Potential Of Particle Swarm Optimization Research Articles

Learning Accuracy with Particle Swarm Optimization for Music Genre Classification Using Recurrent Neural Networks

Deep learning has revolutionized many fields, but its success often depends on optimal selection hyperparameters, this research aims to compare two sets of learning rates, namely the learning set rates from previous research and rates optimized for Particle Swarm Optimization. Particle Swarm Optimization is learned by mimicking the collective foraging behavior of a swarm of particles, and repeatedly adjusting to improve performance. The results show that the level of Particle Swarm Optimization is better previous level, achieving the highest accuracy of 0.955 compared to the previous best accuracy level of 0.933. In particular, specific levels generated by Particle Swarm Optimization, for example, 0.00163064, achieving competitive accuracy of 0.942-0.945 with shorter computing time compared to the previous rate. These findings underscore the importance of choosing the right learning rate for optimizing the accuracy of Recurrent Neural Networks and demonstrating the potential of Particle Swarm Optimization to exceed existing research benchmarks. Future work will explore comparative analysis different optimization algorithms to obtain the learning rate and assess their computational efficiency. These further investigations promise to improve the performance optimization of Recurrent Neural Networks goes beyond the limitations of previous research.

Swarmed Grey Wolf Optimizer

Background: The Particle Swarm Optimization (PSO) algorithm is amongst the utmost favourable optimization algorithms often employed in hybrid procedures by the researchers considering simplicity, smaller count of parameters involved, convergence speed and capability of searching global optima. The PSO algorithm acquires memory and the collaborative swarm interactions enhances the search procedure. The high exploitation ability of PSO which intends to locate the best solution within a limited region of the search domain gives PSO an edge over other optimization algorithms. Whereas, low exploration ability results in lack of assurance of proper sampling of the search domain and thus enhances the chances of rejecting a domain containing high quality solutions. A perfect harmony between exploration and exploitation abilities in the course of selection of best solution is needed. High exploitation capacity makes PSO get trapped in local minima when its initial location is far off from the global minima. OBJECTIVES: The intent of this study is to reform this drawback of PSO of getting trapped in local minima. With an objective to upgrade the potential of Particle Swarm Optimization (PSO) to exploit along with preventing PSO of getting trapped in local minima, we require an algorithm with a positive acceptable exploration capacity. METHODS: We utilized, the recently developed metaheuristic Grey Wolf Optimizer (GWO) emulating the seeking and hunting techniques of Grey wolves for this purpose. In our way, the GWO has been utilized to assist PSO in a manner to unite their strengths and lessen their weaknesses. The proposed hybrid has two driving parameters to adjust and assign the preference to PSO or GWO. RESULTS: To test the act of the proposed hybrid it has been examined in comparison with the PSO and GWO methods. For this, eleven benchmark functions involving different unimodal and multimodal functions have been taken. The PSO, GWO and SGWO pseudo codes were coded in visual basic. In all the functions parameters of PSO and GWO were chosen as: w = 0.7, c1 = c2 = 2, population size = 30, number of iterations = 30. Experiments were redone 25 times for each of the method and for each benchmark function. The methods were compared with regard to their best and worst values besides their average values and standard deviations. The obtained results revealed that in terms of average values and standard deviations our hybrid SGWO outperformed both PSO and GWO notably. CONCLUSION: The outcomes of the experiments reveals that the proposed hybrid is better in comparison to both PSO and GWO in the search ability. Though the SGWO algorithm refines result quality, the computational complexity also gets elevated. Thus, lowering the computational complexity would be another issue of future work. Moreover, we will apply the proposed hybrid in the field of water quality estimation and prediction.

Single and Multiple Placements of Different DG Types On the Power Distribution System

Integration of distributed generation on power distribution system impacts the network for improved voltage stability and power quality. However, inaccurate sizing and placement of the energy sources can worsen the network performance. This paper proposes a hybrid particle swarm optimization/whale optimization algorithm for the optimal placement of different distribution generation types on a power network. Standalone metaheuristics are efficient and robust optimization tools but are mostly challenged with convergence and sub-optimal solutions. The exploration potential of particle swarm optimization with the selection of higher inertial weight is annexed with the exploitation phase of the whale optimization algorithm. The proposed technique is verified on IEEE 33 – bus distribution system. Results show 86.12% and 89.84% improvement in voltage deviation for Type I and Type III DG injection respectively. Besides, the convergence is achieved in less than 50 iterations compared to standalone methods.

A Particle Swarm Based Algorithm for Functional Distributed Constraint Optimization Problems

Distributed Constraint Optimization Problems (DCOPs) are a widely studied constraint handling framework. The objective of a DCOP algorithm is to optimize a global objective function that can be described as the aggregation of several distributed constraint cost functions. In a DCOP, each of these functions is defined by a set of discrete variables. However, in many applications, such as target tracking or sleep scheduling in sensor networks, continuous valued variables are more suited than the discrete ones. Considering this, Functional DCOPs (F-DCOPs) have been proposed that can explicitly model a problem containing continuous variables. Nevertheless, state-of-the-art F-DCOPs approaches experience onerous memory or computation overhead. To address this issue, we propose a new F-DCOP algorithm, namely Particle Swarm based F-DCOP (PFD), which is inspired by a meta-heuristic, Particle Swarm Optimization (PSO). Although it has been successfully applied to many continuous optimization problems, the potential of PSO has not been utilized in F-DCOPs. To be exact, PFD devises a distributed method of solution construction while significantly reducing the computation and memory requirements. Moreover, we theoretically prove that PFD is an anytime algorithm. Finally, our empirical results indicate that PFD outperforms the state-of-the-art approaches in terms of solution quality and computation overhead.

Automatic VMAT planning for post-operative prostate cancer cases using particle swarm optimization: A proof of concept study

ObjectiveTo investigate the potential of Particle Swarm Optimization (PSO) for fully automatic VMAT radiotherapy (RT) treatment planning. Material and MethodsIn PSO a solution space of planning constraints is searched for the best possible RT plan in an iterative, statistical method, optimizing a population of candidate solutions. To identify the best candidate solution and for final evaluation a plan quality score (PQS), based on dose volume histogram (DVH) parameters, was introduced.Automatic PSO-based RT planning was used for N = 10 postoperative prostate cancer cases, retrospectively taken from our clinical database, with a prescribed dose of EUD = 66 Gy in addition to two constraints for rectum and one for bladder. Resulting PSO-based plans were compared dosimetrically to manually generated VMAT plans. ResultsPSO successfully proposed treatment plans comparable to manually optimized ones in 9/10 cases. The median (range) PTV EUD was 65.4 Gy (64.7–66.0) for manual and 65.3 Gy (62.5–65.5) for PSO plans, respectively. However PSO plans achieved significantly lower doses in rectum D2% 67.0 Gy (66.5–67.5) vs. 66.1 Gy (64.7–66.5, p = 0.016). All other evaluated parameters (PTV D98% and D2%, rectum V40Gy and V60Gy, bladder D2% and V60Gy) were comparable in both plans. Manual plans had lower PQS compared to PSO plans with −0.82 (−16.43–1.08) vs. 0.91 (−5.98–6.25). ConclusionPSO allows for fully automatic generation of VMAT plans with plan quality comparable to manually optimized plans. However, before clinical implementation further research is needed concerning further adaptation of PSO-specific parameters and the refinement of the PQS.

Hybrid Dynamic Neural Network and PID Control of Pneumatic Artificial Muscle Using the PSO Algorithm

Pneumatic artificial muscles (PAM) have been recently considered as a prominent challenge regarding pneumatic actuators specifically for rehabilitation and medical applications. Since accomplishing accurate control of the PAM is comparatively complicated due to time-varying behavior, elasticity and ambiguous characteristics, a high performance and efficient control approach should be adopted. Besides of the mentioned challenges, limited course length is another predicament with the PAM control. In this regard, this paper proposes a new hybrid dynamic neural network (DNN) and proportional integral derivative (PID) controller for the position of the PAM. In order to enhance the proficiency of the controller, the problem under study is designed in the form of an optimization trend. Considering the potential of particle swarm optimization, it has been applied to optimally tune the PID-DNN parameters. To verify the performance of the proposed controller, it has been implemented on a real-time system and compared to a conventional sliding mode controller. Simulation and experimental results show the effectiveness of the proposed controller in tracking the reference signals in the entire course of the PAM.

A dynamic particle swarm optimization method applied to global optimizations of engineering inverse problem

PurposeThe aim of this paper is to explore the potential of particle swarm optimization (PSO) algorithm to solve an electromagnetic inverse problem.Design/methodology/approachA modified PSO algorithm is designed.FindingsThe modified PSO algorithm is a more stable, robust and efficient global optimizer for solving the well-known benchmark optimization problems. The new mutation approach preserves the diversity of the population, whereas the proposed dynamic and adaptive parameters maintain a good balance between the exploration and exploitation searches. The numerically experimental results of two case studies demonstrate the merits of the proposed algorithm.Originality/valueSome improvements, such as the design of a new global mutation mechanism and introducing a novel strategy for learning and control parameters, are proposed.

A New Representation in PSO for Discretization-Based Feature Selection.

In machine learning, discretization and feature selection (FS) are important techniques for preprocessing data to improve the performance of an algorithm on high-dimensional data. Since many FS methods require discrete data, a common practice is to apply discretization before FS. In addition, for the sake of efficiency, features are usually discretized individually (or univariate). This scheme works based on the assumption that each feature independently influences the task, which may not hold in cases where feature interactions exist. Therefore, univariate discretization may degrade the performance of the FS stage since information showing feature interactions may be lost during the discretization process. Initial results of our previous proposed method [evolve particle swarm optimization (EPSO)] showed that combining discretization and FS in a single stage using bare-bones particle swarm optimization (BBPSO) can lead to a better performance than applying them in two separate stages. In this paper, we propose a new method called potential particle swarm optimization (PPSO) which employs a new representation that can reduce the search space of the problem and a new fitness function to better evaluate candidate solutions to guide the search. The results on ten high-dimensional datasets show that PPSO select less than 5% of the number of features for all datasets. Compared with the two-stage approach which uses BBPSO for FS on the discretized data, PPSO achieves significantly higher accuracy on seven datasets. In addition, PPSO obtains better (or similar) classification performance than EPSO on eight datasets with a smaller number of selected features on six datasets. Furthermore, PPSO also outperforms the three compared (traditional) methods and performs similar to one method on most datasets in terms of both generalization ability and learning capacity.

Particle Swarm Optimization/Greedy-Search Algorithm for Helicopter Mission Assignment in Disaster Relief

In the immediate aftermath of a large-scale disaster, optimal helicopter rescue mission assignment is critical to saving many lives. However, the current practice in the field is mostly human centered. The Japan Aerospace Exploration Agency has been developing a decision support system for aircraft operation in order to promptly plan and execute rescue missions. The current research focuses on evacuation missions in particular and investigates the potential of particle swarm optimization with an integrated greedy search in aircraft resource management. A robust particle model is proposed that can reflect various helicopter properties as well as evacuation mission characteristics. Particle swarm optimization parameters are modified and set based on numerical simulations, and the values determined in this way are used in an optimization of disaster relief mission assignments based on real data obtained during the Great East Japan earthquake and tsunami of 2011. The swarm is initialized with a greedy-search algorithm to increase the calculation speed and improve the quality of the results. It is shown that a hybrid particle swarm optimization/greedy-search algorithm can be successfully adapted to disaster relief support systems and provide valuable analysis and decision-making information to the authorities in charge.

Particle swarm optimization (PSO). A tutorial

Swarm-based algorithms emerged as a powerful family of optimization techniques, inspired by the collective behavior of social animals. In particle swarm optimization (PSO) the set of candidate solutions to the optimization problem is defined as a swarm of particles which may flow through the parameter space defining trajectories which are driven by their own and neighbors' best performances. In the present paper, the potential of particle swarm optimization for solving various kinds of optimization problems in chemometrics is shown through an extensive description of the algorithm (highlighting the importance of the proper choice of its metaparameters) and by means of selected worked examples in the fields of signal warping, estimation robust PCA solutions and variable selection.

Potential of particle swarm optimization based radial basis function network to predict the discharge coefficient of a modified triangular side weir

Estimating the discharge coefficient is one of the most important steps in the process of side weir design. In this paper, the particle swarm optimization algorithm and radial basis neural network are combined (RBFN-PSO) and employed to model the discharge coefficient of a modified triangular side weir. The developed RBF network has five neurons in the input layer and one neuron in the output layer. The inputs include a wide range of non-dimensional geometrical and hydraulic parameters of a modified triangular side weir, and the output is the discharge coefficient. The RBFN-PSO performance is evaluated using published experimental results and compared with the backpropagation radial basis function network (RBFN-BP) by using the statistical indexes Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and average absolute deviation (%δ). According to the results, the PSO algorithm successfully improved the RBFN while the RBFN-PSO model’s generalization capacity enhanced, with RMSE of 0.071 compared to the RBFN-BP model with RMSE of 0.114 in the testing dataset.

Potential of Particle Swarm Optimization and Genetic Algorithms for FIR Filter Design

This article studies the performance of two metaheuristics, particle swarm optimization (PSO) and genetic algorithms (GA), for FIR filter design. The two approaches aim to find a solution to a given objective function but employ different strategies and computational effort to do so. PSO is a more recent heuristic search method than GA; its dynamics exploit the collaborative behavior of biological populations. Some researchers advocate the superiority of PSO over GA and highlight its capacity to solve complex problems thanks to its ease of implementation. In this paper, different versions of PSOs and GAs including our specific GA scheme are compared for FIR filter design. PSO generally outperforms standard GAs in some performance criteria, but our adaptive genetic algorithm is shown to be better on all criteria except CPU runtime. The study also underlines the importance of introducing intelligence in metaheuristics to make them more efficient by embedding self-tuning strategies. Furthermore, it establishes the potential complementarity of the approaches when solving this optimization problem.

Minimizing sidelobe levels and facilitating null placements of nonlinear antenna arrays using an improved particle swarm optimization method

Purpose – The aim of this paper is to explore the potential of particle swarm optimization (PSO) methods for minimizing the sidelobe levels (SLL) and placing null at arbitrary angles of a nonlinear antenna array. Design/methodology/approach – An improved PSO algorithm is designed. Findings – The improved PSO method is an efficient and robust global optimizer for minimizing the SLL and placing null at arbitrary angles of a nonlinear antenna array. Originality/value – Some improvements, such as the design of some new formulae for both position and velocity updating, the introduction of an age variable, and the devise of an intensification searches using the cross entropy method, are proposed.

AntBot: Ant Colonies for Video Games

The video game industry is an emerging market which continues to expand. From its early beginning, developers have focused mainly on sound and graphical applications, paying less attention to developing game bots or other kinds of nonplayer characters (NPCs). However, recent advances in artificial intelligence offer the possibility of developing game bots which are dynamically adjustable to several difficulty levels as well as variable game environments. Previous works reveal a lack of swarm intelligence approaches to develop these kinds of agents. Considering the potential of particle swarm optimization due to its emerging properties and self-adaptation to dynamic environments, further investigation into this field must be undertaken. This research focuses on developing a generic framework based on swarm intelligence, and in particular on ant colony optimization, such as it allows general implementation of real-time bots that work over dynamic game environments. The framework has been adapted to allow the implementation of intelligent agents for the classical game Ms. Pac-Man. These were trialed at the Ms. Pac-Man competitions held during the 2011 International Congress on Evolutionary Computation.

Pitfalls and potential of particle swarm optimization for contemporary spatial forest planning

We describe here an example of applying particle swarm optimization (PSO) — a population-based heuristic technique — to maximize the net present value of a contemporary southern United States forest plan that includes spatial constraints (green-up and adjacency) and wood flow constraints. When initiated with randomly defined feasible initial conditions, and tuned with some appropriate modifications, the PSO algorithm gradually converged upon its final solution and provided reasonable objective function values. However, only 86% of the global optimal value could be achieved using the modified PSO heuristic. The results of this study suggest that under random-start initial population conditions the PSO heuristic may have rather limited application to forest planning problems with economic objectives, wood-flow constraints, and spatial considerations. Pitfalls include the need to modify the structure of PSO to both address spatial constraints and to repair particles, and the need to modify some of the basic assumptions of PSO to better address contemporary forest planning problems. Our results, and hence our contributions, are contrary to earlier work that illustrated the impressive potential of PSO when applied to stand-level forest planning problems or when applied to a high quality initial population.

Modelling of a twin rotor system: A particle swarm optimization approach

A scaled and simplified version of a practical helicopter, namely the twin rotor multi-input multi-output system (TRMS) is often used as a laboratory platform for control experiments. This system resembles a helicopter in many aspects. Since the TRMS permits both 1 and 2 degrees of freedom (DOF) motions, it can be considered as a static test rig for an air vehicle. This study presents investigations into modelling of the TRMS using particle swarm optimization (PSO). First, 1-DOF models are extracted for both vertical (pitch) and horizontal (yaw) channels independently. Then, a 2-DOF parametric model is developed taking cross-coupling between the channels into consideration. A particle swarm algorithm that uses time-varying inertia weight factor and time-varying acceleration coefficients is considered in this work. The effectiveness of the algorithm in modelling the system is validated and verified in terms of tracking, stability, and ability of derived model in capturing the dynamics of the system. Time domain and frequency domain results of the derived models clearly show the potential of PSO in solving such control problems.

Particle Swarm Optimization

Gerhard Venter (gventer_vrand.conl) *Vanderpla(ds Research and Development, bit.1767 S 8th St'reef. Suite 100, Colorado Springs. CO 80906Jaroslaw Sobieszczanski-Sobieski (j.sobieski:_larc.nasa.gov) *A_4SA Lcmgley Research Ce,_terMS 240, Hampton, I:4 23681-2199The purpose of this paper is to show how the search algorithm, known as par-ticle swarm optimization performs. Here, particle swarm optimization ks appliedto structural design problems, but the method.has a much wider range of possi-ble applications. The paper's new contributions are improvements to the particleswarm optimization algorithm and conclusions and recommendations as to theutility of the algorithm. Results of numerical experiments for both continuousand discrete applications are presented in the paper. The results indicate that theparticle swarm optimization algorithm does locate the constrained minimum de-sign in continuous applications with very good precision, albeit at a much highercomputational cost than that of a typical gradient based optimizer. However, thetrue potential of particle swarm optimization is primarily in applications withdiscrete and/or discontinuous functions and variables. Additionally, particleswarm optimization has the potential of e3_icient computation with very largenumbers of concurrently operating processors.