Adaptive mechanism for enhanced performance of shark smell optimization / Nur Atharah Kamarzaman, Shahril Irwan Sulaiman and Intan Rahayu Ibrahim

Various meta-heuristic approaches have been developed to find the optimal solution to optimization problems. However, different approach takes different amount of time and efficiency to achieve the optimal solution. Determination of high performance and lower computing time with simple algorithm is...

Full description

Saved in:
Bibliographic Details
Main Authors: Kamarzaman, Nur Atharah (Author), Sulaiman, Shahril Irwan (Author), Ibrahim, Intan Rahayu (Author)
Format: Book
Published: Universiti Teknologi MARA, 2021-04.
Subjects:
Online Access:Link Metadata
Tags: Add Tag
No Tags, Be the first to tag this record!

MARC

LEADER 00000 am a22000003u 4500
001 repouitm_47319
042 |a dc 
100 1 0 |a Kamarzaman, Nur Atharah  |e author 
700 1 0 |a Sulaiman, Shahril Irwan  |e author 
700 1 0 |a Ibrahim, Intan Rahayu  |e author 
245 0 0 |a Adaptive mechanism for enhanced performance of shark smell optimization / Nur Atharah Kamarzaman, Shahril Irwan Sulaiman and Intan Rahayu Ibrahim 
260 |b Universiti Teknologi MARA,   |c 2021-04. 
500 |a https://ir.uitm.edu.my/id/eprint/47319/1/47319.pdf 
500 |a  Adaptive mechanism for enhanced performance of shark smell optimization / Nur Atharah Kamarzaman, Shahril Irwan Sulaiman and Intan Rahayu Ibrahim. (2021) Journal of Electrical and Electronic Systems Research (JEESR) <https://ir.uitm.edu.my/view/publication/Journal_of_Electrical_and_Electronic_Systems_Research_=28JEESR=29/>, 18. pp. 9-17. ISSN 1985-5389  
520 |a Various meta-heuristic approaches have been developed to find the optimal solution to optimization problems. However, different approach takes different amount of time and efficiency to achieve the optimal solution. Determination of high performance and lower computing time with simple algorithm is therefore continuously established. Shark Smell Optimization (SSO) algorithm has been proven to have high efficiency in many optimization applications. However, like the other swarm intelligence, SSO algorithm also has possibility to get trapped in local maxima or premature convergence. Thus, a new adaptive shark smell optimization (ASSO) is proposed to improve the convergence efficiency of standard SSO algorithm. An overview and performance comparison of six well-known meta-heuristic optimization algorithm is also presented in this paper. In order to verify the effectiveness of this newly developed method, the algorithm was tested on common benchmark functions used in the literature. Numerical results indicate that the ASSO algorithm strategy outperforms the basic SSO algorithm, Genertic Algorithm (GA), Particle Swarm Intelligence (PSO), Firefly Algorithm (FA), Artificial Bee Colony (ABC) and Teaching Learning Based Optimization (TBLO) in term of reaching for global solution. 
546 |a en 
690 |a Computer software 
690 |a Neural networks (Computer science) 
690 |a Algorithms 
655 7 |a Article  |2 local 
655 7 |a PeerReviewed  |2 local 
787 0 |n https://ir.uitm.edu.my/id/eprint/47319/ 
787 0 |n https://jeesr.uitm.edu.my 
787 0 |n https://doi.org/10.24191/jeesr.v18i1.002 
856 4 1 |u https://ir.uitm.edu.my/id/eprint/47319/  |z Link Metadata