiority of AOA over the other tested algorithms.