A Scalable MapReduce-enabled Glowworm Swarm Optimization Approach for High Dimensional Multimodal Functions

2016 ◽  
Vol 7 (1) ◽  
pp. 32-54 ◽  
Author(s):  
Ibrahim Aljarah ◽  
Simone A. Ludwig
Keyword(s):  
Programming Model ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Multimodal Functions ◽  
Scalability Analysis ◽  
Glowworm Swarm Optimization ◽  
Higher Dimensional ◽  
Parallel Programming Model ◽  
The Common ◽  
Swarm Size

Glowworm Swarm Optimization (GSO) is one of the common swarm intelligence algorithms, where GSO has the ability to optimize multimodal functions efficiently. In this paper, a parallel MapReduce-based GSO algorithm is proposed to speedup the GSO optimization process. The authors argue that GSO can be formulated based on the MapReduce parallel programming model quite naturally. In addition, they use higher dimensional multimodal benchmark functions for evaluating the proposed algorithm. The experimental results show that the proposed algorithm is appropriate for optimizing difficult multimodal functions with higher dimensions and achieving high peak capture rates. Furthermore, a scalability analysis shows that the proposed algorithm scales very well with increasing swarm sizes. In addition, an overhead of the Hadoop infrastructure is investigated to find if there is any relationship between the overhead, the swarm size, and number of nodes used.

scholarly journals Modelling an Effectual Glowworm Swarm Optimization Strategy for Feature Selection in Heart Disease Prediction

2020 ◽  
Vol 9 (1) ◽  
pp. 500-505
Keyword(s):  
Feature Selection ◽  
Heart Disease ◽  
Disease Diagnosis ◽  
Common Disease ◽  
Optimization Approach ◽  
Optimization Strategy ◽  
Swarm Optimization ◽  
Glowworm Swarm Optimization ◽  
Heart Disease Diagnosis ◽  
Sensitivity Specificity

Heart disease is measured as a common disease all over the world. The ultimate target is to provide heart disease diagnosis with improved feature selection with Glow worm swarm optimization algorithm. The anticipated model comprises of optimization approach for feature selection and classifier for predicting heart disease. This system framework comprises of three stages: 1) data processing, 2) feature selection using IGWSO approach and classification with conventional machine learning classifiers. Here, C4.5 classifier is considered for performing the function. The benchmark dataset that has been attained from UCI database was cast off for performing computation. Maximal classification accuracy has been achieved based on cross validation strategy. Outcomes depicts that performance of anticipated model is superior in contrary to other models. Simulation has been done with MATLAB environment. Metrics like accuracy, sensitivity, specificity, F-measure and recall has been evaluated

A Self-Adaptive Step Glowworm Swarm Optimization Approach

2019 ◽  
Vol 18 (01) ◽  
pp. 1950004 ◽  
Author(s):  
Peng Qiong ◽  
Yifan Liao ◽  
Peng Hao ◽  
Xiaonia He ◽  
Chen Hui
Keyword(s):  
Step Length ◽  
Local Optimum ◽  
Optimization Approach ◽  
Step Size ◽  
Swarm Optimization ◽  
Glowworm Swarm Optimization ◽  
Peak Function ◽  
Solution Accuracy ◽  
Adaptive Step ◽  
Self Adaptive

When the basic glowworm swarm optimization (GSO) algorithm optimizes the multi-peak function, the solution accuracy is not high, the later convergence is slow. To solve these problems, the fluorescent factor is introduced to adaptively adjust the step length of the firefly, an adaptive step length firefly optimization algorithm is proposed, this algorithm is an improved self-adaptive step glowworm swarm optimization (ASGSO). In this algorithm, the behavior of glowworms are developed, the step size is dynamically adjusted by the fluorescent factor, the algorithm avoids falling into a local optimum and improves the optimization speed and accuracy. The simulation results show that the improved ASGSO can search for global optimization more quickly and precisely.

Sign in / Sign up

Export Citation Format

Share Document