Multi-swarm Particle Swarm Optimization Research Articles

Prediction of GPS Satellite Clock Offset Based on an Improved Particle Swarm Algorithm Optimized BP Neural Network

Satellite clock offset is an important factor affecting the accuracy of real-time precise point positioning (RT-PPP). Due to missing real-time service (RTS) products provided by the International GNSS Service (IGS) or network faults, users may not obtain effective real-time corrections, resulting in the unavailability of RT-PPP. Considering this issue, an improved back propagation (BP) neural network optimized by heterogeneous comprehensive learning and dynamic multi-swarm particle swarm optimizer (HPSO-BP) is proposed for clock offset prediction. The new model uses the particle swarm optimizer to optimize the initial parameters of the BP neural network, which can avoid the instability and over-fitting problems of the traditional BP neural network. IGS RTS product data is selected for the experimental analysis; the results demonstrate that the average prediction precision of the HPSO-BP model for 20-min and 60-min is better than 0.15 ns, improving by approximately 85% compared to traditional models including the linear polynomial (LP) model, the quadratic polynomial (QP) model, the gray system model (GM (1,1)), and the ARMA time series model. It indicates that the HPSO-BP model has reasonable practicability and stability in the short-term satellite clock offset prediction, and its prediction performance is superior to traditional models. Therefore, in practical applications, the clock offset products predicted by the HPSO-BP model can meet the centimeter-level positioning accuracy requirements of RT-PPP.

Fatigue properties and S-N curve estimating of 316L stainless steel prepared by SLM

This paper investigates the fatigue properties of 316L stainless steel manufactured by selective laser melting technology (SLM) under hot-isostatic-pressing (HIP) heat-treatment. The fatigue tests of 316L specimens were carried out under pulse tensile cyclic loading to obtain the fatigue performance. To avoid falling into local optima and improve the convergence speed, the dynamic multiswarm particle swarm optimizer (DMS-PSO) algorithm was first introduced for parameter optimization of the three-parameter Weibull distribution model, and then the S-N curves based on the model were obtained. The S-N curves were highly consistent with the metallographic and fractographic phenomena. Besides, both the S-N curves and fractographic analysis show that the resistance of high-stress fatigue fracture is significantly improved by using HIP heat-treatment.

Co-Evolutionary Multi-Swarm PSO Based Optimal Placement of Miscellaneous DGs in a Real Electricity Grids Regarding Uncertainties

Distributed generators (DGs) facilitate minimizing a monetary objective for controlling overload or low-voltage obstacles. In conjunction with controlling such complications, a DG unit can be allocated for maximum reliability or efficiency. This study presents a new method based on a new index for locating and sizing DGs in electricity distribution systems. Stable node voltages which are known as power stability index (PSI) are considered in developing the index. An analytical method is applied in visualizing the effect of DG on losses, voltage profile, and voltage stability of the system. In this study, a new approach using co-evolutionary multi-swarm particle swarm optimization (CMPSO) algorithm is purposed for locating DGs in radial electrical distribution systems considering the uncertainty of solar power as well as load and wind power. In this paper, the optimal locations and sizes of DG units are calculated by considering the active power loss, reliability index, and PSI as objective functions. The presented algorithm is tested on 33-bus and 274-bus real distribution networks. The results of the simulation show the effectiveness of the proposed method.

Enhanced multi-swarm cooperative particle swarm optimizer

• An enhanced multi-swarm cooperative particle swarm optimizer is proposed. • Sub-swarms can adaptively adjust the cooperation according to different situation. • A delayed-activation strategy is designed to detect and activate stagnant sub-swarm. • A repulsive mechanism prevents the population from gathering together prematurely. • Exhibiting superior performance in solution accuracy and convergence speed. In this paper, a novel multi-swarm particle swarm optimizer driven by delayed-activation (DA) strategy and repulsive mechanism, named as enhanced multi-swarm cooperative particle swarm optimizer (EMCPSO) is proposed. EMCPSO is designed to make use of the advantage of multi-swarm technique and overcome the problem of premature convergence of original PSO. In this algorithm, the whole population is partitioned into four identical sub-swarms. The best particle of each sub-swarm, sbest , is used to estimate the evolutionary state of the group. If the sbest can continuously improve its solution's quality, that sub-swarm evolves independently without communicating with other counterparts. Otherwise, based on a non-ascending sequence, a delayed-activation (DA) strategy will be triggered. With information sharing among multi-swarm, activating exemplar is constructed to promote the stagnant sub-swarm to search for better solutions again. On the other hand, a repulsive mechanism is introduced to prevent the whole population from gathering together prematurely. In this way, more potential regions of the search space can be explored by EMCPSO. The experiment results on CEC 2017 problem set demonstrate the superior performance of the proposed EMCPSO in terms of solution accuracy and convergence speed.

Training a Feedforward Neural Network Using Hybrid Gravitational Search Algorithm with Dynamic Multiswarm Particle Swarm Optimization.

One of the most well-known methods for solving real-world and complex optimization problems is the gravitational search algorithm (GSA). The gravitational search technique suffers from a sluggish convergence rate and weak local search capabilities while solving complicated optimization problems. A unique hybrid population-based strategy is designed to tackle the problem by combining dynamic multiswarm particle swarm optimization with gravitational search algorithm (GSADMSPSO). In this manuscript, GSADMSPSO is used as novel training techniques for Feedforward Neural Networks (FNNs) in order to test the algorithm's efficiency in decreasing the issues of local minima trapping and existing evolutionary learning methods' poor convergence rate. A novel method GSADMSPSO distributes the primary population of masses into smaller subswarms, according to the proposed algorithm, and also stabilizes them by offering a new neighborhood plan. At this time, each agent (particle) increases its position and velocity by using the suggested algorithm's global search capability. The fundamental concept is to combine GSA's ability with DMSPSO's to improve the performance of a given algorithm's exploration and exploitation. The suggested algorithm's performance on a range of well-known benchmark test functions, GSA, and its variations is compared. The results of the experiments suggest that the proposed method outperforms the other variants in terms of convergence speed and avoiding local minima; FNNs are being trained.

Multi-Swarm PSO Algorithm for Static Workflow Scheduling in Cloud-Fog Environments

Scientific workflow scheduling is a well-known problem that involves the allocation of workflow tasks to particular computational resources. The generation of optimal solutions to reduce run-time, cost, and energy consumption, as well as ensuring proper load balancing, remains a major challenge. Therefore, this work presents a Multi-Swarm based Particle Swarm Optimization (MS-PSO) algorithm to improve the scheduling of scientific workflows in cloud-fog environments. In particular, MS-PSO seeks to address the canonical PSO’s problem of premature convergence, which leads it to suboptimal solutions. In MS-PSO, particles are divided into several swarms, with each swarm having its own cognitive and social learning coefficients. This work also develops a weighted sum objective function for the workflow scheduling problem, based on four objectives: makespan, cost, energy and load balancing for cloud and fog tiers. The FogWorkflowSim Toolkit is used in the evaluation process, with the objectives serving performance metrics. The MS-PSO approach is compared with the canonical PSO, Genetic Algorithm (GA), Differential Evolution (DE) and GA-PSO. The following scientific workflows are used in the simulations: Montage, Cybershake, Epigenomics, LIGO and SIPHT. MS-PSO outperforms the canonical PSO on all scientific workflows and under all performance metrics. It competes fairly well against the other approaches and it is more stable and reliable. It only ranks second to PSO, in terms of execution time. In future, multiple species, incorporating population update mechanisms from several algorithmic frameworks (MS-PSO, DE, GA), will be used for scientific workflow scheduling. Hybdridization of the realized algorithm with dynamic approaches will also be investigated.

Fitness peak clustering based dynamic multi-swarm particle swarm optimization with enhanced learning strategy

Particle Swarm Optimization (PSO) is a well-known swarm intelligence algorithm and its performance primarily depends on the tradeoff between exploration and exploitation. In order to well balance the exploration and exploitation, this paper presents a fitness peak clustering based dynamic multi-swarm Particle Swarm Optimization (FPCMSPSO) with enhanced learning strategy. In the presented FPCMSPSO, first, FPC-based partitioning method is utilized to divide the initialized population into several sub-swarms so as to avoid crossover evolution caused by random partitioning. These sub-swarms evolve independently based on comprehensive learning strategy and along with further evolution they would merge into a global swarm according to their own stagnancy information. Second, an enhanced learning strategy is exploited to some particles, and their velocities are updated based on learning exemplars alternately generated by comprehensive learning or dimensional learning strategies according to their stagnancy information. Extensive experimental results demonstrate that the solution accuracy, convergence speed and stability of FPCMSPSO are remarkably improved due to the usage of above strategies. The comparative results of FPCMSPSO with other existing PSO variants on various optimization problems show that FPCMSPSO statistically outperforms other PSO variants with significant difference.

An Expanded Heterogeneous Particle Swarm Optimization Based on Adaptive Inertia Weight

In this study, a modified version of multiswarm particle swarm optimization algorithm (MsPSO) is proposed. However, the classical MsPSO algorithm causes premature stagnation due to the limitation of particle diversity; as a result, it is simple to slip into a local optimum. To overcome the above feebleness, this work presents a heterogeneous multiswarm PSO algorithm based on adaptive inertia weight strategies called (A-MsPSO). The MsPSO’s main advantages are that it is simple to use and that there are few settings to alter. In the MsPSO method, the inertia weight is a key parameter affecting considerably convergence, exploration, and exploitation. In this manuscript, an adaptive inertia weight is adopted to ameliorate the global search ability of the classical MsPSO algorithm. Its performance is based on exploration, which is defined as an algorithm’s capacity to search through a variety of search spaces. It also aids in determining the best ideal capability for searching a small region and determining the candidate answer. If a swarm discovers a global best location during iterations, the inertia weight is increased, and exploration in that direction is enhanced. The standard tests and indications provided in the specialized literature are used to show the efficiency of the proposed algorithm. Furthermore, findings of comparisons between A-MsPSO and six other common PSO algorithms show that our proposal has a highly promising performance for handling various types of optimization problems, leading to both greater solution accuracy and more efficient solution times.

A modified multi swarm particle swarm optimization algorithm using an adaptive factor selection strategy

In the present study, we suggest a modified version of heterogeneous multi-swarm particle swarm optimization (MSPSO) algorithm, that allows the amelioration of its performance by introducing an adaptive inertia weight approach. In order to bring about a balance between the exploration and exploitation characteristics of MSPSO allowing to promote information exchange amongst the subswarms. However, the classical MSPSO algorithm search behavior has not always been optimal in finding the optimal solution to certain problems, which results in falling into local optimum leading to premature convergence. The most advantages of the MSPSO there are easy to implement and there are few parameters to adjust. The inertia weight (w) is one of the most Particle Swarm Optimization’s (PSO) parameters. Controlling this parameter could facilitate the convergence and prevent an explosion of the swarm. To overcome the above limitations, this paper proposes a heterogeneous multi swarm PSO algorithm based on PSO number selection approach centred on the idea of particle swarm referred to as Multi-Swarm Particle Swarm Optimization algorithm with Factor selection strategy (FMSPSO). In the various process implementations of the particle swarm search, different parameter selection strategies are adopted to ameliorate the global search ability. The proposed FMSPSO is able to improve the population’s diversity and better explore the entire feature space. The statistical test and indicators that are reported in the specialized literature demonstrate that the suggested approach is superior in terms of efficiency to nine other popular PSO algorithms in solving the optimization problem of complex problems. The approach suggests that FMSPSO reaches a very promising performance for solving different types of optimization problems, leading eventually to higher solution accuracy.

Auto-sharing parameters for transfer learning based on multi-objective optimization

Transfer learning methods exploit similarities between different datasets to improve the performance of the target task by transferring knowledge from source tasks to the target task. “What to transfer” is a main research issue in transfer learning. The existing transfer learning method generally needs to acquire the shared parameters by integrating human knowledge. However, in many real applications, an understanding of which parameters can be shared is unknown beforehand. Transfer learning model is essentially a special multi-objective optimization problem. Consequently, this paper proposes a novel auto-sharing parameter technique for transfer learning based on multi-objective optimization and solves the optimization problem by using a multi-swarm particle swarm optimizer. Each task objective is simultaneously optimized by a sub-swarm. The current best particle from the sub-swarm of the target task is used to guide the search of particles of the source tasks and vice versa. The target task and source task are jointly solved by sharing the information of the best particle, which works as an inductive bias. Experiments are carried out to evaluate the proposed algorithm on several synthetic data sets and two real-world data sets of a school data set and a landmine data set, which show that the proposed algorithm is effective.

A Multi-Swarm Structure for Particle Swarm Optimization: Solving the Welded Beam Design Problem

Several studies exist in the literature that utilized metaheuristics and nature-inspired algorithms to solve engineering problems. Particle Swarm Optimization (PSO) is a well-known nature-inspired algorithm that has been used for different optimization problems due to its simplicity and ability to find near-optimal solutions. However, PSO suffers from a problem of balancing between the global search and local search abilities when applied to engineering problems. Recently, a new variant of PSO based on a novel multi-swarm architecture called Multi-swarm Particle Swarm Optimization (MPSO) was proposed. The proposed MPSO was evaluated on solving normal and large-scale problems. This study evaluated the possibility of using MPSO to simulate Welded Beam Design (WDB) problem which is a mechanical engineering problem. Several simulations were performed using the proposed approach and from the results, MPSO model was observed to simulate WBD problem with better optimal solution compared to the standalone PSO. The outcome of this study further confirmed the robustness of the MPSO over the other known metaheuristics. Generally, MPSO achieved an excellent optimization performance with a fast convergence learning process.

Employing reinforcement learning to enhance particle swarm optimization methods

Particle swarm optimization (PSO) is a well-known optimization algorithm that shows good performance in solving different optimization problems. However, PSO usually suffers from slow convergence. In this article, a reinforcement learning strategy is developed to enhance PSO in convergence by replacing the uniformly distributed random number in the updating function with a random number generated from a selected normal distribution. In the proposed method, the mean and standard deviation of the normal distribution are estimated from the current state of each individual through a policy net. The historic behaviour of the swarm group is used to update the policy net and guide the selection of parameters of the normal distribution. The proposed method is integrated into the original PSO and a state-of-the-art PSO, called the self-adaptive dynamic multi-swarm PSO (sDMS-PSO), and tested with numerical functions and engineering problems. The test results show that the convergence rate of PSO methods can be improved with the proposed reinforcement learning strategy.

UAV Path Planning under Dynamic Threats Using an Improved PSO Algorithm

This paper presents the method to solve the problem of path planning for an unmanned aerial vehicle (UAV) in adversarial environments including radar-guided surface-to-air missiles (SAMs) and unknown threats. SAM lethal envelope and radar detection for SAM threats and line-of-sight (LOS) calculation for unknown threats are considered to compute the cost for path planning. In particular, dynamic SAM lethal envelope is taken into account for path planning in that SAM lethal envelope does change its direction according to the flight direction of UAV. In addition, terrain masking, nonisotropic radar cross section (RCS), and dynamic constraints of UAV are considered to determine the cost of the path. An improved particle swarm optimization (PSO) algorithm is proposed for finding an optimal path. The proposed algorithm is composed of preprocessing steps, multi-swarm PSO algorithm, and postprocessing steps. The Voronoi diagram and Dijkstra algorithm as preprocessing steps provide the initial path for the multi-swarm PSO algorithm which uses multiple swarms with sub-swarms for the balance between exploration and exploitation. Postprocessing steps include waypoint insertion and 3D path smoothing. The computation time is reduced by using the map generation, the coordinate transformation, and the graphic processing unit (GPU) implementation of the algorithm. Various simulations are carried out to compare the performance of the proposed method according to the number of iterations, the number of swarms, and the number of cost evaluation points. The t -test results show that the suggested method is statistically better than existing methods.

Intelligent Tuning of Microwave Cavity Filters Using Granular Multi-Swarm Particle Swarm Optimization

The tuning of microwave cavity filters (MCFs) is a complex process to improve the filtering performance. In practice, the tuning is mostly conducted in a manual way, and thus is time and resource intensive. Toward the demand for automatic tuning of MCFs with high accuracy and efficiency, this article proposes an intelligent tuning method for MCFs via modeling and optimization. The main contributions are threefold as follows: 1) A series of performance evaluation functions are defined to comprehensively characterize the tuning output; 2) a block modeling method is proposed to construct the electromechanical characteristic model to facilitate the calculation of the cost function; 3) an improved particle swarm optimization (PSO) algorithm, named granular multi-swarm PSO (GMS-PSO), is proposed to achieve quick search of optimal combinations of the tunable components. The effectiveness and practicality of the proposed method are demonstrated by experiments with a real intelligent tuning system.

Application of binary PSO for public cloud resources allocation system of video on demand (VoD) services

Video streaming, whether on demand or live, has become one of the most popular internet applications. However, financial investments required for it is a severe problem since it needs more real time storage, higher data transfer and a significant amount of computation than other kinds of multimedia data. To tackle this problem, cloud computing, offering services without investing in hardware or software, emerges as a preferred technology. However, there are a large number of cloud service providers and they offer different pricing strategies for various applications in various regions. Therefore, it is of great importance for them that incoming service requests are assigned to the appropriate cloud services at minimum cost and provide maximum user satisfaction [quality of service (QoS) attributes]. Due to the issues, such as multiple cloud providers, different QoS requirements, different service level agreements and uncertainties in demand, price and availability, the optimization of resource allocation present further challenges. The objective of our study is to optimize the cost and performance of video on demand applications using cloud content delivery networks, storage and transcoders based on the QoS requirements of users. To solve the NP-hard problem, Particle Swarm Optimization (PSO) technique is used due to the easiness in its concept and coding, less sensitive to the nature of the objective function, limited number of parameters and generating high quality solution within a short time. We propose a new method in which the optimum solution is affected not only by the best solution of the particle and global best solution but also by the best solution of the neighborhood particles in that iteration. This ternary approach is implemented into the well-known discrete and constrained PSO, achieving the minimum cost with user satisfaction for allocation of video requests to cloud resources. Although the proposed method yields better results in terms of accuracy, execution time of the algorithm is not reasonable. To overcome this inefficiency; ternary approach is embedded into multi-swarm PSO and it is parallelized and combined with greedy heuristic algorithms. The results of the comparison with the benchmarking algorithms show that our proposed method yields better results from the standpoint of both accuracy and execution time.

Heterogeneous comprehensive learning and dynamic multi-swarm particle swarm optimizer with two mutation operators

In this paper, a heterogeneous comprehensive learning and dynamic multi-swarm particle swarm optimizer with two mutation operators (HCLDMS-PSO) is presented. In addition, a comprehensive learning (CL) strategy with the global optimal experience of the whole population is conducted to generate an exploitation subpopulation exemplar. However, a modified dynamic multi-swarm (DMS) strategy is specially designed to construct the exploration subpopulation exemplar. In the canonical DMS strategy, it is unfavorable for different sub-swarms to use the same linear decreasing inertia weight parameter. We first propose classifying the DMS sub-swarms at the search level and then constructing a novel nonlinear adaptive decreasing inertia weight for different sub-swarms, introducing a non-uniform mutation operator to enhance its exploration capability. Finally, the gbest of the whole population also adopts a Gaussian mutation operator to avoid falling into the local optimum. The particles of the two subpopulations will update their velocity independently without crippling one another to prevent a loss of diversity. The performance of HCLDMS-PSO is compared with those of 8 other PSO variants and 11 evolutionary algorithms on two classical benchmark optimization problems and a real-world engineering problem. Experimental results demonstrate that the HCLDMS-PSO improves the convergence speed, accuracy, and reliability on most optimization problems.

Deep learning-based soft computing model for image classification application

The growth of swarm intelligence approaches and machine learning models in the field of medical image processing is extravagant, and the applicability of these approaches for various types of cancer classification has as well grown in the recent years. Considering the growth of these machine learning models, in this work attempt is taken to develop an optimized deep learning neural network classifier for classifying the nodule tissues in the lung cancer images which is an important application in biomedical area. The optimized model developed is the hybrid version of adaptive multi-swarm particle swarm optimizer with the new improved firefly algorithm resulting in better exploration and exploitation mechanism to determine near-optimal solutions. Multi-swarm particle swarm optimizer (MSPSO) possesses strong exploration capability due to its regrouping schedule nature, and the improved firefly algorithm (ImFFA) possesses better exploitation mechanism due to its inherit attractiveness and intensity feature. At this juncture, the new adaptive MSPSO–ImFFA is applied to the deep learning neural classifier to overcome the local and global minima occurrences and premature convergence by tuning its weight values. As a result, in this work the new adaptive MSPSO–ImFFA-based deep learning neural network classifier is employed to classify the lung cancer tissues of the considered lung computed tomography images. Results obtained prove the effectiveness of the deep learning classifier for the considered lung image sample datasets in comparison with the other methods compared from the previous literature works.

A Dynamic Multi-Swarm Particle Swarm Optimizer for Multi-Objective Optimization of Machining Operations Considering Efficiency and Energy Consumption

Facing energy shortage and severe environmental pollution, manufacturing companies need to urgently energy consumption, make rational use of resources and improve economic benefits. This paper formulates a multi-objective optimization model for lathe turning operations which aims to simultaneously minimize energy consumption, machining cost and cutting time. A dynamic multi-swarm particle swarm optimizer (DMS-PSO) is proposed to solve the formulation. A case study is provided to illustrate the effectiveness of the proposed algorithm. The results show that the DMS-PSO approach can ensure good convergence and diversity of the solution set. Additionally, the optimal machining parameters are identified by fuzzy comprehensive evaluation (FCE) and compared with empirical parameters. It is discovered that the optimal parameters obtained from the proposed algorithm outperform the empirical parameters in all three objectives. The research findings shed new light on energy conservation of machining operations.

Novel chaotic grouping particle swarm optimization with a dynamic regrouping strategy for solving numerical optimization tasks

Particle swarm optimization (PSO) has been widely applied to address various optimization problems, since it is easy to implement and only has a few control parameters. However, PSO often suffers from a lack of population diversity during the search process and is ineffective in balancing exploration and exploitation, especially in solving complex numerical optimization tasks. To overcome these disadvantages of PSO for complex numerical optimization problems, a new chaotic grouping PSO algorithm with a dynamic regrouping strategy (CGPSO-DRS) is proposed in this paper. The newly proposed CGPSO-DRS is based on a dynamic multiswarm PSO framework that cooperates with the chaotic grouping mechanism (CGM) and the dynamic regrouping strategy (DRS). First, the CGM divides the entire population into many subswarms via a chaotic sequence. The CGM not only improves the population grouping quality in the search process but also increases the diversity of the population. Second, the DRS is used to guide the regrouping of the population, and the population starts searching with a new configuration. Here, the DRS changes with the number of function evaluations. The DRS facilitates the effective utilization of information to balance the early exploration and the later exploitation performances. In addition, the DRS can increase the diversity of the population in the search process. Experiments have been conducted on 41 benchmark functions, and the numerical results demonstrate that the proposed CGPSO-DRS method outperforms similar population-based approaches and state-of-the-art PSO variants in accelerating the convergence speed and finding the global optimum.

An improved multi-swarm particle swarm optimizer for optimizing the electric field distribution of multichannel transcranial magnetic stimulation.

Multichannel transcranial magnetic stimulation (mTMS) is a therapeutic method to improve psychiatric diseases, which has a flexible working pattern used to different applications. In order to make the electric field distribution in the brain meet the treatment expectations, we have developed a novel multi-swam particle swarm optimizer (NMSPSO) to optimize the current configuration of double layer coil array. To balance the exploration and exploitation abilities, three novel improved strategies are used in NMSPSO based on multi-swarm particle swarm optimizer. Firstly, a novel information exchange strategy is achieved by individual exchanges between sub-swarms. Secondly, a novel leaning strategy is used to control knowledge dissemination in the population, which not only increases the diversity of the particles but also guarantees the convergence. Finally, a novel mutation strategy is introduced, which can help the population jump out of the local optimum for better exploration ability. The method is examined on a set of well-known benchmark functions and the results show that NMSPSO has better performance than many particle swarm optimization variants. And the superior electric field distribution in mTMS can be obtained by NMSPSO to optimize the current configuration of the double layer coil array.