Niche Brain Storm Optimization Algorithm for Multi-Peak Function Optimization

2014 ◽  
Vol 989-994 ◽  
pp. 1626-1630 ◽  
Author(s):  
Heng Jun Zhou ◽  
Ming Yan Jiang ◽  
Xian Ye Ben
Keyword(s):  
Optimization Algorithm ◽  
Population Diversity ◽  
Function Optimization ◽  
Excellent Performance ◽  
Brain Storm Optimization ◽  
Peak Function ◽  
Searching Ability ◽  
Swarm Intelligence Optimization ◽  
Global And Local ◽  
Better Than

Brain Storm Optimization (BSO) is a novel proposed swarm intelligence optimization algorithm which has a fast convergent speed. However, it is easy to trap into local optimal. In this paper, a new model based on niche technology, which is named Niche Brain Storm Optimization (NBSO), is proposed to overcome the shortcoming of BSO. Niche technology effectively prevents premature and maintains population diversity during the evolution process. NBSO shows excellent performance in searching global value and finding multiple global and local optimal solutions for the multi-peak problems. Several benchmark functions are introduced to evaluate its performance. Experimental results show that NBSO performs better than BSO in global searching ability and faster than Niche Genetic Algorithm (NGA) in finding peaks for multi-peak function.

scholarly journals A Dynamic Multistage Hybrid Swarm Intelligence Optimization Algorithm for Function Optimization

2012 ◽  
Vol 2012 ◽  
pp. 1-22 ◽  
Author(s):  
Daqing Wu ◽  
Jianguo Zheng
Keyword(s):  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Hybrid Algorithm ◽  
Particle Swarm ◽  
Population Diversity ◽  
Function Optimization ◽  
Benchmark Problems ◽  
Particle Swarm Optimizer ◽  
Hybrid Swarm ◽  
Swarm Intelligence Optimization

A novel dynamic multistage hybrid swarm intelligence optimization algorithm is introduced, which is abbreviated as DM-PSO-ABC. The DM-PSO-ABC combined the exploration capabilities of the dynamic multiswarm particle swarm optimizer (PSO) and the stochastic exploitation of the cooperative artificial bee colony algorithm (CABC) for solving the function optimization. In the proposed hybrid algorithm, the whole process is divided into three stages. In the first stage, a dynamic multiswarm PSO is constructed to maintain the population diversity. In the second stage, the parallel, positive feedback of CABC was implemented in each small swarm. In the third stage, we make use of the particle swarm optimization global model, which has a faster convergence speed to enhance the global convergence in solving the whole problem. To verify the effectiveness and efficiency of the proposed hybrid algorithm, various scale benchmark problems are tested to demonstrate the potential of the proposed multistage hybrid swarm intelligence optimization algorithm. The results show that DM-PSO-ABC is better in the search precision, and convergence property and has strong ability to escape from the local suboptima when compared with several other peer algorithms.

Improved Particle Swarm Optimization Algorithm and its Application Based on the Aggregation Degree

2013 ◽  
Vol 427-429 ◽  
pp. 1934-1938
Author(s):  
Zhong Rong Zhang ◽  
Jin Peng Liu ◽  
Ke De Fei ◽  
Zhao Shan Niu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Population Diversity ◽  
Function Optimization ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

The aim is to improve the convergence of the algorithm, and increase the population diversity. Adaptively particles of groups fallen into local optimum is adjusted in order to realize global optimal. by judging groups spatial location of concentration and fitness variance. At the same time, the global factors are adjusted dynamically with the action of the current particle fitness. Four typical function optimization problems are drawn into simulation experiment. The results show that the improved particle swarm optimization algorithm is convergent, robust and accurate.

Research on Extreme Points Optimizing of Nonlinear Multi-Peak Function Based on Genetic Algorithm

2010 ◽  
Vol 121-122 ◽  
pp. 304-308
Author(s):  
Lu Gang Yang
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Extreme Points ◽  
Function Optimization ◽  
Experimental Result ◽  
Genetic Optimization ◽  
Binary Coding ◽  
Coding Method ◽  
Peak Function ◽  
Better Than

In the application of Genetic Algorithm (GA) to solve the function optimization problem, different encoding methods have different effect on performance of GA. Aiming at the global optimization problem of a class of nonlinear multi-peak function, the paper utilized binary coding and floating coding methods for genetic optimization and analyzed their performance. The experimental result of four kinds of typical nonlinear multi-peak function showed that under the precondition of given genetic operator, the optimizing performance of floating coding method to optimize nonlinear multi-peak function with isolated extreme points is less that the binary coding. The tuning ability of floating coding is stronger. As to the ordinary multi-peak function, the search affect is better than binary coding.

An Optimization Algorithm Based on Brainstorming Process

2013 ◽  
pp. 137-166
Author(s):  
Yuhui Shi
Keyword(s):  
Optimization Algorithm ◽  
Information Entropy ◽  
The Other ◽  
Future Research ◽  
Brain Storm Optimization ◽  
Cluster Distance ◽  
Simulation Results ◽  
Better Than

In this paper, the human brainstorming process is modeled, based on which two versions of Brain Storm Optimization (BSO) algorithm are introduced. Simulation results show that both BSO algorithms perform reasonably well on ten benchmark functions, which validates the effectiveness and usefulness of the proposed BSO algorithms. Simulation results also show that one of the BSO algorithms, BSO-II, performs better than the other BSO algorithm, BSO-I, in general. Furthermore, average inter-cluster distance Dc and inter-cluster diversity De are defined, which can be used to measure and monitor the distribution of cluster centroids and information entropy of the population over iterations. Simulation results illustrate that further improvement could be achieved by taking advantage of information revealed by Dc and/or De, which points at one direction for future research on BSO algorithms.

An Optimization Algorithm Based on Brainstorming Process

2015 ◽  
pp. 1-35 ◽  
Author(s):  
Yuhui Shi
Keyword(s):  
Optimization Algorithm ◽  
Information Entropy ◽  
The Other ◽  
Future Research ◽  
Brain Storm Optimization ◽  
Cluster Distance ◽  
Simulation Results ◽  
Better Than

In this chapter, the human brainstorming process is modeled, based on which two versions of a Brain Storm Optimization (BSO) algorithm are introduced. Simulation results show that both BSO algorithms perform reasonably well on ten benchmark functions, which validates the effectiveness and usefulness of the proposed BSO algorithms. Simulation results also show that one of the BSO algorithms, BSO-II, performs better than the other BSO algorithm, BSO-I, in general. Furthermore, average inter-cluster distance Dc and inter-cluster diversity De are defined, which can be used to measure and monitor the distribution of cluster centroids and information entropy of the population over iterations. Simulation results illustrate that further improvement could be achieved by taking advantage of information revealed by Dc, which points at one direction for future research on BSO algorithms.

scholarly journals An Improved Brain Storm Optimization with Differential Evolution Strategy for Applications of ANNs

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Zijian Cao ◽  
Xinhong Hei ◽  
Lei Wang ◽  
Yuhui Shi ◽  
Xiaofeng Rong
Keyword(s):  
Differential Evolution ◽  
Control Method ◽  
Size Control ◽  
Evolution Strategy ◽  
Step Size ◽  
Local Optima ◽  
Brain Storm Optimization ◽  
Searching Ability ◽  
Swarm Intelligence Algorithm ◽  
Better Than

Brain Storm Optimization (BSO) algorithm is a swarm intelligence algorithm inspired by human being’s behavior of brainstorming. The performance of BSO is maintained by the creating process of ideas, but when it cannot find a better solution for some successive iterations, the result will be so inefficient that the population might be trapped into local optima. In this paper, we propose an improved BSO algorithm with differential evolution strategy and new step size method. Firstly, differential evolution strategy is incorporated into the creating operator of ideas to allow BSO jump out of stagnation, owing to its strong searching ability. Secondly, we introduce a new step size control method that can better balance exploration and exploitation at different searching generations. Finally, the proposed algorithm is first tested on 14 benchmark functions of CEC 2005 and then is applied to train artificial neural networks. Comparative experimental results illustrate that the proposed algorithm performs significantly better than the original BSO.

An Optimization Algorithm Based on Brainstorming Process

2011 ◽  
Vol 2 (4) ◽  
pp. 35-62 ◽  
Author(s):  
Yuhui Shi
Keyword(s):  
Optimization Algorithm ◽  
Information Entropy ◽  
The Other ◽  
Future Research ◽  
Brain Storm Optimization ◽  
Cluster Distance ◽  
Simulation Results ◽  
Better Than

In this paper, the human brainstorming process is modeled, based on which two versions of Brain Storm Optimization (BSO) algorithm are introduced. Simulation results show that both BSO algorithms perform reasonably well on ten benchmark functions, which validates the effectiveness and usefulness of the proposed BSO algorithms. Simulation results also show that one of the BSO algorithms, BSO-II, performs better than the other BSO algorithm, BSO-I, in general. Furthermore, average inter-cluster distance Dc and inter-cluster diversity De are defined, which can be used to measure and monitor the distribution of cluster centroids and information entropy of the population over iterations. Simulation results illustrate that further improvement could be achieved by taking advantage of information revealed by Dc and/or De, which points at one direction for future research on BSO algorithms.

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

AO-BBO: A Novel Optimization Algorithm and Its Application in Plant Drug Extraction

2019 ◽  
Vol 19 (2) ◽  
pp. 139-145 ◽  
Author(s):  
Bote Lv ◽  
Juan Chen ◽  
Boyan Liu ◽  
Cuiying Dong
Keyword(s):  
Optimization Algorithm ◽  
Learning Strategy ◽  
Optimal Solution ◽  
Population Diversity ◽  
Global Optimal Solution ◽  
Plant Medicine ◽  
Suitability Index ◽  
Sensing Model ◽  
Local Optimal Solution ◽  
Global Optimal

<P>Introduction: It is well-known that the biogeography-based optimization (BBO) algorithm lacks searching power in some circumstances. </P><P> Material & Methods: In order to address this issue, an adaptive opposition-based biogeography-based optimization algorithm (AO-BBO) is proposed. Based on the BBO algorithm and opposite learning strategy, this algorithm chooses different opposite learning probabilities for each individual according to the habitat suitability index (HSI), so as to avoid elite individuals from returning to local optimal solution. Meanwhile, the proposed method is tested in 9 benchmark functions respectively. </P><P> Result: The results show that the improved AO-BBO algorithm can improve the population diversity better and enhance the search ability of the global optimal solution. The global exploration capability, convergence rate and convergence accuracy have been significantly improved. Eventually, the algorithm is applied to the parameter optimization of soft-sensing model in plant medicine extraction rate. Conclusion: The simulation results show that the model obtained by this method has higher prediction accuracy and generalization ability.</P>

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