Nugroho, Vanilia Cahya (2019) Solusi Optimasi Global Kontinu Menggunakan Algoritma Scout Particle Swarm. Sarjana thesis, Universitas Brawijaya.
Abstract
Optimasi merupakan suatu proses untuk mencari solusi maksimum maupun solusi minimum. Masalah optimasi dapat ditemui dalam berbagai bidang, antara lain pada bidang ekonomi, keuangan, transportasi, persediaan dan sains komputasi. Terdapat beberapa metode yang dapat menyelesaikan masalah optimasi fungsi, diantaranya adalah algoritma Particle Swarm Optimization (PSO) dan Artificial Bee Colony (ABC). Metode PSO mempunyai efisiensi yang rendah dalam eksplorasi global. PSO tidak memuat parameter yang berfungsi meregenerasi partikel yang tidak efektif (partikel yang tidak dapat memperbaiki nilai
English Abstract
Optimization is the process for finding the maximum and minimum solutions. Optimization problems can be found in various fields including economy, finance, transportation, inventory, and computational science. There are many methods that can solve optimization problem, for example Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC). PSO method has low global exploration efficiency. PSO lack of parameter for regenerating ineffective particles (particle that cannot improve their personal best position) which is caused ineffective particles to be trapped in the local optima. In contrast, ABC method has good global exploration efficiency because it has a scout bee phase that acts to regenerate ineffective particles and avoid that particle to be trapped in the local optima. In this paper, a hybrid algorithm called Scout Particle Swarm Optmization (ScPSO) is proposed, which combines standard PSO with the addition of scout bee phase in ABC to eliminates the most important handicap of PSO in global exploration for continuous global optimization problem. To evaluate the performance, ScPSO is compared with hybrid approaches based on PSO and ABC algorithms (HPA, ABC-PS dan PS-ABC) on function optimization. The test functions used have dimensions of 25 and 60. As seen in the results, optimization using ScPSO, HPA, ABC-PS and PS-ABC show that ScPSO is able to solve optimization problem better in terms of computation time and standard deviation computation time than other approaches.
Other obstract
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| Item Type: | Thesis (Sarjana) |
|---|---|
| Identification Number: | SKR/MIPA/2019/38/051910740 |
| Uncontrolled Keywords: | Scout Particle Swarm Optimization, Optimasi Fungsi Numerik, Particle Swarm Optimization, Artificial Bee Colony Optimization, Pendekatan Hibrida, Scout Particle Swarm Optimization, Numerical Function Optimization, Particle Swarm Optimization, Artificial Bee Colony Optimization, Hybrid Approach |
| Subjects: | 500 Natural sciences and mathematics > 518 Numerical analysis > 518.1 Algorithms |
| Divisions: | Fakultas Matematika dan Ilmu Pengetahuan Alam > Matematika |
| Depositing User: | Budi Wahyono Wahyono |
| Date Deposited: | 05 Aug 2020 07:57 |
| Last Modified: | 29 Mar 2022 02:08 |
| URI: | http://repository.ub.ac.id/id/eprint/176753 |
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