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Journals / ACADEMIC PLATFORM-JOURNAL OF ENGINEERING AND SCIENCE / 2020 / Cilt: 8 - Sayı: 2

The Solution of 0-1 Multidimensional Knapsack Problem with Pheromonal Artificial Bee Colony (pABC) Algorithm

0-1 Çok Boyutlu Sırt Çantası Probleminin Feromonal Yapay Arı Koloni (fYAK) Algoritması ile Çözümü

Dursun EKMEKCİ
Pages
355–364
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The performance of pABC was tested on popular benchmark problems, and the results obtained by the algorithm were compared with the results of ABC and ACO.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Optimization algorithms can produce more successful solutions by focusing more on some problems in terms of their development style.
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Key Findings & Data:For example, the artificial bee colony (ABC) algorithm produced by the numerical solution approach can achieve more successful results in numerical optimization problems, whereas ant colony optimization (ACO) can produce more successful solutions in discrete structure problems such as traveling salesman problem (TSP). 0-1 optimization problems are discrete structured problems.
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Conclusion & Impact:The performance of pABC was tested on popular benchmark problems, and the results obtained by the algorithm were compared with the results of ABC and ACO.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

Optimization algorithms can produce more successful solutions by focusing more on some problems in terms of their development style. For example, the artificial bee colony (ABC) algorithm produced by the numerical solution approach can achieve more successful results in numerical optimization problems, whereas ant colony optimization (ACO) can produce more successful solutions in discrete structure problems such as traveling salesman problem (TSP). 0-1 optimization problems are discrete structured problems. However, it can be considered as the third group of optimization problems in terms of solution items. In this study, the pABC algorithm developed as a hybrid version of ABC and ACO algorithms for 0-1 multidimensional knapsack problems was proposed. The performance of pABC was tested on popular benchmark problems, and the results obtained by the algorithm were compared with the results of ABC and ACO.

Özet

Optimizasyon algoritmaları, geliştirilme tarzları itibariyle bazı problemlere daha çok odaklanarak, daha başarılı çözümler üretebilmektedirler. Örneğin sayısal çözüm yaklaşımıyla üretilen yapay arı koloni (YAK) algoritması, nümerik optimizasyon problemlerinde daha başarılı sonuçlara ulaşabilirken, karınca koloni optimizasyonu (KKO), gezgin satıcı problemi (GSP) benzeri ayrık yapılı optimizasyon problemlerinde daha başarılı çözümler üretebilir. 0-1 optimizasyon problemleri, ayrık yapılı problemlerdir. Ancak çözüm elemanları itibariyle optimizasyon problemlerinin üçüncü grubu olarak değerlendirilebilir. Bu çalışmada 0-1 çok boyutlu sırt çantası problemleri için YAK ve KKO algoritmalarının melez versiyonu olarak geliştirilen fYAK algoritması önerilmiştir. Algoritma performansı, popüler test problemleri üzerinde denenmiş ve elde edilen sonuçlar YAK ve KKO sonuçlarıyla karşılaştırılmıştır.

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Cite This Article

EKMEKCİ, D. (2020). The Solution of 0-1 Multidimensional Knapsack Problem with Pheromonal Artificial Bee Colony (pABC) Algorithm. ACADEMIC PLATFORM-JOURNAL OF ENGINEERING AND SCIENCE, Cilt: 8 - Sayı: 2, 355–364. https://ajindex.com/articles/354874

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