Brain Storm Optimization Research Articles

A multi-objective brain storm optimization for integrated distributed flexible job shop and distribution problems

Production and distribution are critical components of the furniture supply chain, and achieving optimal performance through their integration has become a vital focus for both the academic and business communities. Moreover, as economic globalization progresses, distributed manufacturing has become a pioneering production technique. Via leveraging a distributed flexible manufacturing system, mass flexible production at lower costs can be achieved. To this end, this study presents an integrated distributed flexible job shop and distribution problem to minimize makespan and total tardiness. In our research, a set of custom furniture orders from different customers are processed among flexible job shops and then delivered by vehicles to customers as the due date. To distinctly show the presented problem, a mixed integer mathematical programming model is created, and a multi-objective brain storm optimization method is introduced considering the problem's features. In comparison to the other three advanced methods, the superiority of the algorithm created is showcased. The findings of the experiments demonstrate that the constructed model and the introduced algorithm have remarkable competitiveness in addressing the problem being examined.

Enhanced nonlinear active noise control: A novel approach using brain storm optimization algorithm

Active Noise Control (ANC) systems play a crucial role in reducing unwanted noise in various settings. Traditional ANC methods, like the Filtered-x Least Mean Squares (FxLMS) algorithm, are effective in linear noise scenarios. However, they often struggle with more nonlinear and complex noise patterns. This paper introduces a novel approach using the brain storm optimization (BSO) algorithm in nonlinear ANC systems, which represents a significant departure from conventional techniques. The BSO algorithm, inspired by human brainstorming processes, excels in addressing the complexities of nonlinear noise by incorporating principles, such as delayed evaluation, free imagination, quantity and quality, and comprehensive improvement. By combining the BSO algorithm with an Extended Kalman Filter (EKF), a new ANC system is proposed that can adapt to a wide range of noise types with improved speed and accuracy. Experimental results showcase the superior performance of the BSO algorithm, achieving an impressive noise reduction of up to 48 dB (dB) in a 500Hz sinusoidal noise scenario, with a convergence time as fast as 0.01 s, outperforming the FxLMS algorithm by a significant margin. Moreover, in complex environments with multi-frequency and random noise, the BSO algorithm consistently demonstrates better noise reduction and quicker convergence, reducing noise levels by up to 27 dB within 0.001 s. The innovative use of the BSO algorithm in ANC systems not only enhances noise reduction capabilities, especially for nonlinear and complex noise signals, but also improves convergence times, paving the way for future advancements in ANC technologies.

Integrated scheduling of multi-constraint open shop and vehicle routing: Mathematical model and learning-driven brain storm optimization algorithm

Recent years have witnessed a surge of interest in integrated production and distribution scheduling problems which can achieve an overall optimization of the production and distribution activities. However, integrated scheduling of open shop and distribution receives rare attention in existing studies. This work proposes an integrated scheduling problem of multi-constraint open shop and vehicle routing to minimize maximum completion time, where group and transportation operations are considered together in the production process. All jobs are divided into multiple groups, and then handled in an open shop with multiple machines. Subsequently, the jobs are delivered to their corresponding customers. First, a mixed integer programming model is formulated to define the problem. Second, a Q-learning-driven brain storm optimization algorithm is developed to address the formulated model. A Q-learning method is employed to choose search strategies for generating new individuals rather than using fixed probability parameters straightforwardly as basic brain storm optimizers. In addition, the solution encoding, heuristic decoding, population initialization, clustering, new individual generation and selection methods are specially devised in consideration of problem-specific knowledge. At last, the developed model and algorithm are verified by addressing a set of benchmark instances, and comparison experiments are conducted with an exact solver CPLEX and four meta-heuristics from existing literature. The results validate the competitive advantages of the formulated model and algorithm in solving the considered problems.

Solving dynamic multimodal optimization problems via a niching-based brain storm optimization with two archives algorithm

Dynamic and multimodal properties are simultaneously possessed in the dynamic multimodal optimization problems (DMMOPs), which aim to find multiple optimal solutions in a dynamic environment. However, more work still needs to be devoted to solving DMMOPs, which still require significant attention. A niching-based brain storm optimization with two archives (NBSO2A) algorithm is proposed to solve DMMOPs. The two niching methods, i.e., neighborhood-based speciation (NS), and nearest-better clustering (NBC), are incorporated into a BSO algorithm to generate new solutions. The two archives preserve the optimal solutions that meet the requirements and practical, inferior solutions discarded during the generation. Improved taboo area (ITA) removes highly similar individuals from the population. An evolution strategy with covariance matrix adaptation (CMA-ES) is adopted to enhance the local search ability and improve the quality of the solutions. The NBSO2A algorithm and four other algorithms were tested on 12 benchmark problems to validate the performance of the NBSO2A algorithm on DMMOPs. The experimental results show that the NBSO2A algorithm outperforms the other compared algorithms on most tested benchmark problems.

Multiobjective Scheduling of Energy-Efficient Stochastic Hybrid Open Shop With Brain Storm Optimization and Simulation Evaluation

Multiobjective Scheduling of Energy-Efficient Stochastic Hybrid Open Shop With Brain Storm Optimization and Simulation Evaluation

Past five years on strategies and applications in hybrid brain storm optimization algorithms: a review

Abstract Optimization, in general, is regarded as the process of finding optimal values for the variables of a given problem in order to minimize or maximize one or more objective function(s). Brain storm optimization (BSO) algorithm solves a complex optimization problem by mimicking the human idea generating process, in which a group of people solves a problem together. The aim of this paper is to present hybrid BSO algorithm solutions in the past 5 years. This study could be divided into two parts: strategies and applications. In the first part, different strategies for the hybrid BSO algorithms intended to improve the various ability of the original BSO algorithm are displayed. In the second part, the real-world applications in the past five years in optimization, prediction and feature selection processes are presented.

A Symmetrical 32 × 1 and 16 × 1 Elements Antenna Array Failure Correction with Nulls Using Brain Storm Optimization

A Symmetrical 32 × 1 and 16 × 1 Elements Antenna Array Failure Correction with Nulls Using Brain Storm Optimization

Generating a Multi-Day Travel Itinerary Recommendation Using the Hybrid Ant Colony System and Brainstorm Optimization Algorithm

Generating a Multi-Day Travel Itinerary Recommendation Using the Hybrid Ant Colony System and Brainstorm Optimization Algorithm

Improved Brain Storm Optimization Algorithm Based on Flock Decision Mutation Strategy

To tackle the problem of the brain storm optimization (BSO) algorithm’s suboptimal capability for avoiding local optima, which contributes to its inadequate optimization precision, we developed a flock decision mutation approach that substantially enhances the efficacy of the BSO algorithm. Furthermore, to solve the problem of insufficient BSO algorithm population diversity, we introduced a strategy that utilizes the good point set to enhance the initial population’s quality. Simultaneously, we substituted the K-means clustering approach with spectral clustering to improve the clustering accuracy of the algorithm. This work introduced an enhanced version of the brain storm optimization algorithm founded on a flock decision mutation strategy (FDIBSO). The improved algorithm was compared against contemporary leading algorithms through the CEC2018. The experimental section additionally employs the AUV intelligence evaluation as an application case. It addresses the combined weight model under various dimensional settings to substantiate the efficacy of the FDIBSO algorithm further. The findings indicate that FDIBSO surpasses BSO and other enhanced algorithms for addressing intricate optimization challenges.

Global-best brain storm optimization algorithm based on chaotic difference step and opposition-based learning

Recently, the following global-best strategy and discussion mechanism have been prevailing to solve the slow convergence and the low optimization accuracy in the brain storm optimization (BSO) algorithm. However, the traditional BSO algorithm also suffers from the problem that it is easy to fall into local optimum. Therefore, this work innovatively designed the chaotic difference step strategy. This strategy introduced four commonly used chaotic maps and difference step to expand the population search space to improve the situation. Moreover, opposition-based learning thought was innovatively adopted into the BSO algorithm. The thought aims to generate the opposition-based population, increase the search density, and make the algorithm out of the local optimum as soon as possible. In summary, this work proposed a global-best brain storm optimization algorithm based on the chaotic difference step and opposition-based learning (COGBSO). According to the CEC2013 benchmark test suit, 15 typical benchmark functions were selected, and multiple sets of simulation experiments were conducted on MATLAB. The COGBSO algorithm was also compared to recent competitive algorithms based on the complete CEC2018 benchmark test suit. The results demonstrate that the COGBSO outperforms BSO and other improved algorithms in solving complex optimization problems.

An ensemble of brain storm optimization and Q-learning methods for distributed flexible job shop scheduling problems with distribution operations

Distributed manufacturing scheduling problems have attracted much concern from both industrial and academic areas. Nevertheless, distributed scheduling problems with distribution operations are seldom studied. This work proposes a distributed flexible job shop scheduling problem with distribution operations. A set of jobs is handled at distributed flexible job shops, and then the finished jobs are transported to their corresponding customers following given due dates. First, a mixed integer programming model is established to minimize total tardiness. Second, an ensemble of brain storm optimization and Q-learning methods is developed to solve the formulated model. Six heuristics are hybridized to generate a high-quality initial population. A Q-learning method is devised by fully employing found search information to guide subsequent search processes instead of using fixed parameters as basic brain storm optimization. A variable neighborhood search method combining problem-specific knowledge is designed to further refine the found best individual. At last, the formulated model and method are compared with three state-of-the-art metaheuristics and a mathematical programming solver CPLEX via using a group of problem instances. The results and analysis demonstrate that the developed model and algorithm have more powerful competitiveness in addressing the studied problem.

Running-Time Analysis of Brain Storm Optimization Based on Average Gain Model.

The brain storm optimization (BSO) algorithm has received increased attention in the field of evolutionary computation. While BSO has been applied in numerous industrial scenarios due to its effectiveness and accessibility, there are few theoretical analysis results about its running time. Running-time analysis can be conducted through the estimation of the upper bounds of the expected first hitting time to evaluate the efficiency of BSO. This study estimates the upper bounds of the expected first hitting time on six single individual BSO variants (BSOs with one individual) based on the average gain model. The theoretical analysis indicates the following results. (1) The time complexity of the six BSO variants is O(n) in equal coefficient linear functions regardless of the presence or absence of the disrupting operator, where n is the number of the dimensions. Moreover, the coefficient of the upper bounds on the expected first hitting time shows that the single individual BSOs with the disrupting operator require fewer iterations to obtain the target solution than the single individual BSOs without the disrupting operator. (2) The upper bounds on the expected first hitting time of single individual BSOs with the standard normally distributed mutation operator are lower than those of BSOs with the uniformly distributed mutation operator. (3) The upper bounds on the expected first hitting time of single individual BSOs with the U-12,12 mutation operator are approximately twice those of BSOs with the U(-1,1) mutation operator. The corresponding numerical results are also consistent with the theoretical analysis results.

An Improved Multi-Objective Brain Storm Optimization Algorithm for Hybrid Microgrid Dispatch

The increasing integration of renewable energy sources into microgrids has led to challenges in achieving daily optimal scheduling for hybrid alternating current/direct current microgrids (HMGs). To solve the problem, this article presents a novel hybrid AC/DC microgrid scheduling method based on an improved brain storm optimization (BSO) algorithm. Firstly, with economic and energy storage device health as the primary objective functions, this paper proposes a dispatch model for AC-DC hybrid microgrids. To overcome the limitations of traditional algorithms, including premature convergence and can only find non-inferior solution sets, this article proposes a multi-objective BSO algorithm that integrates learning and selection strategies. Additionally, a fuzzy decision-making method is employed to achieve optimal daily dispatching and select the most suitable compromise solution. Finally, experiments are conducted to verify the effectiveness of the proposed multi-objective optimal scheduling method and to demonstrate the practicality and effectiveness of the method in real application scenarios.

Planar antenna array synthesis for wireless power transmission based on brain storm optimization algorithm

AbstractThis study investigates the synthesis of a two‐dimensional transmitting planar antenna array for a wireless power transmission system. The antenna synthesis problem is transformed into an optimization problem under multiple constraints. Accordingly, a brainstorming optimization algorithm is utilized to optimize the element position of the planar antenna arrays under the constraints of aperture size, array spacing, and side lobe level. A numerical simulation of the beam collection efficiency is conducted under different receiving region shapes. The simulation results confirm the effectiveness of the proposed method in optimizing the two‐dimensional transmitting planar antenna array for wireless power transmission systems.

A Sparse Model for Electricity Price Forecasting with LASSO-GRBFN and Brain Storm Optimization

This paper proposes a new method for predicting electricity prices. The method is based on the Generalized Radial Basis Function Network (GRBFN), an extension of the RBFN of Artificial Neural Network (ANN) used for one-step-ahead forecasting. The GRBFN is superior to the RBFN because it evaluates the Gaussian function parameters during learning. Conventional ANN methods consider overfitting with the weight decay method, based on the L2 norm of weights between neurons, but there is still room for improvement. Therefore, the paper proposes using the Least Absolute Shrinkage and Selection Operator (LASSO) to improve the model’s performance. Furthermore, the paper presents the Brain Storm Optimization (BSO) of evolutionary computation to evaluate the cost function with the term of the L1 norm. Conventional methods with the gradient are not suitable for the L1 norm. The proposed method is successfully applied to actual data of ISO New England in the USA.

A Discrete Predator-Prey Brain Storm Optimization Technique for Optimal Allocation of Micro-PMUs in Distribution System State Estimation

This paper proposes a practical method for determining the optimal allocation of micro-Phasor Measurement Units (μPMUs) for distribution system state estimation (DSSE). The proposed method formulates the state estimation based on the nest structure of DistFlow, which is used for power flow calculation in distribution systems due to the redundancy of less than 1. The use of μPMUs can significantly improve the accuracy of estimates. However, Distribution System Operators (DSOs) need to consider optimization constraints before deciding on the optimal location of μPMUs. To tackle this issue, this paper proposes the use of DPPBSO (Discrete Predator-Prey Brain Storm Optimization) of Evolutionary Computation (EC) to optimize the location of μPMUs. PPBSO is an extension method that applies the Predator-Prey strategy to Brain Storm Optimization (BSO), and DPPSO is the discrete version of PPBSO used in solving combinatorial optimization problems. The Predator-Prey strategy is critical in improving the solution candidates by intensifying and diversifying solution searches in EC. Simulation results demonstrate the effectiveness of the proposed method in the IEEE 69-node distribution system.

Novel Hybrid Particle Swarm and Brainstorm Optimization: Electromagnetic applications with enhanced performance

Novel Hybrid Particle Swarm and Brainstorm Optimization: Electromagnetic applications with enhanced performance

Oppositional Brain Storm Optimization With Deep Learning Based Facial Emotion Recognition for Autonomous Intelligent Systems

Oppositional Brain Storm Optimization With Deep Learning Based Facial Emotion Recognition for Autonomous Intelligent Systems

Solving multimodal optimization problems by a knowledge-driven brain storm optimization algorithm

Multimodal optimization problem (MMOP) refers to the problem having more than one optima or satisfied solution in the decision space. The accuracy and diversity of solutions should be considered when solving MMOPs. In the brain storm optimization (BSO) algorithm, the information on current solutions is analyzed, but the information on previous solutions needs to be more effectively used to guide the search. A knowledge-driven BSO in objective space (KBSOOS) algorithm is proposed to enhance the search performance and to maintain the diversity of the solutions for solving MMOPs. In addition, a diversity indicator is proposed as a quantitative measurement to measure the performance of various algorithms for solving MMOPs. The 30 nonlinear equation system (NES) problems are modeled as MMOPs and solved by six swarm intelligence algorithms to validate the proposed algorithm’s performance. Based on the experimental results, the diversity indicator could give a good indication of the performance of algorithms, and the KBSOOS algorithm could enhance the performance of various BSO algorithms.

Improved multi-objective brain storm optimization algorithm for RFID network planning

Improved multi-objective brain storm optimization algorithm for RFID network planning