Bare Bones Particle Swarm Optimization Research Articles

A Bi-Objective Optimal Scheduling Method for the Charging and Discharging of EVs Considering the Uncertainty of Wind and Photovoltaic Output in the Context of Time-of-Use Electricity Price

With the increasing share of renewable energy generation and the integration of large-scale electric vehicles (EVs) into the grid, the reasonable charging and discharging scheduling of electric vehicles is essential for the stable operation of power grid. Therefore, this paper proposes a bi-objective optimal scheduling strategy for microgrids based on the participation of electric vehicles in vehicle-to-grid technology (V2G) mode. Firstly, the system structure for electric vehicles participating in the charging and discharging schedule was established. Secondly, a bi-objective optimization model was formulated, considering load mean square error and user charging cost. A heuristic method was employed to handle constraints related to system energy balance and equipment output. Then, the Monte Carlo method was employed to simulate electric vehicle loads and to facilitate the generation of and reduction in scenario scenes. Finally, the model was solved using an improved multi-objective barebones particle swarm optimization algorithm. The simulation results show that the proposed scheduling strategy has a lower charging cost (CNY 11,032.4) and lower load mean square error (12.84 × 105 kW2) than the strategy employed in the comparison experiment, which ensures the economic and stable operation of the microgrid.

A rhinopithecus swarm optimization algorithm for complex optimization problem

This paper introduces a novel meta-heuristic algorithm named Rhinopithecus Swarm Optimization (RSO) to address optimization problems, particularly those involving high dimensions. The proposed algorithm is inspired by the social behaviors of different groups within the rhinopithecus swarm. RSO categorizes the swarm into mature, adolescent, and infancy individuals. Due to this division of labor, each category of individuals employs unique search methods, including vertical migration, concerted search, and mimicry. To evaluate the effectiveness of RSO, we conducted experiments using the CEC2017 test set and three constrained engineering problems. Each function in the test set was independently executed 36 times. Additionally, we used the Wilcoxon signed-rank test and the Friedman test to analyze the performance of RSO compared to eight well-known optimization algorithms: Dung Beetle Optimizer (DBO), Beluga Whale Optimization (BWO), Salp Swarm Algorithm (SSA), African Vultures Optimization Algorithm (AVOA), Whale Optimization Algorithm (WOA), Atomic Retrospective Learning Bare Bone Particle Swarm Optimization (ARBBPSO), Artificial Gorilla Troops Optimizer (GTO), and Harris Hawks Optimization (HHO). The results indicate that RSO exhibited outstanding performance on the CEC2017 test set for both 30 and 100 dimension. Moreover, RSO ranked first in both dimensions, surpassing the mean rank of the second-ranked algorithms by 7.69% and 42.85%, respectively. Across the three classical engineering design problems, RSO consistently achieves the best results. Overall, it can be concluded that RSO is particularly effective for solving high-dimensional optimization problems.

A self-training method based on fast binary bare-bones particle swarm optimization for semi-supervised classification

The generalization performance of self-training methods for semi-supervised classification depends on the selection of pseudo-labeled samples. While multiple variations of self-training methods are developed, strategies for selecting pseudo-labeled samples still suffer from the following challenging issues: a) heavily relying on specific assumptions about geometric and class relationships between different instances; b) parameter dependency; c) failure to effectively eliminate mislabeled pseudo-labeled noisy samples. These issues lead to low robustness and performance deterioration. To select pseudo-labeled samples more effectively and overcome the above-mentioned challenging issues, a self-training method based on fast binary bare-bones particle swarm optimization (ST-F3BPSO) is proposed. The core ideas of ST-F3BPSO are that a) a binary bare-bones particle swarm optimization (F3BPSO) with a new search space reduction strategy, a new particle update mechanism, and a new fitness function is first proposed; b) an F3BPSO-based sample sub-space optimization (SSO–F3BPSO) is second proposed to help ST-F3BPSO select pseudo-labeled samples and eliminate mislabeled pseudo-labeled noisy samples in the iterative self-training process without parameters and assumptions about geometric and class relationships of data. Intensive experiments on benchmark data sets have proven that a) ST-F3BPSO outperforms 7 state-of-the-art non-evolutionary algorithms-based self-training methods and 2 state-of-the-art evolutionary algorithms-based self-training methods in classification accuracy and marco F-measure of 2 popular base classifiers with various ratios of the initial labeled data or the class noise; b) ST-F3BPSO outperforms 2 state-of-the-art evolutionary algorithms-based self-training methods in computational efficiency.

Optimal design of automatic generation control based on BBPSO-tuned PI for a restructured environment

ABSTRACT This paper intends to model an AGC regulator for a restructured environment using Bare Bone Particle Swarm Optimization (BBPSO). The gain-controlled Proportional–Integral (PI) Controller is designed here to enhance the performance of the BBPSO algorithm along with Gaussian distribution. The practical difficulty in handling the area control error to zero is the sudden variations in load. In practice, the tremendous contribution of deregulation in the power sector causes volatility in frequency and tie-line power deviations. To ensure the robustness of the proposed controller, three different cases of power system transactions have been considered. The performance has been validated by comparing it with Real Coded Genetic Algorithm-tuned PI controller (RCGA-PI) and Differential Evolutionary Algorithm-tuned PI controller (DE-PI) for the five area Thermal-Thermal generation test system. Moreover, the dynamic performance of an extensive range of demands and disturbances of all areas like settling time and overshoot against parametric precariousness has been done on the proposed test system.

Spectral reflectance reconstruction based on wideband multi-illuminant imaging and a modified particle swarm optimization algorithm.

A method for spectral reflectance factor reconstruction based on wideband multi-illuminant imaging was proposed, using a programmable LED lighting system and modified Bare Bones Particle Swarm Optimization algorithms. From a set of 16 LEDs with different spectral power distributions, nine light sources with correlated color temperatures in the range of 1924 K - 15746 K, most of them daylight simulators, were generated. Samples from three color charts (X-Rite ColorChecker Digital SG, SCOCIE ScoColor paint chart, and SCOCIE ScoColor textile chart), were captured by a color industrial camera under the nine light sources, and used in sequence as training and/or testing colors. The spectral reconstruction models achieved under multi-illuminant imaging were trained and tested using the canonical Bare Bones Particle Swarm Optimization and its proposed modifications, along with six additional and commonly used algorithms. The impacts of different illuminants, illuminant combinations, algorithms, and training colors on reconstruction accuracy were studied comprehensively. The results indicated that training colors covering larger regions of color space give more accurate reconstructions of spectral reflectance factors, and combinations of two illuminants with a large difference of correlated color temperature achieve more than twice the accuracy of that under a single illuminant. Specifically, the average reconstruction error by the method proposed in this paper for patches from two color charts under A + D90 light sources was 0.94 and 1.08 CIEDE2000 color difference units. The results of the experiment also confirmed that some reconstruction algorithms are unsuitable for predicting spectral reflectance factors from multi-illuminant images due to the complexity of optimization problems and insufficient accuracy. The proposed reconstruction method has many advantages, such as being simple in operation, with no requirement of prior knowledge, and easy to implement in non-contact color measurement and color reproduction devices.

Response strategies for coping with imperfect items of a joint replenishment model with the adaptive bare-bone differential evolution

In joint replenishment problem with random number of imperfect items (IJRP), it is a great challenge for managers to correctly ascertain the number of imperfect items and take the right response strategy to make rectifications simultaneously. In this paper, the effectiveness of four common response strategies, i.e., scrap, all-unit quantity discount, rework & repair, and buy replacements for coping with imperfect items are investigated based on IJRP for the first time, respectively. Considering the NP-hard nature of the classical JRP and its extensions, as well as enhancing the explorable performance in crossing scale-levels of IJRP models and exploitable performance in diversifying the solution population of bare-bones differential evolutionary (BBDE), a meta-heuristic algorithm named as the adaptive BBDE (ABBDE) is designed by introducing an problem scale related adaptive recombination probability and a generalized opposition-based learning (OBL). Through numerical experiments, it has been found that (1) The adaptive improvements of ABBDE have been verified through obtaining the lower best-found total cost (TC), the lowest mean value, and the smallest standard error comparing to that of genetic evolution algorithm (GA), particle swarm optimization (PSO), differential evolution (DE), bare-bones PSO (BBPSO), BBDE. (2) Parameter sensitivity analyses indicate that the scale of yearly demand, lead-time, and significance of uncertainty have significant impacts on system cost, whilst the parameters pertinent to specific response strategy are main factors causing system cost disturbance. (3) Management insights and limitations on adoption of a right response strategy and the practical usage of ABBDE are obtained.

Improved Bare Bones Particle Swarm Optimization for DNA Sequence Design.

DNA computing has efficient computational power, but requires high requirements on the DNA sequences used for coding, and reliable DNA sequences can effectively improve the quality of DNA encoding. And designing reliable DNA sequences is an NP problem, because it requires finding DNA sequences that satisfy multiple sets of conflicting constraints from a large solution space. To better solve the DNA sequence design problem, we propose an improved bare bones particle swarm optimization algorithm (IBPSO). The algorithm uses dynamic lensing opposition-based learning to initialize the population to improve population diversity and enhance the ability of the algorithm to jump out of local optima; An evolutionary strategy based on signal-to-noise ratio(SNR) distance is designed to balance the exploration and exploitation of the algorithm; Then an invasive weed optimization algorithm with niche crowding(NCIWO) is used to eliminate low-quality solutions and improve the search efficiency of the algorithm. In addition, we introduce the triplet-bases unpaired constraint to further improve the quality of DNA sequences. Finally, the effectiveness of the improved strategy is demonstrated by ablation experiments; and the DNA sequences designed by our algorithm are of higher quality compared with those generated by the four advanced algorithms.

Enhanced bare-bones particle swarm optimization based evolving deep neural networks

In this research, we propose a variant of the Bare-Bones Particle Swarm Optimization (BBPSO) algorithm for hyper-parameter selection and deep architecture generation for image, audio and video classification tasks. Since the search process of the original BBPSO model is guided by a single leader and the particles’ personal best experiences, there is a lack of interactions pertaining to the neighbouring elite solutions. To overcome this limitation, we propose a versatile search process for a modified BBPSO model that incorporates a number of effective components and operations. These include the neighbouring and global best signals, search actions with Cauchy/Levy scale factors, sub-dimension operations guided by the local and global elite solutions, and a Levy-driven local search mechanism. Moreover, root-finding algorithms are employed which use informative mathematical principles to estimate new root offspring for leader/particle enhancement. A reinforcement learning algorithm is subsequently used to identify the optimal sequential deployment of these numerical analysis methods to increase robustness. Several medical imaging data sets, i.e., ISIC 2017, PH2 and Dermofit skin lesion databases, the ALL-IDB2 microscopic blood image data set, the MURA musculoskeletal radiographic database, the CK + facial expression data set, as well as the Coswara respiratory audio data set and UCF101 video action data set, are employed for evaluation. The proposed BBPSO-optimized Convolutional Neural Network (CNN), bidirectional Long Short-Term Memory (BiLSTM) with attention mechanism, and CNN-BiLSTM models outperform those devised by other PSO and BBPSO variants, as well as state-of-the-art existing studies, significantly, for image, audio respiratory abnormality and realistic video action recognition.

A deep memory bare-bones particle swarm optimization algorithm for single-objective optimization problems.

A deep memory bare-bones particle swarm optimization algorithm (DMBBPSO) for single-objective optimization problems is proposed in this paper. The DMBBPSO is able to perform high-precision local search while maintaining a large global search, thus providing a reliable solution to high-dimensional complex optimization problems. Normally, maintaining high accuracy while conducting global searches is an important challenge for single-objective optimizers. Traditional particle swarms optimizers can rapidly lose the diversity during iterations and are unable to perform global searches efficiently, and thus are more likely to be trapped by local optima. To address this problem, the DMBBPSO combines multiple memory storage mechanism (MMSM) and a layer-by-layer activation strategy (LAS). The MMSM catalyzes a set of deep memories to increase the diversity of the particle swarm. For every single particle, both of the personal best position and deep memories will be used in the evaluation process. The LAS enables the particle swarm to avoid premature convergence while enhancing local search capabilities. The collaboration between MMSM and LAS enhances the diversity of the particle swarm, which in turn enhances the robustness of the DMBBPSO. To investigate the optimization ability of the DMBBPSO for single-objective optimization problems, The CEC2017 benchmark functions are used in experiments. Five state-of-the-art evolutionary algorithms are used in the control group. Finally, experimental results demonstrate that the DMBBPSO can provide high precision results for single-objective optimization problems.

Radiation shielding optimization design research based on bare-bones particle swarm optimization algorithm

In order to further meet the requirements of weight, volume, and dose minimization for new nuclear energy devices, the bare-bones multi-objective particle swarm optimization algorithm is used to automatically and iteratively optimize the design parameters of radiation shielding system material, thickness, and structure. The radiation shielding optimization program based on the bare-bones particle swarm optimization algorithm is developed and coupled into the reactor radiation shielding multi-objective intelligent optimization platform, and the code is verified by using the Savannah benchmark model. The material type and thickness of Savannah model were optimized by using the BBMOPSO algorithm to call the dose calculation code, the integrated optimized data showed that the weight decreased by 78.77%, the volume decreased by 23.10% and the dose rate decreased by 72.41% compared with the initial solution. The results show that the method can get the best radiation shielding solution that meets a lot of different goals. This shows that the method is both effective and feasible, and it makes up for the lack of manual optimization.

A Bare-Bones Particle Swarm Optimization With Crossed Memory for Global Optimization

The offspring selection strategy is the core of evolutionary algorithms, which directly affects the method’s accuracy. Normally, to improve the search accuracy in local areas, the population converges quickly around the optimal individual. However, excessive aggregation can narrow the search range of the population, and thus the population may be trapped by local optima. To overcome this problem, a bare-bones particle swarm optimization with crossed memory (BPSO-CM) is proposed in this work. The BPSO-CM contains a multi-memory storage mechanism (MSM) and an elite offspring selection strategy (EOSS). The MSM enables an extra storage space to extend the search ability of the particle swarm and the EOSS enhances the local minimum escaping ability of the particle swarm. The population is endowed with the ability of enhanced global search through the cooperation of the MSM and the EOSS. To verify the performance of the BPSO-CM, the CEC2017 benchmark functions are used in experiments, five population-based methods are selected in the control group. Finally, experimental results proved that the BPSO-CM can present highly accurate results for global optimization problems.

A Twinning Memory Bare-Bones Particle Swarm Optimization Algorithm for No-Linear Functions

Been trapped by local minimums is an important problem in no-linear optimization problems, which is blocking evolutionary algorithms to find the global optimum. Normally, to increase the optimization accuracy, evolutionary algorithms implement search around the best individual. However, overuse of information from a single individual can lead to a rapid diversity losing of the population, and thus reduce the search ability. To overcome this problem, a twinning memory bare-bones particle swarm optimization (TMBPSO) algorithm is presented in this work. The TMBPSO contains a twining memory storage mechanism (TMSM) and a multiple memory retrieval strategy (MMRS). The TMSM enables an extra storage space to extend the search ability of the particle swarm and the MMRS enhances the local minimum escaping ability of the particle swarm. The particle swarm is endowed with the ability of self-rectification by the cooperation of the TMSM and the MMRS. To verify the search ability of the TMBPSO, the CEC2017 benchmark functions and five state-of-the-art population-based optimization algorithms are selected in experiments. Finally, experimental results confirmed that the TMBPSO can obtain high accurate results for no-linear functions.

Multi-Objective Intelligent Decision and Linkage Control Algorithm for Mine Ventilation

A novel bare-bones particle swarm optimization (BBPSO) algorithm is proposed to realize intelligent mine ventilation decision-making and overcome the problems of low precision, low speed, and difficulty in converging on an optimal global solution. The proposed method determines the decision objective function based on the minimal power consumption and maximal air demand. Three penalty terms, namely, dynamic ventilation condition, the supplied air volume at the location where the air is required, and roadway wind speed, are established. The particle construction method of “wind resistance” instead of “wind resistance & air volume” is proposed to reduce the calculation dimension effectively. Three optimization strategies, namely the contraction factor, optimal initial value, and elastic mirror image, are proposed to avoid premature convergence of the algorithm. The application flow of intelligent decision-making in the field and the parallel computing architecture are also discussed. Five methods are used to solve the problems. The results reveal that the improved parallel BBPSO algorithm (BBPSO-Para-Improved) outperforms other algorithms in terms of convergence efficiency, convergence time, and global optimization performance and meets the requirements of large ventilation systems for achieving economic and safety targets.

An electronic transition-based bare bones particle swarm optimization algorithm for high dimensional optimization problems.

An electronic transition-based bare bones particle swarm optimization (ETBBPSO) algorithm is proposed in this paper. The ETBBPSO is designed to present high precision results for high dimensional single-objective optimization problems. Particles in the ETBBPSO are divided into different orbits. A transition operator is proposed to enhance the global search ability of ETBBPSO. The transition behavior of particles gives the swarm more chance to escape from local minimums. In addition, an orbit merge operator is proposed in this paper. An orbit with low search ability will be merged by an orbit with high search ability. Extensive experiments with CEC2014 and CEC2020 are evaluated with ETBBPSO. Four famous population-based algorithms are also selected in the control group. Experimental results prove that ETBBPSO can present high precision results for high dimensional single-objective optimization problems.

A twinning bare bones particle swarm optimization algorithm.

A twinning bare bones particle swarm optimization(TBBPSO) algorithm is proposed in this paper. The TBBPSO is combined by two operators, the twins grouping operator (TGO) and the merger operator (MO). The TGO aims at the reorganization of the particle swarm. Two particles will form as a twin and influence each other in subsequent iterations. In a twin, one particle is designed to do the global search while the other one is designed to do the local search. The MO aims at merging the twins and enhancing the search ability of the main group. Two operators work together to enhance the local minimum escaping ability of proposed methods. In addition, no parameter adjustment is needed in TBBPSO, which means TBBPSO can solve different types of optimization problems without previous information or parameter adjustment. In the benchmark functions test, the CEC2014 benchmark functions are used. Experimental results prove that proposed methods can present high precision results for various types of optimization problems.

A Low-Complexity Codebook Optimization Scheme for Sparse Code Multiple Access

Sparse code multiple access (SCMA) is a promising non-orthogonal multiple access technique to support massive connectivity for future wireless Internet of Things (IoT) networks. As the main feature of SCMA, modulation and spread spectrum is embedded into codebook mapping, offering significant codebook shaping gains to mitigate inter-cell interference. Maximizing the constellation-constrained average mutual information (AMI) is an effective way for SCMA codebook optimization. However, deriving a closed-form expression of the AMI is analytically intractable, while it is computationally costly to estimate the AMI by numerical methods. To address this challenge, this paper first derives a lower bound of the AMI with a closed-form expression. On this basis, we propose a novel codebook optimization method referred to as joint bare bones particle swarm optimization (JBBPSO) through maximizing the AMI lower bound. The proposed low-complexity method jointly optimizes the mother codebook including basic constellation and other non-zero-dimensional constellations, and the rotation angles of multiple users. Numerical results show that our proposed optimized codebooks outperform the state-of-the-art SCMA codebooks in terms of both the lower bound and the error performance.

An Empirical Study of Cluster-Based MOEA/D Bare Bones PSO for Data Clustering †

Previously, cluster-based multi or many objective function techniques were proposed to reduce the Pareto set. Recently, researchers proposed such techniques to find better solutions in the objective space to solve engineering problems. In this work, we applied a cluster-based approach for solution selection in a multiobjective evolutionary algorithm based on decomposition with bare bones particle swarm optimization for data clustering and investigated its clustering performance. In our previous work, we found that MOEA/D with BBPSO performed the best on 10 datasets. Here, we extend this work applying a cluster-based approach tested on 13 UCI datasets. We compared with six multiobjective evolutionary clustering algorithms from the existing literature and ten from our previous work. The proposed technique was found to perform well on datasets highly overlapping clusters, such as CMC and Sonar. So far, we found only one work that used cluster-based MOEA for clustering data, the hierarchical topology multiobjective clustering algorithm. All other cluster-based MOEA found were used to solve other problems that are not data clustering problems. By clustering Pareto solutions and evaluating new candidates against the found cluster representatives, local search is introduced in the solution selection process within the objective space, which can be effective on datasets with highly overlapping clusters. This is an added layer of search control in the objective space. The results are found to be promising, prompting different areas of future research which are discussed, including the study of its effects with an increasing number of clusters as well as with other objective functions.

Coded modulation faster-than-Nyquist transmission with precoder and channel shortening optimization

Faster-than-Nyquist (FTN) signaling can improve the spectrum efficiency (SE) of the transmission system. In this paper, we propose a coded modulation FTN (CM-FTN) transmission scheme with precoder and channel shortening (CS) optimization to improve bit error rate (BER) performance and reduce the complexity of FTN equalizer. In our proposal, the information rate (IR) or spectral efficiency (SE) is employed and verified as a better performance metric for CM-FTN than the minimum Euclidian distance (MED). The precoder of CM-FTN is optimized for maximizing the IR criterion using the bare-bones particle swarm optimization (BB-PSO) algorithm. Further, a three-carrier CM-FTN system model is used to capture the broadening effect of precoder. Also targeting for the IR maximization, the inter-symbol interference (ISI) length for CS is optimized to reduce the receiver complexity without performance loss. Simulation results demonstrate that our method has a 0.6dB precoding gain compared with the non-precoding scheme and a maximum of 87.5% of the complexity of FTN equalizer is reduced without BER loss.

Feature selection using bare-bones particle swarm optimization with mutual information

Feature selection (FS) is an important data processing method in pattern recognition and data mining. Due to not considering characteristics of the FS problem itself, traditional particle update mechanisms and swarm initialization strategies adopted in most particle swarm optimization (PSO) limit their performance on dealing with high-dimensional FS problems. Focused on it, this paper proposes a novel feature selection algorithm based on bare bones PSO (BBPSO) with mutual information. Firstly, an effective swarm initialization strategy based on label correlation is developed, making full use of the correlation between features and class labels to accelerate the convergence of swarm. Then, in order to enhance the exploitation performance of the algorithm, two local search operators, i.e., the supplementary operator and the deletion operator, are developed based on feature relevance-redundancy. Furthermore, an adaptive flip mutation operator is designed to help particles jump out of local optimal solutions. We apply the proposed algorithm to typical datasets based on the K-Nearest Neighbor classifier (K-NN), and compare it with eleven state-of-the-art algorithms, SFS, PTA, SGA, BPSO, PSO(4-2), HPSO-LS, Binary BPSO, NaFA, IBFA, KPLS-mRMR and SMBA-CSFS. The experimental results show that the proposed algorithm can achieve a feature subset with better performance, and is a highly competitive FS algorithm.

Heterogeneous Cooperative Bare-Bones Particle Swarm Optimization with Jump for High-Dimensional Problems

This paper proposes a novel Bare-Bones Particle Swarm Optimization (BBPSO) algorithm for solving high-dimensional problems. BBPSO is a variant of Particle Swarm Optimization (PSO) and is based on a Gaussian distribution. The BBPSO algorithm does not consider the selection of controllable parameters for PSO and is a simple but powerful optimization method. This algorithm, however, is vulnerable to high-dimensional problems, i.e., it easily becomes stuck at local optima and is subject to the “two steps forward, one step backward” phenomenon. This study improves its performance for high-dimensional problems by combining heterogeneous cooperation based on the exchange of information between particles to overcome the “two steps forward, one step backward” phenomenon and a jumping strategy to avoid local optima. The CEC 2010 Special Session on Large-Scale Global Optimization (LSGO) identified 20 benchmark problems that provide convenience and flexibility for comparing various optimization algorithms specifically designed for LSGO. Simulations are performed using these benchmark problems to verify the performance of the proposed optimizer by comparing the results of other variants of the PSO algorithm.