Solving multi-task optimization problems using the sine cosine algorithm
Optimization problems relate to the problem of finding minimum or maximum values from a large pools of solutions whereby exhaustive search is practically impossible. Often, optimization problems are solved using metaheuristic algorithms which provide good enough solution within reasonable execution...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
Association for Computing Machinery
2022
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/34368/1/Solving%20multi-task%20optimization%20problems%20using%20the%20sine%20cosine%20algorithm_FULL.pdf http://umpir.ump.edu.my/id/eprint/34368/2/Solving%20multi-task%20optimization%20problems%20using%20the%20sine%20cosine%20algorithm%20.pdf http://umpir.ump.edu.my/id/eprint/34368/ https://doi.org/10.1145/3524304.3524336 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Optimization problems relate to the problem of finding minimum or maximum values from a large pools of solutions whereby exhaustive search is practically impossible. Often, optimization problems are solved using metaheuristic algorithms which provide good enough solution within reasonable execution time and limited resources. Recently, much research focus in the literature is devoted on a new kind of optimization problem, called multi-task optimization (MTO). This paper highlights our on-going work dealing with MTO problem. More precisely, our work investigates the adoption of partitioned population based on Sine Cosine algorithm for dealing with MTO problem. We took the team formation problem from IMDB dataset as our case study based on two objectives, minimizing team costs and team load distribution. |
|---|