A Study of Population Diversity Using an Enhanced Brain Storm Optimization

2020 ◽  
Author(s):  
Samuel Nartey Kofie ◽  
Samson Hansen Sackey
Keyword(s):  
Population Diversity ◽  
Brain Storm Optimization

scholarly journals Population Diversity Maintenance In Brain Storm Optimization Algorithm

2014 ◽  
Vol 4 (2) ◽  
pp. 83-97 ◽  
Author(s):  
Shi Cheng ◽  
Yuhui Shi ◽  
Quande Qin ◽  
Qingyu Zhang ◽  
Ruibin Bai
Keyword(s):  
Swarm Intelligence ◽  
Collective Behavior ◽  
Population Diversity ◽  
Premature Convergence ◽  
Exploration And Exploitation ◽  
Search Results ◽  
Brain Storm Optimization ◽  
Convergence And Divergence ◽  
Definition Of ◽  
The Brain

Abstract The convergence and divergence are two common phenomena in swarm intelligence. To obtain good search results, the algorithm should have a balance on convergence and divergence. The premature convergence happens partially due to the solutions getting clustered together, and not diverging again. The brain storm optimization (BSO), which is a young and promising algorithm in swarm intelligence, is based on the collective behavior of human being, that is, the brainstorming process. The convergence strategy is utilized in BSO algorithm to exploit search areas may contain good solutions. The new solutions are generated by divergence strategy to explore new search areas. Premature convergence also happens in the BSO algorithm. The solutions get clustered after a few iterations, which indicate that the population diversity decreases quickly during the search. A definition of population diversity in BSO algorithm is introduced in this paper to measure the change of solutions’ distribution. The algorithm's exploration and exploitation ability can be measured based on the change of population diversity. Different kinds of partial reinitialization strategies are utilized to improve the population diversity in BSO algorithm. The experimental results show that the performance of the BSO is improved by part of solutions re-initialization strategies.

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.

Genetic Population Diversity of Aedes caspius in Southern Provinces of Iran

2018 ◽  
Vol 111 (1) ◽  
pp. 31-37 ◽  
Author(s):  
S DOOSTI ◽  
MR YAGHOOBI-ERSHADI ◽  
MM SEDAGHAT ◽  
SH MOOSA-KAZEMI ◽  
K AKBARZADEH ◽  
...  
Keyword(s):  
Population Diversity ◽  
Genetic Population ◽  
Aedes Caspius

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>

scholarly journals Knowledge-guided multiobjective particle swarm optimization with fusion learning strategies

2021 ◽  
Author(s):  
Wei Li ◽  
Xiang Meng ◽  
Ying Huang ◽  
Soroosh Mahmoodi
Keyword(s):  
Particle Swarm Optimization ◽  
Learning Strategies ◽  
Learning Strategy ◽  
Particle Swarm ◽  
Population Diversity ◽  
Selection Strategy ◽  
Rapid Loss ◽  
Solution Quality ◽  
Swarm Optimization ◽  
Multiobjective Particle Swarm Optimization

AbstractMultiobjective particle swarm optimization (MOPSO) algorithm faces the difficulty of prematurity and insufficient diversity due to the selection of inappropriate leaders and inefficient evolution strategies. Therefore, to circumvent the rapid loss of population diversity and premature convergence in MOPSO, this paper proposes a knowledge-guided multiobjective particle swarm optimization using fusion learning strategies (KGMOPSO), in which an improved leadership selection strategy based on knowledge utilization is presented to select the appropriate global leader for improving the convergence ability of the algorithm. Furthermore, the similarity between different individuals is dynamically measured to detect the diversity of the current population, and a diversity-enhanced learning strategy is proposed to prevent the rapid loss of population diversity. Additionally, a maximum and minimum crowding distance strategy is employed to obtain excellent nondominated solutions. The proposed KGMOPSO algorithm is evaluated by comparisons with the existing state-of-the-art multiobjective optimization algorithms on the ZDT and DTLZ test instances. Experimental results illustrate that KGMOPSO is superior to other multiobjective algorithms with regard to solution quality and diversity maintenance.

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