Performance Analysis of Some New Hybrid Metaheuristic Algorithms for High‐Dimensional Optimization Problems

Author(s):  
Souvik Ganguli ◽  
Gagandeep Kaur ◽  
Prasanta Sarkar
2012 ◽  
Vol 236-237 ◽  
pp. 1184-1189
Author(s):  
Wen Hua Han ◽  
Chang Dong Zhu

This paper presents a novel optimization technique called embedded micro-particle swarm optimization (EMPSO) to solve high-dimensional problems with continuous variables. The proposed EMPSO adopts a population memory which is divided into two portions as the source of diversity, and an external memory to collect particles performing well in an embedded PSO with a very small population size. However, the fact that the new method doesn’t excel in all of the benchmark functions highlights the necessity of developing improvement. Thus an adaptive mutation operator is introduced into EMPSO to remedy the issue. The experimental results show that the improved EMPSO has good performance for solving large-scale optimization problems.


2020 ◽  
Vol 29 (2) ◽  
pp. 337-343 ◽  
Author(s):  
Shijie Zhao ◽  
Leifu Gao ◽  
Jun Tu ◽  
Dongmei Yu

2015 ◽  
Vol 122 (12) ◽  
pp. 1-10 ◽  
Author(s):  
Ali OsmanTopal ◽  
Oguz Altun ◽  
Yunus Emre Yildiz

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