The Grey Wolf Optimizer (GWO) is a relatively new population-based optimizer. Various optimization problems have been solved using GWO. This paper presents an experiment on how to apply GWO to solve the minimum spanning tree (MST) problem. MST is normally solved using revision strategy when formulating the fitness in other population-based algorithms. Another strategy, called Penalty strategy, is used in the experiment. Existing dataset for MST problem is tested using GWO. The experiment showed that implementation of penalty strategy in the fitness function of GWO can find a solution with almost 96% accuracy.
Algoritma semut merupakan salah satu dari sejumlah algoritma yang dapat diterapkan pada permasalahan optimisasi kombinatorial. Algoritma ini mengadopsi perilaku semut alamiah yang telah banyak diimplementasikan salah satunya pada penjadwalan produksi jobshop.Penelitian ini memfokuskan pada implementasi penjadwalan produksi jobshop mesin dengan pendekatan algoritma semut, untuk menemukan fungsi tujuan, yaitu minimum makespan time dari seluruh kombinasi job-operasi yang didefinisikan.Pada tulisan ini akan dipaparkan teknik pendekatan algoritma semut pada penjadwalan produksi jobshop yang kemudian diimplementasikan menjadi perangkat lunak yang akan diujikan dengan 5 job dan 5 mesin.
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