Enhancing the Local Search Ability of the Brain Storm Optimization Algorithm by Covariance Matrix Adaptation

2019 ◽  
pp. 105-122
Author(s):  
Seham Elsayed ◽  
Mohammed El-Abd ◽  
Karam Sallam
Keyword(s):  
Local Search ◽  
Covariance Matrix ◽  
Optimization Algorithm ◽  
Brain Storm Optimization ◽  
Covariance Matrix Adaptation ◽  
The Brain

Parallel hardware implementation of the brain storm optimization algorithm using FPGAs

2020 ◽  
Vol 74 ◽  
pp. 103005 ◽  
Author(s):  
Ahmed Hassanein ◽  
Mohammed El-Abd ◽  
Issam Damaj ◽  
Haseeb Ur Rehman
Keyword(s):  
Optimization Algorithm ◽  
Hardware Implementation ◽  
Brain Storm Optimization ◽  
The Brain

scholarly journals Matrix Adaptation Evolution Strategy with Multi-Objective Optimization for Multimodal Optimization

Algorithms ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 56
Author(s):  
Wei Li
Keyword(s):  
Covariance Matrix ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Population Diversity ◽  
Evolution Strategy ◽  
Multimodal Optimization ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Covariance Matrix Adaptation ◽  
Adaptation Evolution

The standard covariance matrix adaptation evolution strategy (CMA-ES) is highly effective at locating a single global optimum. However, it shows unsatisfactory performance for solving multimodal optimization problems (MMOPs). In this paper, an improved algorithm based on the MA-ES, which is called the matrix adaptation evolution strategy with multi-objective optimization algorithm, is proposed to solve multimodal optimization problems (MA-ESN-MO). Taking advantage of the multi-objective optimization in maintaining population diversity, MA-ESN-MO transforms an MMOP into a bi-objective optimization problem. The archive is employed to save better solutions for improving the convergence of the algorithm. Moreover, the peaks found by the algorithm can be maintained until the end of the run. Multiple subpopulations are used to explore and exploit in parallel to find multiple optimal solutions for the given problem. Experimental results on CEC2013 test problems show that the covariance matrix adaptation with Niching and the multi-objective optimization algorithm (CMA-NMO), CMA Niching with the Mahalanobis Metric and the multi-objective optimization algorithm (CMA-NMM-MO), and matrix adaptation evolution strategy Niching with the multi-objective optimization algorithm (MA-ESN-MO) have overall better performance compared with the covariance matrix adaptation evolution strategy (CMA-ES), matrix adaptation evolution strategy (MA-ES), CMA Niching (CMA-N), CMA-ES Niching with Mahalanobis Metric (CMA-NMM), and MA-ES-Niching (MA-ESN).

scholarly journals Brain Storm Optimization Algorithm for Data Classification

2021 ◽  
Author(s):  
Zhifeng Zhang ◽  
Shaolin Zhu ◽  
Tianqi Li ◽  
Baohuan Li
Keyword(s):  
Optimization Algorithm ◽  
Fuzzy System ◽  
Fuzzy Systems ◽  
Inductive Learning ◽  
Fuzzy Rule ◽  
Data Classification ◽  
Search Space ◽  
Exponential Model ◽  
Brain Storm Optimization ◽  
The Brain

Abstract With the increasing of the number of dimensions or variables in the search space, the inductive learning of fuzzy rule classifier will be influenced by the generation and optimization of rules. Thus, the extensibility and accuracy of fuzzy systems will be affected. In this paper, the brain storm optimization algorithm was used. A new fuzzy system was designed by modifying the rules definition process in traditional fuzzy system. In the derivation of rules, the exponential model was introduced to improve the traditional brain storming algorithm. On the basis, this new fuzzy system was used for the research on data classification. The experimental results show that this new fuzzy system can improve the accuracy of data classification.

Simultaneous and Sequential Estimation of Optimal Placement and Controls of Wells With a Covariance Matrix Adaptation Algorithm

SPE Journal ◽  
2016 ◽  
Vol 21 (02) ◽  
pp. 501-521 ◽  
Author(s):  
Fahim Forouzanfar ◽  
Walter E. Poquioma ◽  
Albert C. Reynolds
Keyword(s):  
Life Cycle ◽  
Covariance Matrix ◽  
Optimization Algorithm ◽  
Optimization Problem ◽  
Temporal Correlation ◽  
Production Optimization ◽  
Sequential Optimization ◽  
Joint Optimization ◽  
Well Placement ◽  
Covariance Matrix Adaptation

Summary In this paper, we present both simultaneous and sequential algorithms for the joint optimization of well trajectories and their life-cycle controls. The trajectory of a well is parameterized in terms of six variables that define a straight line in three dimensions. In the simultaneous joint optimization algorithm, the set of controls of a well throughout the life cycle of the reservoir is constructed as a linear combination of the left singular vectors that correspond to the largest singular values of a specified temporal covariance matrix. This covariance matrix is used to impose a temporal correlation on the controls at each well. In this approach, well controls are parameterized in terms of a few optimization parameters to reduce the dimension of the joint optimization problem. Moreover, the imposed smoothness on the well controls will result in temporally smooth well controls. We use an implementation of the covariance matrix adaptation–evolution strategy (CMA-ES) optimization algorithm to solve the defined optimization problem. In the sequential optimization algorithm, first, the trajectories of the wells are optimized with the CMA-ES optimization algorithm whereas the controls of the wells are prespecified. After the optimum trajectories of the wells are obtained, the life-cycle production optimization step is performed to find the optimal well controls for the specified well trajectories. For the production optimization step, we compare the performance of three optimization algorithms that are the standard ensemble-based optimization algorithm (EnOpt), the standard CMA-ES algorithm, and a variant of the CMA-ES algorithm in which we set the initial covariance matrix equal to a prespecified covariance that imposes a temporal correlation on the controls of each well. The performance of the proposed algorithms is tested for the joint optimization of well trajectories and controls of injectors and producers for the PUNQ reservoir model. The proposed simultaneous well placement/well control optimization algorithm obtained better results than did the sequential optimization framework. The CMA-ES algorithm performed well for both well placement and production optimization purposes. Moreover, the CMA algorithm with a prespecified covariance that imposes a temporal correlation on the well controls obtained a higher net present value compared with EnOpt for the life-cycle production optimization step of the sequential framework.

scholarly journals Linear Array Synthesis for Wireless Power Transmission Based on Brain Storm Optimization Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Guang Xu Liu ◽  
Qin Qin ◽  
Qing He Zhang
Keyword(s):  
Optimization Algorithm ◽  
Power Transmission ◽  
Linear Array ◽  
Collection Efficiency ◽  
Linear Antenna ◽  
Wireless Power ◽  
Optimization Constraints ◽  
Brain Storm Optimization ◽  
Simulation Results ◽  
The Brain

Based on the brain storm optimization algorithm, this paper proposed a new method to optimize the beam collection efficiency of the linear antenna array. In the process of optimization, constraints such as aperture size and minimum antenna spacing are considered. In this paper, the optimization with different antenna apertures, different antenna angles, and different array numbers are studied. A series of representative data and simulation results are given and the superiority of the brainstorming algorithm is demonstrated by comparing with the genetic algorithm.

MSA: An Application of Brain Storm Optimization Algorithm

2020 ◽  
Author(s):  
Pujari Jeevana Jyothi ◽  
Karteeka Pavan K ◽  
S M Raiyyan ◽  
T Rajasekhar
Keyword(s):  
Optimization Algorithm ◽  
Brain Storm Optimization
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