Optimization Of The Beluga Whale Optimization (BWO) Algorithm - Beluga Balinası Optimizasyonu (BWO) Algoritmasının Optimizasyonu
2024-mso - Project codes ⭐
Optimization of BWO Algorithm Project Report.pdf - Project report ⭐
BWO Article.pdf - Original BWO Article
Benchmark_Functions_for_CEC_2022_Competition_on_Se.pdf - Document containing CEC 2022 Competition details
This project aims to improve the performance of the Beluga Whale Optimization (BWO) algorithm. The enhancements involve using Fitness Distance Balance (FDB) and Roulette Fitness Distance Balance (RFDB) methods for selecting different algorithm parameters.
- Algorithm Name: BWO (Beluga Whale Optimization)
- Applied Method(s): FDB (Fitness Distance Balance), RFDB (Roulette Fitness Distance Balance)
- Total number of cases: 7
- Number of runs per problem: 12
In this project, modifications have been made to various control and exploration parameters used in the BWO algorithm. These variables include:
- RJ: Randomly selected guide solution.
- r1, r7, alpha, C1, C2, r5: Different control and exploration parameters used in the BWO algorithm.
Each case changes the selection method of a specific parameter. The impact of these changes on the algorithm's performance has been thoroughly investigated.