Particle Swarm Optimization Framework Research Articles

Trust region framework-based design of sub-6 GHz m-MIMO antenna and evaluation of SAR

Purpose Currently, massive multiple-input multiple-output (m-MIMO) antennas are typically designed using complex trial-and-error methods. The purpose of this study is to determine an effective optimization method to achieve more efficient antenna design processes. Design/methodology/approach This paper presents the design stages of a m-MIMO antenna array compatible with 5G smartphones operating in long term evolution (LTE) bands 42, 43 and 46, based on a specific algorithm. Each antenna element in the designed 10-port m-MIMO antenna array is intended to perfectly cover the three specified LTE bands. The optimization methods used for this purpose include the Nelder–Mead simplex algorithm, covariance matrix adaptation evolution strategy, particle swarm optimization and trust region framework (TRF). Findings Among the primary optimization algorithms, the TRF algorithm met the defined objectives most effectively. The achieved antenna efficiency values exceeded 60.81% in the low band and 68.39% in the high band, along with perfect coverage of the desired bands, demonstrating the success of the design with the TRF algorithm. In addition, the potential electromagnetic field exposure caused by the designed m-MIMO antenna array is elaborated upon in detail using computational human models through specific absorption rate analysis. Originality/value The comparison of four different algorithms (two local and two global) for use in the design of a 10-element m-MIMO antenna array with a complex structural configuration and the success of the design implemented with the selected algorithm distinguish this study from others.

A modified shuffled frog leaping algorithm with inertia weight

The shuffled frog leaping algorithm (SFLA) is a promising metaheuristic bionics algorithm, which has been designed by the shuffled complex evolution and the particle swarm optimization (PSO) framework. However, it is easily trapped into local optimum and has the low optimization accuracy when it is used to optimize complex engineering problems. To overcome the shortcomings, a novel modified shuffled frog leaping algorithm (MSFLA) with inertia weight is proposed in this paper. To extend the scope of the direction and length of the updated worst frog (vector) of the original SFLA, the inertia weight α was introduced and its meaning and range of the new parameters are fully explained. Then the convergence of the MSFLA is deeply analyzed and proved theoretically by a new dynamic equation formed by Z-transform. Finally, we have compared the solution of the 7 benchmark functions with the original SFLA, other improved SFLAs, genetic algorithm, PSO, artificial bee colony algorithm, and the grasshopper optimization algorithm with invasive weed optimization. The testing results showed that the modified algorithms can effectively improve the solution accuracy and convergence property, and exhibited an excellent ability of global optimization in high-dimensional space and complex function problems.

A Particle Swarm and Smell Agent-Based Hybrid Algorithm for Enhanced Optimization

The particle swarm optimization (PSO) algorithm is widely used for optimization purposes across various domains, such as in precision agriculture, vehicular ad hoc networks, path planning, and for the assessment of mathematical test functions towards benchmarking different optimization algorithms. However, because of the inherent limitations in the velocity update mechanism of the algorithm, PSO often converges to suboptimal solutions. Thus, this paper aims to enhance the convergence rate and accuracy of the PSO algorithm by introducing a modified variant, which is based on a hybrid of the PSO and the smell agent optimization (SAO), termed the PSO-SAO algorithm. Our specific objective involves the incorporation of the trailing mode of the SAO algorithm into the PSO framework, with the goal of effectively regulating the velocity updates of the original PSO, thus improving its overall performance. By using the trailing mode, agents are continuously introduced to track molecules with higher concentrations, thus guiding the PSO’s particles towards optimal fitness locations. We evaluated the performance of the PSO-SAO, PSO, and SAO algorithms using a set of 37 benchmark functions categorized into unimodal and non-separable (UN), multimodal and non-separable (MS), and unimodal and separable (US) classes. The PSO-SAO achieved better convergence towards global solutions, performing better than the original PSO in 76% of the assessed functions. Specifically, it achieved a faster convergence rate and achieved a maximum fitness value of −2.02180678324 when tested on the Adjiman test function at a hopping frequency of 9. Consequently, these results underscore the potential of PSO-SAO for solving engineering problems effectively, such as in vehicle routing, network design, and energy system optimization. These findings serve as an initial stride towards the formulation of a robust hyperparameter tuning strategy applicable to supervised machine learning and deep learning models, particularly in the domains of natural language processing and path-loss modeling.

A Novel Swarm Exploring Varying Parameter Recurrent Neural Network for Solving Non-Convex Nonlinear Programming.

Aiming at solving non-convex nonlinear programming efficiently and accurately, a swarm exploring varying parameter recurrent neural network (SE-VPRNN) method is proposed in this article. First, the local optimal solutions are searched accurately by the proposed varying parameter recurrent neural network. After each network converges to the local optimal solutions, information is exchanged through a particle swarm optimization (PSO) framework to update the velocities and positions. The neural network searches for the local optimal solutions again from the updated position until all the neural networks are searched to the same local optimal solution. For improving the global searching ability, wavelet mutation is applied to increase the diversity of particles. Computer simulations show that the proposed method can solve the non-convex nonlinear programming effectively. Compared with three existing algorithms, the proposed method has advantages in accuracy and convergence time.

A collective neurodynamic penalty approach to nonconvex distributed constrained optimization

A nonconvex distributed optimization problem involving nonconvex objective functions and inequality constraints within an undirected multi-agent network is considered. Each agent communicates with its neighbors while only obtaining its individual local information (i.e. its constraint and objective function information). To overcome the challenge caused by the nonconvexity of the objective function, a collective neurodynamic penalty approach in the framework of particle swarm optimization is proposed. The state solution convergence of every neurodynamic penalty approach is directed towards the critical point ensemble of the nonconvex distributed optimization problem. Furthermore, employing their individual neurodynamic models, each neural network conducts accurate local searches within constraints. Through the utilization of both locally best-known solution information and globally best-known solution information, along with the incremental enhancement of solution quality through iterations, the globally optimal solution for a nonconvex distributed optimization problem can be found. Simulations and an application are presented to demonstrate the effectiveness and feasibility.

Synergistic Optimization of Unit Commitment Using PSO and Random Search

Optimizing the order of thermal units for power generation plays a pivotal role in meeting load demand while minimizing fuel consumption. This paper introduces an enhanced hybrid method designed to schedule generating units with the simultaneous objectives of cost and emission reduction, which often pose a trade-off challenge. The hybrid approach integrates the parametric adaptation of particle swarm optimization (PSO) with the randomness of a random search algorithm. The introduction of intermediate variables enhances the performance of particles in the PSO framework, contributing to more effective optimization. To update the individual population's locations within the particle swarm optimization process, randomness is judiciously introduced using a random search method. To assess the potential of the proposed method, it is applied to the IEEE-39 bus system and a four-unit thermal system. The results obtained through the proposed approach are compared with those achieved by existing methods, demonstrating its effectiveness in achieving optimal solutions for the unit commitment problem.

Data Driven Discovery of MOFs for Hydrogen Gas Adsorption.

Hydrogen gas (H2) is a clean and renewable energy source, but the lack of efficient and cost-effective storage materials is a challenge to its widespread use. Metal-organic frameworks (MOFs), a class of porous materials, have been extensively studied for H2 storage due to their tunable structural and chemical features. However, the large design space offered by MOFs makes it challenging to select or design appropriate MOFs with a high H2 storage capacity. To overcome these challenges, we present a data-driven computational approach that systematically designs new functionalized MOFs for H2 storage. In particular, we showcase the framework of a hybrid particle swarm optimization integrated genetic algorithm, grand canonical Monte Carlo (GCMC) simulations, and our in-house MOF structure generation code to design new MOFs with excellent H2 uptake. This automated, data driven framework adds appropriate functional groups to IRMOF-10 to improve its H2 adsorption capacity. A detailed analysis of the top selected MOFs, their adsorption isotherms, and MOF design rules to enhance H2 adsorption are presented. We found a functionalized IRMOF-10 with an enhanced H2 adsorption increased by ∼6 times compared to that of pure IRMOF-10 at 1 bar and 77 K. Furthermore, this study also utilizes machine learning and deep learning techniques to analyze a large data set of MOF structures and properties, in order to identify the key factors that influence hydrogen adsorption. The proof-of-concept that uses a machine learning/deep learning approach to predict hydrogen adsorption based on the identified structural and chemical properties of the MOF is demonstrated.

The use of water allocation efficiency in optimizing crop production in drylands using a fuzzy trading model

Abstract Considering the importance of agriculture in food security in arid and semiarid areas, it is absolutely necessary to apply the planning model based on different objectives. This study has been conducted to simulate the trading and non-trading policies in the Particle Swarm Optimization framework for estimating the optimal economic values of agricultural water in Jilin province, northeastern China. Fuzzy solutions have been also calculated by definition of the feasible domain for water availability and permits. A generalized extreme value function was incorporated into the problem to predict the rainfall amounts with a 25-year return period. The results showed that determining the agricultural pattern using a trading policy with the aim of benefiting from the water will reduce the water demand in the study area. The trading policy can lead to a more efficient allocation of released water and reduce water scarcity, but it can eliminate some plants from the cropping pattern by reducing the water level. As a result, trading policy can be effective in the short term, especially in drought conditions due to insufficient water supply.

Dimension selection for EEG classification in the SPD Riemannian space based on PSO

Several classification methods in the task of electroencephalogram (EEG) classification represent input features as symmetric positive definite (SPD) matrices. By translating the data into a Riemannian space, the classification methods have obtained superior performance; however, the high dimensionality of the data typically results in low computational efficiency and degraded classification performance. Therefore, this paper proposes a novel dimension selection method named dimension selection for SPD matrices on Riemannian manifold (DSSR). DSSR can eliminate redundant dimensions for SPD matrices in the Riemannian space based on the framework of particle swarm optimization (PSO). First, we treat one column of the SPD matrix and its corresponding row as a group, where upon the PSO is used to find optimal groups. Next, we use Riemannian classifiers to evaluate the goodness of the selected groups. Finally, the dimension selected SPD matrices are classified by the generalized learning Riemannian space quantization (GLRSQ). Experimental results on two motor imagery datasets show the superior performance of the proposed methods over EEG classification. Neuroscience experiments prove that the optimal dimensions selected by our proposed method are consistent with the findings of brain science research.

Integrated Planning of Feeder Route Selection, Schedule Design, and Fleet Allocation with Multimodal Transport Path Selection Considered

In the shipping network optimization, the feeder liner companies not only need to decrease the operation cost by comprehensively optimizing the route, schedule, and fleet but also try to increase the operation income by attracting more shippers, with multimodal transport-path selection considered. Therefore, this paper proposes the integrated planning of feeder route selection, schedule design, and fleet allocation with shippers’ transport-path selection considered. We utilize the nested Logit model to analyze the shipper selection behavior for the multimodal transport path and then formulate a mixed-integer nonlinear programming model of integrated optimization for route, schedule, and fleet. To solve our nonlinear model, we propose a particle swarm optimization (PSO) framework embedded with CPLEX solver by combining the constraint relaxations and the linearization techniques with the heuristic rules. Based on the multimodal transport system in Northern China, computational experiments were conducted to verify the effectiveness of our model and algorithm. The sensitivity analyses of model parameters show that with the increase in ship rent, feeder liner companies should reduce the ship capacity or try to increase the number of port call. With the increase in fuel price, feeder liner companies should reduce the ship speed under the constraints of the arrival-time window and schedule interval. If shippers’ time utility coefficient is high, feeder liner companies should shorten the sailing time of ships by decreasing the number of port calls or shorten the at-port time of ships by increasing the service frequency. If the multimodal transport paths are cost-advantaged or if the waterway transport time has a small impact on multimodal transport time, feeder liner companies should weigh the increase in income and cost when using the measures of time saving. These management insights provide decision support for the operation practice of liner shipping network optimization.

A parallel particle swarm optimization framework based on a fork-join thread pool using a work-stealing mechanism

Particle Swarm Optimization (PSO) is one of the most popular optimization algorithms that has been adopted in various fields, including design, scheduling, and biochemistry. However, the algorithm is time-consuming when facing high-dimensional or multi-objective optimizing problems. Parallel PSO is thus proposed to improve its computing efficiency, and many studies have been conducted. However, the low-level system design is seldom considered in parallel programming, which may have a nonnegligible impact on computing efficiency, creating a gap between low-level optimization and high-level algorithm design. Therefore, this paper proposes a Parallel Asynchronous PSO (PAPSO) framework based on thread pools utilizing multicore processors and adopts a cross-level approach to bridge the gap. A series of experiments are designed and conducted to examine how the aforementioned method can improve the parallel execution efficiency of PSO compared with the OpenMP framework and nonparallel PSO. Results indicate that PAPSO can significantly improve PSO computing efficiency by reducing the elapsed time, approaching approximately 20% optimization compared with OpenMP. Additionally, it achieves an approximately linear speedup of up to 4.5 threads. In addition, the particle communication experiment shows that the nonblocking communication protocol is effective for maintaining the computing elapsed time in the same level facing different neighborhood sizes. Finally, the work-stealing mechanism achieves an average of 16% improvement for general computing scenarios and maintain up to 16% improvement for imbalanced workload scenarios. Generally, the major contribution of this paper is that we innovate the thread pooling management concept with the fork-join model in parallelizing PSO to make sufficient use of multiple core CPUs for parallel computing through multithread programming.

An efficient particle swarm optimization with evolutionary multitasking for stochastic area coverage of heterogeneous sensors

This paper investigates the stochastic area coverage problem of sensors with uncertain detection probability. The risk associated with uncertain parameters is managed using the conditional value-at-risk (CVaR) risk measure. The loss function is represented by the uncovered area coverage rate. We then formulate the minimum CVaR-based uncovered area coverage (CVaR-UAC) problem and provide some theoretical guarantees for the problem. Unlike previous research that treats area coverage as a single problem, we propose an efficient particle swarm optimization (PSO) with evolutionary multitasking to solve the stochastic area coverage problem along with multiple simplified problem forms. These simplified problems act as the auxiliary tasks for the original CVaR-UAC to enhance the evolutionary search. We have improved the proposed PSO algorithm from the framework of disturbance PSO and virtual force directed co-evolutionary particle swarm optimization, using a hybrid method in population initialization and an adaptive perturbation in individual updating. As a result, the exploration ability of the algorithm is significantly enhanced. The experiment results have demonstrated the effectiveness of the proposed algorithm compared with state-of-the-art algorithms in terms of solution quality.

An adaptive dynamic multi-swarm particle swarm optimization with stagnation detection and spatial exclusion for solving continuous optimization problems

Particle swarm optimization (PSO) is a simple and efficient optimization method that has been used in many fields. However, PSO has some flaws including premature convergence and poor population diversity. To solve these problems of PSO, an adaptive dynamic multi-swarm particle swarm optimization with stagnation detection and spatial exclusion (ADPSO) is proposed. The newly proposed ADPSO is based on a dynamic multi-swarm PSO framework that cooperates with stagnation detection mechanism (SDM) and spatial exclusion strategy (SES). Firstly, the whole population is divided into multiple equal sub-swarms, which can be regrouped during evolution. The best particle of each sub-swarm, lbest, is used to evaluate the evolutionary state of each sub-swarm. If the lbest cannot improve its solution continuously without reaching the regroup period, the SDM will be triggered. In this case, a vitality particle is generated to help the stagnant sub-swarm to search for promising areas again. To keep the population diversity, the vitality particle is constructed according to the excellent historical information of all particles in the whole population. Secondly, to enhance the diversity of the population, the SES is proposed to avoid premature adsorption of all sub-swarms. Finally, the effect of ADPSO is evaluated using CEC2013, CEC2017 and four engineering optimization problems. The results shows that the proposed ADPSO is suitable for solving most optimization problems, and ADPSO has better results compared to the state-of-the-art PSO variants (i.e., MSCPSO, HCDMPS) and other popular evolutionary algorithms.

Enhancing OLSR protocol in VANETs with multi-objective particle swarm optimization

The Optimized Link State Routing Protocol (OLSR) is envisioned as one of the key components to the operation of future vehicular ad hoc networks (VANETs). Its performance is however fairly constrained in the vehicular communication environment due to the limited coverage of wireless transmission and high-speed movement of nodes. In this brief, we propose a multi-objective particle swarm optimization (MOPSO) framework to enhance the performance of OLSR in VANETs. Specifically, we formulate a multi-objective optimization problem (MOP) considering not only the quality of service (QoS), including throughput, delay and packet loss rate, but also the cost of service, i.e., routing load. We solve this MOP with MOPSO and obtain the Pareto front corresponding to the optimal equilibrium of performance and cost. In the experiment, our optimization framework is used in a general setting of VANETs to obtain the optimal parameters of OLSR, i.e., Hello and TC intervals, and these parameters are further validated in realistic VANET scenarios. Our optimization framework can be naturally extend to other dynamic routing protocols in networks.

Prediction of hydrogen yield from supercritical gasification process of sewage sludge using machine learning and particle swarm hybrid strategy

This study presents an integrated framework of machine learning models (Artificial Neural Network, Ensembled Learning Tree, Support Vector Machine, and Gaussian Process Regression) and particle swarm optimization (PSO) to predict and optimize hydrogen production from SCWG using sewage sludge characteristics and process parameters. According to the results, ELT-PSO is preferred for forecasting hydrogen yield (Coefficient of determination (R2) = 0.997, Root Mean Square Error (RMSE) = 0.093), and it is highly suggested for handling complex variable-target correlation. However, Support Vector Machine (SVM) performed poorly, with R2 = 0.83 and RMSE = 2.28. According to SHAP feature importance Temperature, Carbon, Hydrogen, and Pressure are among the parameters that have strong impact. In addition, by adjusting the ML hyperparameters, optimization method (PSO) was used to maximize H2 yield. The optimized ELT-PSO model was utilized to establish a Graphical User Interface that made it simple to calculate H2 yield.

Efficient Data Dissemination Strategy for UAV in UAV-Assisted VANETs

With development of emerging intelligent transportation systems (ITSs), unmanned aerial vehicles (UVAs) are gaining growing attention to achieve UAV-assisted VANETs for improving performance of data dissemination. However, in UAV-assisted VANETs, the performance of data dissemination can be significantly compromised by the limited bandwidth resource of UAVs. In this paper, we propose a scheduling strategy to provide efficient data dissemination. In particular, first, we take into account a scenario with either unavailable or overloaded infrastructure, where a UAV is employed to provide data dissemination. Furthermore, considering the limited bandwidth resource of the UAV, we formulate a coding-based cooperative caching data dissemination (C3D2) problem which makes use of the synergy between network coding and vehicular caching with the objective of maximizing the bandwidth efficiency of UAVs. Meanwhile, the NP-hardness of the C3D2 problem is proved by constructing a polynomial time reduction from the simultaneous matrix completion problem. Based on the effectiveness of the binary particle swarm optimization (BPSO) framework in solving the complex problems, we propose a BPSO-based cooperative caching scheduling (BPSO_C2S) algorithm consisting of a series of strategies, such as particle representation, particle evaluation, particle update and infeasible solution amendment. In addition, we analyze the computational complexity of the BPSO_C2S. Finally, we build the simulation model and give a comprehensive performance evaluation through the real-world map and taxi trajectories. Simulation results demonstrate the effectiveness and superiority of the proposed solution.

Container liner shipping schedule optimization with shipper selection behavior considered

ABSTRACT The liner shipping schedules determine the container transportation time and the arrival time of ships, which has a significant influence on the shipper selection behavior and the transportation demand. This paper addresses the container liner shipping schedule optimization with shipper selection behavior considered. Our problem is formulated as a mixed-integer nonlinear programming model, where the shipper selection behavior is evaluated by a nested logit model. A particle swarm optimization (PSO) framework embedded with CPLEX solver is designed, by combining the constraint relaxations and the linearization techniques with the heuristic rules. The numerical experiments are conducted based on the Persian Gulf route of COSCO SHIPPING LINES. The results show that: the total freight demand is increased by 23% and the weekly operation revenue is increased by 31% after considering shipper selection. Besides, we find that the planned ship speed should be increased for time-preference shippers with electronic or refrigerated products, while it should be decreased for price-preference shippers with general or bulk cargoes. These conclusions can provide decision support for the operation practice of liner shipping schedule design.

Multi-swarm particle swarm optimization based on CUDA for sparse reconstruction

Particle swarm optimization (PSO) has been successfully applied to the sparse reconstruction problem and achieved good results. With the dimension of the problem increases, parallelizing PSO is an effective method to reduce its running time. This paper proposes a parallel PSO framework to solve the sparse reconstruction problem based on Compute Unified Device Architecture (CUDA) platform on Graphics Processing Unit (GPU). In order to further utilize potential computing resources in the GPU and improve the performance of the algorithm, each particle is launched by CUDA threads and the swarm is divided into multiple sub-swarms in CUDA streams. A local search strategy based on gradient and a particle coding strategy is combined into PSO for the purposes of achieving better reconstruction accuracy and accelerating convergence. In addition, in order to further optimize the parallel execution process of CUDA, the reduction algorithm and dynamic parallelism are incorporated into the proposed method. In the performance experiments, the proposed algorithm achieves a maximum speedup ratio of 25 times compared to the serial version in the signal reconstruction tasks.

Application of Neural Network–Based Modeling for Leak Localization in Water Mains

Water distribution network (WDN) leakage is estimated to account for more than 20% of potable water in the United States. Because existing leakage detection practices are mainly laborious or require excessive budget allocation, novel model-based techniques have become prevalent by employing smart meters. Specifically, incorporating machine learning and metaheuristic optimization methods into such model-based approaches to predict water pipeline leakage is one of the state-of-the-art approaches. This paper presents a predictive leakage detection approach by leveraging advanced metering infrastructure (AMI) and cyber-monitoring data. Primarily, the synchronous collection of consumption and monitoring data (i.e., flow and pressure) constitutes the proposed predictive tool in a combinative framework of genetic algorithms (GA) and particle swarm optimization (PSO). The time-consuming EPANET 2.2 simulation toolkit in MATLAB is circumvented by a set of trained artificial neural networks (ANNs). Sensitivity analyses are conducted to investigate the sensitivity of the proposed leakage prediction approach to multiple parameters. Ultimately, the number and location of the flow and pressure monitoring stations are optimized using maximum sensor availability thresholds. Mean absolute percentage error (MAPE) is employed to measure the model’s accuracy. The proposed predictive tool offers insightful horizons on how considerable background leakage can be detected and prioritized without using labor-intensive, costly methods.

Resource-controlled stochastic customer order scheduling via particle swarm optimization with bound information

PurposeCycle time reduction is important for order fulling process but often subject to resource constraints. This study considers an unrelated parallel machine environment where orders with random demands arrive dynamically. Processing speeds are controlled by resource allocation and subject to diminishing marginal returns. The objective is to minimize long-run expected order cycle time via order schedule and resource allocation decisions.Design/methodology/approachA stochastic optimization algorithm named CAP is proposed based on particle swarm optimization framework. It takes advantage of derived bound information to improve local search efficiency. Parameter impacts including demand variance, product type number, machine speed and resource coefficient are also analyzed through theoretic studies. The algorithm is evaluated and benchmarked with four well-known algorithms via extensive numerical experiments.FindingsFirst, cycle time can be significantly improved when demand randomness is reduced via better forecasting. Second, achieving processing balance should be of top priority when considering resource allocation. Third, given marginal returns on resource consumption, it is advisable to allocate more resources to resource-sensitive machines.Originality/valueA novel PSO-based optimization algorithm is proposed to jointly optimize order schedule and resource allocation decisions in a dynamic environment with random demands and stochastic arrivals. A general quadratic resource consumption function is adopted to better capture diminishing marginal returns.