Bare Bones Particle Swarm Optimization Research Articles

Solving optimal reactive power dispatch problem using a novel teaching–learning-based optimization algorithm

The paper presents a novel teaching–learning-based optimization (TLBO) algorithm, the Gaussian bare-bones TLBO (GBTLBO) algorithm, with its modified version (MGBTLBO) for the optimal reactive power dispatch (ORPD) problem with discrete and continuous control variables in the standard IEEE power systems for reduction in power transmission loss. The feasibility and performance of the GBTLBO and MGBTLBO algorithms are demonstrated for standard IEEE 14-bus and standard IEEE 30-bus systems. A comparison of simulation results reveals optimization efficacy of the GBTLBO and MGBTLBO algorithms over other well established other algorithms like bare-bones differential evolution (BBDE) and bare-bones particle swarm optimization (BBPSO) algorithm. Results for ORPD problem demonstrate superiority in terms of solution quality of the GBTLBO and MGBTLBO algorithms over original TLBO algorithm and other algorithm.

Self regulating particle swarm optimization algorithm

In this paper, we propose a new particle swarm optimization algorithm incorporating the best human learning strategies for finding the optimum solution, referred to as a Self Regulating Particle Swarm Optimization (SRPSO) algorithm. Studies in human cognitive psychology have indicated that the best planners regulate their strategies with respect to the current state and their perception of the best experiences from others. Using these ideas, we propose two learning strategies for the PSO algorithm. The first one uses a self-regulating inertia weight and the second uses the self-perception on the global search direction. The self-regulating inertia weight is employed by the best particle for better exploration and the self-perception of the global search direction is employed by the rest of the particles for intelligent exploitation of the solution space. SRPSO algorithm has been evaluated using the 25 benchmark functions from CEC2005 and a real-world problem for a radar system design. The results have been compared with six state-of-the-art PSO variants like Bare Bones PSO (BBPSO), Comprehensive Learning PSO (CLPSO), etc. The two proposed learning strategies help SRPSO to achieve faster convergence and provide better solutions in most of the problems. Further, a statistical analysis on performance evaluation of the different algorithms on CEC2005 problems indicates that SRPSO is better than other algorithms with a 95% confidence level.

Coal Dust Recognition Based on Concave Point Extraction and Ellipse Fitting

A new coal dust particle recognition algorithm based on concave points extraction and ellipse fitting is proposed for the features of irregularities and particle overlap. The new algorithm includes contour processing and ellipse fitting in this paper. In the part of contour processing, the feature points are obtained with polygonal approximation on the edge of a binary dust particles image, and then concave points of overlapping particles are extracted by the method of angle combined with size, finally the edge is segmented by concave points. To solve the problem that direct least square ellipse fitting is easily affected by noise points, bare bones particle swarm optimization is introduced to find global optimum fitting parameters and the segmented edge is ellipse fitted. Experiment results show this proposed algorithm obtains better recognition performance.

A Novel Empirical Equation for Relative Permeability in Low Permeability Reservoirs

In this paper, a novel empirical equation is proposed to calculate the relative permeability of low permeability reservoir. An improved item is introduced on the basis of Rose empirical formula and Al-Fattah empirical formula, with one simple model to describe oil/water relative permeability. The position displacement idea of bare bones particle swarm optimization is applied to change the mutation operator to improve the RNA genetic algorithm. The parameters of the new empirical equation are optimized with the hybrid RNA genetic algorithm (HRGA) based on the experimental data. The data is obtained from a typical low permeability reservoir well 54 core 27-1 in GuDong by unsteady method. We carry out matlab programming simulation with HRGA. The comparison and error analysis show that the empirical equation proposed is more accurate than the Rose empirical formula and the exponential model. The generalization of the empirical equation is also verified.

Integrating opposition-based learning into the evolution equation of bare-bones particle swarm optimization

Bare-bones particle swarm optimization (BPSO) is attractive since it is parameter free and easy to implement. However, it suffers from premature convergence because of quickly losing diversity, and the dimensionality of the solved problems has great impact on the solution accuracy. To overcome these drawbacks, this paper proposes an opposition-based learning (OBL) modified strategy. First, to decrease the complexity of algorithm, OBL is not used for population initialization. Second, OBL is employed on the personal best positions (i.e., Pbest) to reconstruct Pbest, which is helpful to enhance convergence speed. Finally, we choose the global worst particle (Gworst) from Pbest, which simulates the human behavior and is called rebel learning item, and is integrated into the evolution equation of BPSO to help jump out local optima by changing the flying direction. The proposed modified BPSO is called BPSO-OBL, it has been evaluated on a set of well-known nonlinear benchmark functions in different dimensional search space, and compared with several variants of BPSO, PSOs and other evolutionary algorithms. Experimental results and statistic analysis confirm promising performance of BPSO-OBL on solution accuracy and convergence speed in solving majority nonlinear functions.

Bare bones particle swarm optimization with scale matrix adaptation.

Bare bones particle swarm optimization (BBPSO) is a swarm algorithm that has shown potential for solving single-objective unconstrained optimization problems over continuous search spaces. However, it suffers of the premature convergence problem that means it may get trapped into a local optimum when solving multimodal problems. In order to address this drawback and improve the performance of the BBPSO, we propose a variant of this algorithm, named by us as BBPSO with scale matrix adaptation (SMA), SMA-BBPSO for short reference. In the SMA-BBPSO, the position of a particle is selected from a multivariate t-distribution with a rule for adaptation of its scale matrix. We use the multivariate t-distribution in its hierarchical form, as a scale mixtures of normal distributions. The t -distribution has heavier tails than those of the normal distribution, which increases the ability of the particles to escape from a local optimum. In addition, our approach includes the normal distribution as a particular case. As a consequence, the t -distribution can be applied during the optimization process by maintaining the proper balance between exploration and exploitation. We also propose a simple update rule to adapt the scale matrix associated with a particle. Our strategy consists of adapting the scale matrix of a particle such that the best position found by any particle in its neighborhood is sampled with maximum likelihood in the next iteration. A theoretical analysis was developed to explain how the SMA-BBPSO works, and an empirical study was carried out to evaluate the performance of the proposed algorithm. The experimental results show the suitability of the proposed approach in terms of effectiveness to find good solutions for all benchmark problems investigated. Nonparametric statistical tests indicate that SMA-BBPSO shows a statistically significant improvement compared with other swarm algorithms.

Support vector machine with parameter optimization by a novel hybrid method and its application to fault diagnosis

The performance of support vector machine (SVM) heavily depends on its parameters. The parameter optimization for SVM is still an ongoing research issue. The current parameter optimization methods either are easy to fall into local optimal solution, or are time consuming. Moreover, some optimization methods depend also on the choice of parameters for them, provoking thus a vicious circle. In view of this, a new hybrid method is proposed to optimize the parameters of SVM in this paper. It uses the inter-cluster distance in the feature space (ICDF) to determine a small and effective search interval from a larger kernel parameter search space, while a hybrid of the barebones particle swarm optimization and differential evolution (BBDE) is used to search the optimal parameter combination in the new search space. The ICDF shows the degree the classes are separated. The BBDE is a new, almost parameter-free optimization algorithm. Some benchmark datasets are used to evaluate the proposed algorithm. Furthermore, the proposed method is used to diagnose the faults of rolling element bearings. Experiments and engineering application show that the proposed method outperforms other methods both mentioned in this paper and published in other literature. A new hybrid method is used to optimize the parameters of SVM.ICDF measure is used to determine a small yet effective search interval for kernel parameter.An almost parameter-free evolutionary algorithm, BBDE, is used to search the optimal parameters of SVM.The proposed method is tested on several benchmark datasets as well as fault diagnosis for rolling element bearings.Experimental results show the outstanding performance of the proposed method.

Feature selection algorithm based on bare bones particle swarm optimization

Abstract Feature selection is a useful pre-processing technique for solving classification problems. As an almost parameter-free optimization algorithm, the bare bones particle swarm optimization (BPSO) has been applied to the topic of optimization on continuous or integer spaces, but it has not been applied to feature selection problems with binary variables. In this paper, we propose a new method to find optimal feature subset by the BPSO, called the binary BPSO. In this algorithm, a reinforced memory strategy is designed to update the local leaders of particles for avoiding the degradation of outstanding genes in the particles, and a uniform combination is proposed to balance the local exploitation and the global exploration of algorithm. Moreover, the 1-nearest neighbor method is used as a classifier to evaluate the classification accuracy of a particle. Some international standard data sets are selected to evaluate the proposed algorithm. The experiments show that the proposed algorithm is competitive in terms of both classification accuracy and computational performance.

Anticipatory load shedding for line overload alleviation using Teaching learning based optimization (TLBO)

This paper presents a load shedding algorithm for alleviating line overloads employing Teaching learning based optimization (TLBO). The buses are selected for load shed based on the sensitivity of severity index with respect to load shed. Load shed is based on the next interval predicted load which could cause emergency situation from thermal limit consideration. Line flow constraints have not only considered for next predicted interval but in present base case loading conditions also. Optimum load shed at the selected buses have been obtained for 30-bus, 39-bus standard test systems. Further another technique modified version of bare bones particle swarm optimization known as (BBExp) has been used to validate the algorithm for load shedding.

Reliability Optimization of Radial Distribution Systems Employing Differential Evolution and Bare Bones Particle Swarm Optimization

This paper describes a methodology for determination of optimum failure rate and repair time for each section of a radial distribution system. An objective function in terms of reliability indices and their target values is selected. These indices depend mainly on failure rate and repair time of a section present in a distribution network. A cost is associated with the modification of failure rate and repair time. Hence the objective function is optimized subject to failure rate and repair time of each section of the distribution network considering the total budget allocated to achieve the task. The problem has been solved using differential evolution and bare bones particle swarm optimization. The algorithm has been implemented on a sample radial distribution system.

Using BBPSO Algorithm to Estimate the Weibull Parameters with Censored Data

This article proposes the maximum likelihood estimates based on bare bones particle swarm optimization (BBPSO) algorithm for estimating the parameters of Weibull distribution with censored data, which is widely used in lifetime data analysis. This approach can produce more accuracy of the parameter estimation for the Weibull distribution. Additionally, the confidence intervals for the estimators are obtained. The simulation results show that the BB PSO algorithm outperforms the Newton–Raphson method in most cases in terms of bias, root mean square of errors, and coverage rate. Two examples are used to demonstrate the performance of the proposed approach. The results show that the maximum likelihood estimates via BBPSO algorithm perform well for estimating the Weibull parameters with censored data.

A Bare-bones PSO-based Fuzzy Progressive Transductive SVC for Water Quality Assessment

Water quality assessment is very significant to take timely measures for protecting water resources. This paper is to present a hybrid algorithm of fuzzy progressive transductive support vector classifier and bare-bones particle swarm optimization for water quality assessment. Then, we employ bare-bone particle swarm optimization to optimize the parameters of fuzzy progressive transductive support vector classifier, and the optimal parameters of fuzzy progressive transductive support vector classifier are obtained based on bare-bone particle swarm optimization. The experimental results indicate that the evaluation accuracy of bare-bones PSO-based fuzzy progressive transductive support vector classifier is higher than that of fuzzy progressive transductive support vector classifier, support vector classifier or artificial neural networks. Therefore, bare-bones PSO-based fuzzy progressive transductive support vector classifier is very suitable for water quality evaluation.

Hybrid bare-bones PSO for dynamic economic dispatch with valve-point effects

A new genetic operator by integrating the mutation and the crossover operators, called the M+C operator, where a genetic probability pe is used to control the recombination speed among particles. I...

Bare-bones particle swarm optimization with disruption operator

Bare-bones particle swarm optimization (BPSO) is attractive since it is easy to implement and parameter-free. However, it suffers from premature convergence because of quickly losing diversity. To enhance population diversity and speed up convergence rate of BPSO, this paper proposes a novel disruption strategy, originating from astrophysics, to shift the abilities between exploration and exploitation during the search process. We research the distribution and diversity on the proposed disruption operator, and illustrate the position relationship between the original and disrupted position. The proposed Disruption BPSO (DBPSO) has also been evaluated on a set of well-known nonlinear benchmark functions and compared with several variants of BPSO and other evolutionary algorithms (such as DE, ABC, ES and BSA). Experimental results and statistic analysis confirm promising performance of DBPSO with least computation cost in solving major nonlinear functions.

A practical approach for solving multi-objective reliability redundancy allocation problems using extended bare-bones particle swarm optimization

This paper proposes a practical approach, combining bare-bones particle swarm optimization and sensitivity-based clustering for solving multi-objective reliability redundancy allocation problems (RAPs). A two-stage process is performed to identify promising solutions. Specifically, a new bare-bones multi-objective particle swarm optimization algorithm (BBMOPSO) is developed and applied in the first stage to identify a Pareto-optimal set. This algorithm mainly differs from other multi-objective particle swarm optimization algorithms in the parameter-free particle updating strategy, which is especially suitable for handling the complexity and nonlinearity of RAPs. Moreover, by utilizing an approach based on the adaptive grid to update the global particle leaders, a mutation operator to improve the exploration ability and an effective constraint handling strategy, the integrated BBMOPSO algorithm can generate excellent approximation of the true Pareto-optimal front for RAPs. This is followed by a data clustering technique based on difference sensitivity in the second stage to prune the obtained Pareto-optimal set and obtain a small, workable sized set of promising solutions for system implementation. Two illustrative examples are presented to show the feasibility and effectiveness of the proposed approach.

Hybrid bare-bones PSO for dynamic economic dispatch with valve-point effects

This paper presents an efficient hybrid particle swarm optimization algorithm to solve dynamic economic dispatch problems with valve-point effects, by integrating an improved bare-bones particle swarm optimization (BBPSO) with a local searcher called directionally chaotic search (DCS). The improved BBPSO is designed as a basic level search, which can give a good direction to optimal regions, while DCS is used as a fine-tuning operator to locate optimal solution. And an adaptive disturbance factor and a new genetic operator are also incorporated into the improved BBPSO to enhance its search capability. Moreover, a heuristic handing mechanism for constraints is introduced to modify infeasible particles. Finally, the proposed algorithm is applied to the 5-, 10-, 30-unit-test power systems and several numerical functions, and a comparative study is carried out with other existing methods. Results clarify the significance of the proposed algorithm and verify its performance.

On the effectiveness of nature-inspired metaheuristic algorithms for performing phase equilibrium thermodynamic calculations.

The search for reliable and efficient global optimization algorithms for solving phase stability and phase equilibrium problems in applied thermodynamics is an ongoing area of research. In this study, we evaluated and compared the reliability and efficiency of eight selected nature-inspired metaheuristic algorithms for solving difficult phase stability and phase equilibrium problems. These algorithms are the cuckoo search (CS), intelligent firefly (IFA), bat (BA), artificial bee colony (ABC), MAKHA, a hybrid between monkey algorithm and krill herd algorithm, covariance matrix adaptation evolution strategy (CMAES), magnetic charged system search (MCSS), and bare bones particle swarm optimization (BBPSO). The results clearly showed that CS is the most reliable of all methods as it successfully solved all thermodynamic problems tested in this study. CS proved to be a promising nature-inspired optimization method to perform applied thermodynamic calculations for process design.

Reliability Performance Optimization of Meshed Electrical Distribution System Considering Customer and Energy based Reliability Indices

This paper describes a methodology for determination of optimum failure rate and repair time for each component of a meshed distribution system. In this paper the reliability indices for a sample meshed network are optimized. An objective function incorporating customer and energy based reliability indices and their target values is formulated. These indices are function of failure rate and repair time of a section of a distribution network. Modification of failure rate and repair time modifies the cost attached to them. Hence the optimization of the objective function is achieved by modifying the failure rate and repair time of each section of the meshed distribution system accounting constraint on budget allocated. The problem has been solved using population based differential evolution and bare bones particle swarm optimization techniques and results have been compared for a sample meshed distribution system.

Parameter identification for solid oxide fuel cells using cooperative barebone particle swarm optimization with hybrid learning

Solid oxide fuel cell (SOFC) has been widely recognized as one of the most promising fuel cells. The SOFC performance is highly influenced by several parameters associated with the internal multi-physicochemical processes. In this work, the optimal modeling strategy is designed to determine the parameters of SOFC using a simple and efficient barebone particle swarm optimization (BPSO) algorithm. The cooperative coevolution strategy is applied to divide the output voltage function into four subfunctions based on the interdependence among variables. To the nonlinear characteristic of SOFC model, a hybrid learning strategy is proposed for BPSO to ensure a good balance between exploration and exploitation. The experimental results illustrate the effectiveness of the proposed algorithm. The comparisons also indicate that cooperative coevolution strategy and hybrid learning improve the performance of original PSO algorithm, offering better approximation effect and stronger robustness.

Multiobjective Optimization of a Benfield HiPure Gas Sweetening Unit

We show how a multiobjective bare-bones particle swarm optimization can be used for a process parameter tuning and performance enhancement of a natural gas sweetening unit. This has been made through maximization of hydrocarbon recovery and minimization of the total energy of the process as the two objectives of the optimization. A trade-off exists between these two objectives as illustrated by the Pareto front. This algorithm has been applied to a sweetening unit that uses the Benfield HiPure process. Detailed models of the natural gas unit are developed in ProMax process simulator and integrated to the multi-objective optimization developed in visual basic environment (VBA). In this study, the solvent circulation rates, stripper pressure and reboiler duties are considered as the decision variables while hydrogen sulfide and carbon dioxide concentrations in the sweetened gas are considered as process constraints. The upper and lower bounds of the decision variables are obtained through a parametric sensitivity analysis of the models. The Pareto sets show a significant improvement in hydrocarbon recovery and a decent reduction in the heat consumption of the process.