Derin Q Ağları Tabanlı Parçacık Sürü Optimizasyonu

Year 2023, , 855 - 863, 01.09.2023

Özlem Tülek , İhsan Hakan Selvi

https://doi.org/10.35234/fumbd.1313906

Abstract

Günümüzde, yapay zekâ ve makine öğrenmesi teknolojilerindeki hızlı gelişmeler, optimizasyon problemlerinin çözümüne farklı ve yenilikçi yaklaşımlar getirmiştir. Bu yöntemler, problem çözümünde klasik optimizasyon tekniklerine alternatif yaklaşımlar sunmaktadırlar. Optimizasyon problemlerinin çözümünde sıklıkla kullanılan metasezgisel algoritmaları makine öğrenmesi teknikleriyle birlikte kullanmak güçlü bir potansiyel sunmaktadır. Bu çalışmada doğa esinli bir metasezgisel algoritma olan parçacık sürü optimizasyonu ile bir makine öğrenmesi yöntemi olan pekiştirmeli öğrenmeyi birlikte kullanan bir model önerilmiştir. Önerilen model 9 tane kıyaslama problemi kullanılarak 50 ve 100 boyut için test edilmiştir. Elde edilen sonuçlar pekiştirmeli öğrenmenin PSO’nun yakınsama ve küresel keşif yeteneklerini geliştirmek için büyük bir potansiyel sunduğunu göstermektedir.

Keywords

Pekiştirmeli öğrenme, DQN, metasezgisel optimizasyon, parçacık sürü optimizasyonu

Deep Q Networks Based Particle Swarm Optimization

Abstract

Today, rapid developments in artificial intelligence and machine learning technologies have brought different and innovative approaches to the solution of optimization problems. These methods offer alternative approaches to classical optimization techniques in problem solving. Using metaheuristic algorithms, which are frequently used in solving optimization problems, together with machine learning techniques offers a strong potential. In this study, a model that uses particle swarm optimization, which is a nature-inspired metaheuristic algorithm, and reinforcement learning, which is a machine learning method, is proposed. The proposed model is tested for 50 and 100 dimensions using 9 comparison problems. The results show that reinforcement learning offers great potential to enhance the convergence and global exploration capabilities of PSO.

Keywords

Reinforcement learning, DQN, metaheuristic optimization, particle swarm optimization.

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