Performance Enhancement Of Artificial Bee Colony Optimization Algorithm
Artificial Bee Colony (ABC) algorithm is a recently proposed bio-inspired optimization algorithm, simulating foraging phenomenon of honeybees. Although literature works have revealed the superiority of ABC algorithm on numerous benchmark functions and real-world applications, the standard ABC and it...
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
2013
|
| Subjects: | |
| Online Access: | http://eprints.usm.my/45016/1/Abdul%20Ghani%20Abro24.pdf |
| _version_ | 1797011298768125952 |
|---|---|
| author | Abro, Abdul Ghani |
| author_facet | Abro, Abdul Ghani |
| author_sort | Abro, Abdul Ghani |
| collection | USM |
| description | Artificial Bee Colony (ABC) algorithm is a recently proposed bio-inspired optimization algorithm, simulating foraging phenomenon of honeybees. Although literature works have revealed the superiority of ABC algorithm on numerous benchmark functions and real-world applications, the standard ABC and its variants have been found to suffer from slow convergence, prone to local-optima traps, poor exploitation and poor capability to replace exhaustive potential-solutions. To overcome the problems, this research work has proposed few modified and new ABC variants; Gbest Influenced-Random ABC (GRABC) algorithm systematically exploits two different mutation equations for appropriate exploration and exploitation of search-space, Multiple Gbest-guided ABC (MBABC) algorithm enhances the capability of locating global optimum by exploiting so-far-found multiple best regions of a search-space, Enhanced ABC (EABC) algorithm speeds up exploration for optimal-solutions based on the best so-far-found region of a search-space and Enhanced Probability-Selection ABC (EPS-ABC) algorithm, a modified version of the Probability-Selection ABC algorithm, simultaneously capitalizes on three different mutation equations for determining the global-optimum. All the proposed ABC variants have been incorporated with a proposed intelligent scout-bee scheme whilst MBABC and EABC employ a novel elite-update scheme. |
| first_indexed | 2024-03-06T15:32:17Z |
| format | Thesis |
| id | usm.eprints-45016 |
| institution | Universiti Sains Malaysia |
| language | English |
| last_indexed | 2024-03-06T15:32:17Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | usm.eprints-450162019-07-23T02:59:16Z http://eprints.usm.my/45016/ Performance Enhancement Of Artificial Bee Colony Optimization Algorithm Abro, Abdul Ghani TK1-9971 Electrical engineering. Electronics. Nuclear engineering Artificial Bee Colony (ABC) algorithm is a recently proposed bio-inspired optimization algorithm, simulating foraging phenomenon of honeybees. Although literature works have revealed the superiority of ABC algorithm on numerous benchmark functions and real-world applications, the standard ABC and its variants have been found to suffer from slow convergence, prone to local-optima traps, poor exploitation and poor capability to replace exhaustive potential-solutions. To overcome the problems, this research work has proposed few modified and new ABC variants; Gbest Influenced-Random ABC (GRABC) algorithm systematically exploits two different mutation equations for appropriate exploration and exploitation of search-space, Multiple Gbest-guided ABC (MBABC) algorithm enhances the capability of locating global optimum by exploiting so-far-found multiple best regions of a search-space, Enhanced ABC (EABC) algorithm speeds up exploration for optimal-solutions based on the best so-far-found region of a search-space and Enhanced Probability-Selection ABC (EPS-ABC) algorithm, a modified version of the Probability-Selection ABC algorithm, simultaneously capitalizes on three different mutation equations for determining the global-optimum. All the proposed ABC variants have been incorporated with a proposed intelligent scout-bee scheme whilst MBABC and EABC employ a novel elite-update scheme. 2013-07 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/45016/1/Abdul%20Ghani%20Abro24.pdf Abro, Abdul Ghani (2013) Performance Enhancement Of Artificial Bee Colony Optimization Algorithm. PhD thesis, Universiti Sains Malaysia. |
| spellingShingle | TK1-9971 Electrical engineering. Electronics. Nuclear engineering Abro, Abdul Ghani Performance Enhancement Of Artificial Bee Colony Optimization Algorithm |
| title | Performance Enhancement Of Artificial Bee Colony Optimization Algorithm |
| title_full | Performance Enhancement Of Artificial Bee Colony Optimization Algorithm |
| title_fullStr | Performance Enhancement Of Artificial Bee Colony Optimization Algorithm |
| title_full_unstemmed | Performance Enhancement Of Artificial Bee Colony Optimization Algorithm |
| title_short | Performance Enhancement Of Artificial Bee Colony Optimization Algorithm |
| title_sort | performance enhancement of artificial bee colony optimization algorithm |
| topic | TK1-9971 Electrical engineering. Electronics. Nuclear engineering |
| url | http://eprints.usm.my/45016/1/Abdul%20Ghani%20Abro24.pdf |
| work_keys_str_mv | AT abroabdulghani performanceenhancementofartificialbeecolonyoptimizationalgorithm |