Original Particle Swarm Optimization Research Articles

A Modified Particle Swarm Optimization for Task Scheduling in Cloud Computing

Cloud computing emerges as a powerful platform to deliver IT services through internet. Due to rapid development of cloud computing the user’s dependencies on cloud have increased therefore scheduling of task is the key challenge. This research paper focused on scheduling user’s requests to best suitable resources in optimal way. We propose a modified particle swarm optimization (MPSO) task scheduling algorithm in order to optimize execution time, transmission time, makespan, transmission cost and load balancing of virtual machines. The MPSO algorithm maintains inertia weight from 0.4 to 0.9 and it plays an important role to achieve best cost. The implementation result of MPSO on CloudSim produces best cost as compared to original PSO.

A novel improvement of Kriging surrogate model

PurposeThis paper aims to introduce a method based on the optimizer of the particle swarm optimization (PSO) algorithm to improve the efficiency of a Kriging surrogate model.Design/methodology/approachPSO was first used to identify the best group of trend functions and to optimize the correlation parameter thereafter.FindingsThe Kriging surrogate model was used to resolve the fuselage optimization of an unmanned helicopter.Practical implicationsThe optimization results indicated that an appropriate PSO scheme can improve the efficiency of the Kriging surrogate model.Originality/valueBoth the STANDARD PSO and the original PSO algorithms were chosen to show the effect of PSO on a Kriging surrogate model.

Microwave imaging based on two hybrid particle swarm optimization approaches

AbstractIn this paper, the solution of the inverse scattering problem for determining the shape and location of perfectly conducting scatterers by making use of electromagnetic scattered fields is presented. Based on the boundary condition and the measured scattered field, a set of nonlinear integral equations is derived and the imaging problem is reformulated into an optimization one. Then, two evolutionary algorithms are used to solve the inverse scattering problem. To further clarify, our contribution is to test two well-known algorithms in the literature to the problem of microwave imaging. The hybrid approaches combine the standard particle swarm optimization (PSO) with the ideas of the simulated annealing and extremal optimization algorithms, respectively. Both of them are shown to be more efficient than original PSO technique. Reconstruction results by using the two presented schemes are compared with exact shapes of some conducting cylinders; and good agreements with the original shapes are observed.

Hybrid PSO-FLC for dynamic global peak extraction of the partially shaded photovoltaic system.

Particle Swarm Optimization (PSO) is widely used in maximum power point tracking (MPPT) of photovoltaic (PV) energy systems. Nevertheless, this technique suffers from two main problems in the case of partial shading conditions (PSCs). The first problem is that PSO is a time invariant optimization technique that cannot follow the dynamic global peak (GP) under time variant shading patterns (SPs) and sticks to the first GP that occurs at the beginning. This problem can be solved by dispersing the PSO particles using two new techniques introduced in this paper. The two new proposed PSO re-initialization techniques are to disperse the particles upon the SP changes and the other one is upon a predefined time (PDT). The second problem is regarding the high oscillations around steady state, which can be solved by using fuzzy logic controller (FLC) to fine-tune the output power and voltage from the PV system. The new contribution of this paper is the hybrid PSO-FLC with two PSO particles dispersing techniques that is able to solve the two previous mentioned problems effectively and improve the performance of the PV system in both normal and PSCs. A detailed list of comparisons between hybrid PSO-FLC and original PSO using the two proposed methodologies are achieved. The results prove the superior performance of hybrid PSO-FLC compared to PSO in terms of efficiency, accuracy, oscillations reduction around steady state and soft tuning of the GP tracked.

Real-Time Particle Swarm Optimization on FPGA for the Optimal Message-Chain Structure

This paper addresses the real-time optimization problem of the message-chain structure to maximize the throughput in data communications based on half-duplex command-response protocols. This paper proposes a new variant of the particle swarm optimization (PSO) algorithm to resolve real-time optimization, which is implemented on field programmable gate arrays (FPGA) to be performed faster in parallel and to avoid the delays caused by other tasks on a central processing unit. The proposed method was verified by finding the optimal message-chain structure much faster than the original PSO, as well as reliably with different system and algorithm parameters.

Air Pollutant Concentration Forecast Based on Support Vector Regression and Quantum-Behaved Particle Swarm Optimization

In order to improve the forecasting accuracy of atmospheric pollutant concentration, a prediction model of atmospheric PM2.5 and nitrogen dioxide (NO2) concentration based on support vector regression (SVR) is established. Quantum-behaved particle swarm optimization (QPSO) algorithm is used to select the optimal parameters influencing the performance of SVR. And in order to improve the problem that the fixed SVR model is difficult to adapt to the highly nonlinear process, a simple online SVR based on re-modeling method is proposed instead of the fixed one. According to hourly PM2.5 and NO2 concentrations and meteorological conditions from May 2014 to April 2015 in Wanliu Monitoring Station of Beijing in China, the experiment is carried out based on the data of 3 months. Meanwhile, PM2.5 concentration is predicted by three different prediction methods, including the recursive prediction method, direct prediction method, and online direct prediction method. The results show that the online direct prediction method is the most accurate in the three prediction methods. In addition, compared with original particle swarm optimization (PSO) algorithm, QPSO algorithm is tested more efficiently for the improvement of global search ability and robustness during the procedure of parameter selection. Moreover, the hybrid QPSO-SVR model proposed in this paper has higher prediction accuracy and less computational time compared with the PSO-SVR model, genetic algorithm (GA)-SVR model, and grid search (GS)-SVR model, which indicates reliability and effectiveness of the QPSO-SVR model in prediction of these two pollutant concentrations.

An ameliorated particle swarm optimizer for solving numerical optimization problems

Although the particle swarm optimizer (PSO) has been widely used to address various complicated engineering problems, it is likely to suffer lack of diversity and ineffectiveness of balance between the global search ability and the local search ability in the search process. In this paper, we report an innovative and improved optimization method called ameliorated particle swarm optimizer (A-PSO), which is different from the original PSO algorithm and its variants in parameter update and the position generation of each particle. In A-PSO, the nonlinear dynamic acceleration coefficients, logistic map and a modified particle position update approach are introduced in PSO to improve the solution quality and accelerate the global convergence rate. Twenty well-known numerical optimization functions are adopted to evaluate the effectiveness of the proposed method and it is illustrated that, for most numerical optimization problems, the convergence performance and search accuracy of the A-PSO method are superior to the similar heuristic optimization algorithms and other well-known PSO variants. Namely, the proposed A-PSO technique has a faster convergence rate and is more stable than other PSO variants and similar population-based methods for almost all numerical optimization problems. Therefore, the A-PSO method is successfully used as a new optimization technique for solving numerical optimization problems.

A particle swarm optimizer with modified velocity update and adaptive diversity regulation

AbstractThis study introduces reverse direction supported particle swarm optimization (RDS‐PSO) with an adaptive regulation procedure. It benefits from identifying the global worst and global best particles to increase the diversity of the PSO. The velocity update equation of the original PSO was changed according to this idea. To control the impacts of the global best and global worst particles on the velocity update equation, the alpha parameter was added to the velocity update equation. Moreover, a procedure for diversity regulation based on cosine amplitude or max–min methods was introduced. Alpha value was changed adaptively with respect to this diversity measure. Besides, RDS‐PSO was implemented with both linearly increasing and decreasing inertia weight (with 1,000 and 2,000 iterations) in order to survey the effects of these variations on RDS‐PSO performances. Six most commonly used benchmark functions and three medical classification problems were selected as experimental data sets. All experimental results showed that when the grain searching ability is not so small in the last generations, the algorithm performance continues to increase. Experimental proof of it was showed up especially in RDS‐PSO using the cosine amplitude approach. Because the best results among all the RDS‐PSO types for decreasing inertia weight modes were obtained with 2,000 maximal iterations rather than 1,000 ones.

A novel optimal replication allocation strategy for particle swarm optimization algorithms applied to simulation optimization problem

This study proposes a simulation optimization algorithm, called optimal replication allocation strategy (ORAS), for particle swarm optimization (PSO) algorithms in simulation models with high computing cost in large design spaces. In this study, the PSO is employed to explore and exploit near-optimal or optimal solution in the design space. Given the uncertainties in real-word applications, a simulation model is constructed to evaluate the performance of each design alternative. Optimal computing budget allocation (OCBA), a state-of-the-art resampling method, is combined with metaheuristic principles to improve the accuracy of estimating best solutions and enhancing efficiency by intelligently allocating the number of replications. However, the solution space is nonlinear or multimodal; that is, various local or global solutions exist. In OCBA, the probability of correct selection (P(CS)) in the currect best solution serves as an important measurement. P(CS) refers to the probability that the “best” of k populations is selected according to some specified criteria of best. OCBA can halt allocation when P(CS) reaches higher than the desired value, i.e., P(CS)*. The multimodal solution space features a high probability of reaching a low P(CS) as many solutions perform closely to the current best of each generation. This situation indicates that P(CS) cannot achieve P(CS)*; OCBA cannot stop allocation, and additional computational cost may be wasted. In this study, we redefine and modify P(CS). The new version is P(CSE), which considers calculation of global best, called the super individual, instead of current best solution within a confidence level. The proposed ORAS can provide an asymptotically optimal allocation rule for combining with population metaheuristics based on P(CSE). We apply ORAS using an original particle swarm optimization (PSO) and two variants of PSO to address stochastic buffer allocation problem and stochastic function optimization problem compared with several state-of-the-art technologies from literature. The resulting ORAS with three PSOs is an effective procedure as it intelligently utilizes limited computing resources. Numerical tests indicate that ORAS increases P(CSE) in each generation and subsequently enhances the efficiency of PSO algorithms.

Parameter optimization of power system stabilizers via kidney-inspired algorithm

This article describes the application of a new population-based meta-heuristic optimization algorithm inspired by the kidney process in the human body for the tuning of power system stabilizers (PSSs) in a multi-machine power system. The tuning problem of PSS parameters is formulated as an optimization problem that aims at maximizing the damping ratio of the electromechanical modes and the kidney-inspired algorithm (KA) is used to search for the optimal parameters. The efficacy of the KA-based PSS design was successfully tested on a well-known 16-machine, 68-bus power system. The obtained results are evaluated and compared with the other results obtained by the original particle swarm optimization (PSO) and the bat algorithm (BA) methods. From the detailed eigenvalue analysis, the nonlinear simulation studies and some performance indices it has been found out that for damping oscillations, the performance of the proposed KA approach in this study is better than that obtained by other intelligent techniques (PSO and BA). Moreover, the efficiency and the superior performance of the proposed method over the other two algorithms in terms of computation time, convergence rate and solution quality are confirmed.

Intelligent skin cancer detection using enhanced particle swarm optimization

In this research, we undertake intelligent skin cancer diagnosis based on dermoscopic images using a variant of the Particle Swarm Optimization (PSO) algorithm for feature optimization. Since the identification of the most significant discriminative characteristics of the benign and malignant skin lesions plays an important role in robust skin cancer detection, the proposed PSO algorithm is employed for feature optimization. It incorporates not only subswarms, local and global food and enemy signals, attraction and flee operations, and mutation-based local exploitation, but also diverse matrix representations to mitigate premature convergence of the original PSO algorithm. Specifically, two remote swarm leaders, which show similar fitness but low position proximity, are used to lead the subswarm-based search and to enable the exploration of more distinctive search regions. Modified velocity updating strategies are also proposed to enable the particles to follow multiple swarm leaders and avoid the local and global worst individuals, partially (i.e. in randomly selected sub-dimensions) and fully (in every dimension), with an attempt to search for global optima. Probability distribution and dynamic matrix representations are used to diversify the search process. Evaluated with multiple skin lesion and UCI databases and diverse unimodal and multimodal benchmark functions, the proposed PSO variant shows a superior performance over those of other advanced and classical search methods for identifying discriminative features that facilitate benign and malignant lesion classification as well as for solving diverse optimization problems with different landscapes. The Wilcoxon rank sum test is adopted to further ascertain superiority of the proposed algorithm over other methods statistically.

Ensemble learning particle swarm optimization for real-time UWB indoor localization

This paper presents an ensemble learning particle swarm optimization (ELPSO) algorithm for real-time indoor localization based on ultra-wideband (UWB). Indoor localization problem can be formulated as an optimization problem to predict the target. The proposed algorithm expands the original PSO into ELPSO under superbest guide, which is a parameter employed to identify the top gbest by learning from three individual algorithms and updated asynchronously. The performance of the proposed ELPSO is evaluated by using the CEC2005 benchmark and compared with each individual algorithm and other state-of-the-art optimization algorithms. The feasibility of the proposed ELPSO is demonstrated in both 2D and 3D UWB indoor localization system generating promising results.

Empirical study of particle swarm optimization inspired by Lotka–Volterra model in Ecology

Particle swarm optimization (PSO) has been proved to be an effective technique in solving complex global optimization problems. Many modified versions of the original PSO algorithm emerged during the last 15 years. Many of those existing methods employ all particles in a single population which adopts the similar monotonic strategy. The loss of diversity resulted in the premature convergence problem. In this paper, we proposed a suite of multi-swarm Lotka–Volterra model inspired particle swarm optimization algorithms (MSLVPSO) to address the premature convergence problem. The intraspecific and interspecific cooperation and competition strategy of the proposed model dramatically increased diversity of particles. As a result, it makes the particles more likely to break away from the local optimum. In addition, we derived the method to set parameters and developed several cooperative–competitive schemes. We evaluated the proposed MSLVPSO algorithms using a variety of benchmark functions. We also compared our proposed method with typical single-swarm PSO algorithms. Our experimental results show that the proposed MSLVPSO optimizers outperform other state-of-the-art algorithms.

Double-Group Particle Swarm Optimization and Its Application in Remote Sensing Image Segmentation.

Particle Swarm Optimization (PSO) is a well-known meta-heuristic. It has been widely used in both research and engineering fields. However, the original PSO generally suffers from premature convergence, especially in multimodal problems. In this paper, we propose a double-group PSO (DG-PSO) algorithm to improve the performance. DG-PSO uses a double-group based evolution framework. The individuals are divided into two groups: an advantaged group and a disadvantaged group. The advantaged group works according to the original PSO, while two new strategies are developed for the disadvantaged group. The proposed algorithm is firstly evaluated by comparing it with the other five popular PSO variants and two state-of-the-art meta-heuristics on various benchmark functions. The results demonstrate that DG-PSO shows a remarkable performance in terms of accuracy and stability. Then, we apply DG-PSO to multilevel thresholding for remote sensing image segmentation. The results show that the proposed algorithm outperforms five other popular algorithms in meta-heuristic-based multilevel thresholding, which verifies the effectiveness of the proposed algorithm.

Multi-objective short-term scheduling of a renewable-based microgrid in the presence of tidal resources and storage devices

Daily increasing use of tidal power generation proves its outstanding features as a renewable source. Due to environmental concerns, tidal current energy which has no greenhouse emission attracted researchers’ attention in the last decade. Additionally, the significant potential of tidal technologies to economically benefit the utility in long-term periods is substantial. Tidal energy can be highly forecasted based on short-time given data and hence it will be a reliable renewable resource which can be fitted into power systems. In this paper, investigations of effects of a practical stream tidal turbine in Lake Saroma in the eastern area of Hokkaido, Japan, allocated in a real microgrid (MG), is considered in order to solve an environmental/economic bi-objective optimization problem. For this purpose, an intelligent evolutionary multi-objective modified bird mating optimizer (MMOBMO) algorithm is proposed. Additionally, a detailed economic model of storage devices is considered in the problem. Results show the efficiency of the suggested algorithm in satisfying economic/environmental objectives. The effectiveness of the proposed approach is validated by making comparison with original BMO and PSO on a practical MG.

An improved quantum particle swarm optimisation and its application on hand kinematics tracking

The evolutional motivated particle swarm optimisation (PSO) has been widely employed in various scientific areas, and there has been plenty of contribution on the modification and improvement of PSO. Recently, a quantum behaviour inspired optimisation algorithm (QPSO) was developed by modelling a Delta potential well in quantum space, which shows better performance in global search ability and convergence precision compared with the original PSO algorithm. In this paper, based on the principle of QPSO, we proposed a dynamic search strategy fused with chaos map to strengthen the ability of escaping from local optima, and replaced the attractor with beta distribution for faster convergence speed. We first compared this improved algorithm (DCQPSO) with PSO and QPSO on general optimisation benchmark functions. Then, from the point view of application, we also achieved a simplicity-oriented human hand kinematics tracking system by leveraging DCQPSO, which can be further served in human computer interaction (HCI). Indicated by the experiments result, DCQPSO outperforms either traditional PSO or QPSO algorithm, and it can be well qualified with optimisation task in hand kinematics tracking.

An effective Bat algorithm for node localization in distributed wireless sensor network

Wireless sensor networks (WSNs) have recently been extensively investigated due to their numerous applications in processes that have to be spread over a large area. One of the important issues of WSN is node localization; node localization capability is highly desirable for the performance evaluation in monitoring applications. Localization is defined as estimating the locations of sensors with initially unknown location information as most sensors do not know their locations due to the cost and size of sensors. The main objective of localization is to find the nodes' positions in a short time with a low energy cost; for this reason, recent approaches relying on swarm intelligence techniques are utilized, and node localization is considered an optimization problem in a multidimensional space. Recently, the meta‐heuristic Bat algorithm was proposed as a solution for the node localization problem. This paper proposes an effective Bat algorithm for the node localization problem, the effectiveness of which is based on the adaptation of velocity of the Bats by hybridization, with Doppler effect for improving the performance, aptly termed Dopeffbat. Hence, Dopeffbat computes (through evolution) the nodes' positions iteratively through the Euclidian distance as fitness. Deploying this algorithm on a large WSN with hundreds of sensors demonstrates decent performance in terms of node localization. Moreover, the Dopeffbat parameters are simulated and interpreted in different scenarios of simulation; in addition, a comparative study was performed to further demonstrate the performance of the proposed algorithm, and the simulation results prove that Dopeffbat has a high convergence rate and greater precision compared with the original Bat algorithm and particle swarm optimization (PSO) algorithms.

A Particle Swarm Optimization-Based Heuristic for Software Module Clustering Problem

The large-scale software module clustering problems (SMCPs) are very difficult to solve by using traditional analytical/deterministic-based optimization methods due to their high complexity and computation cost. Recently, particle swarm optimization (PSO) algorithm, a non-deterministic meta-heuristic search algorithm, gained wide attention and has been adapted to address the various large-scale science and engineering optimization problems. However, the applicability and usefulness of PSO algorithm have not been studied by any researcher till date to solve the SMCPs. In this paper, we introduce PSO-based module clustering (PSOMC), which partitions software system by optimizing: (1) intracluster dependency, (2) intercluster dependency, (3) a number of clusters, and (4) a number of module per cluster. To this contribution, we redefine the terms “position” and “velocity” of original PSO under the discrete scenario that best suited to SMCPs. To demonstrate the performance of the proposed approach, extensive experiments on six real-world SMCPs are carried out. We also compare our approach with existing state-of-the-art software module clustering meta-heuristic approaches (group genetic algorithm, hill climbing, and simulated annealing algorithm). The experimental results show that the proposed approach is effective and promising for solving SMCPs.

A two-dimensional (2-D) learning framework for Particle Swarm based feature selection

This paper proposes a new generalized two dimensional learning approach for particle swarm based feature selection. The core idea of the proposed approach is to include the information about the subset cardinality into the learning framework by extending the dimension of the velocity. The 2D-learning framework retains all the key features of the original PSO, despite the extra learning dimension. Most of the popular variants of PSO can easily be adapted into this 2D learning framework for feature selection problems. The efficacy of the proposed learning approach has been evaluated considering several benchmark data and two induction algorithms: Naive–Bayes and k-Nearest Neighbor. The results of the comparative investigation including the time-complexity analysis with GA, ACO and five other PSO variants illustrate that the proposed 2D learning approach gives feature subset with relatively smaller cardinality and better classification performance with shorter run times.

A fuzzy C‐regression model algorithm using a new PSO algorithm

SummaryIn this paper, a new methodology is introduced for the identification of the parameters of the multiple‐input–multiple‐output local linear Takagi‐Sugeno fuzzy models using the weighted recursive least squares (WRLS). The WRLS is sensitive to initialization, which leads to no convergence. In order to overcome this problem, adaptive chaos particle swarm optimization is proposed to optimize the initial states of WRLS. This new algorithm is improved versions of the original particle swarm optimization algorithm. Finally, comparative experiments are designed to verify the validity of the proposed clustering algorithm and the Takagi‐Sugeno fuzzy model identification method, and the results show that the new method is effective in describing a complicated nonlinear system with significantly high accuracies compared with approaches in the literature.