INFLUENCE OF JUMPING RATE ON OPPOSITION-BASED JAYA ALGORITHM FOR DISCRETE TIME COST TRADE-OFF OPTIMIZATION PROBLEMS

Abstract

This paper aims to develop a new multi-objective optimization algorithm for handling construction time-cost trade-off problems (TCTPs). An intelligent strategy called opposition-based learning (OBL) is incorporated into the Jaya algorithm, resulting in the opposition-based Jaya Algorithm (OBJA). The proposed model introduces an innovative approach to opposition-based optimization by employing an iterative-based varying oppositional jumping rates. This adaptive strategy significantly contributes to increased population diversity and effective avoidance of local optima throughout both the initialization and generational phases of the optimization process. By systematically varying the opposition jumping rate, its impact on the algorithm's convergence speed, solution quality, and computational efficiency are evaluated. The experimental results demonstrate that an iterative-based varying opposition jumping rate significantly enhances OBJA's efficiency to explore and exploit the search space, leading to superior tradeoff solutions. Hence, computational experiments on 9 and 19 activity problems reveal that an iterativebased varying opposition jumping rate result in high quality solution with reduced number of function evaluations. Furthermore, the OBJA model proved to be more successful than the non-dominated sorting GA (NSGA-II), multi-objective particle swarm optimzaiton (MOPSO), and plain Jaya algorithm for handling these complex TCTPs in construction project management.

Keywords

• Time-cost trade-off problem
• Jaya algorithm
• Opposition-based learning
• Iterative-based varying opposition jumping rate.

Sıçrama Oranının Ayrık Zaman Maliyeti Ödünleşim Optimizasyonu Problemleri için Karşıtlık Tabanlı JAYA Algoritması Üzerindeki Etkisi

Abstract

Bu makale, inşaat sektörünün zaman-maliyet ödünleşim problemlerini (ZMÖP) çözmek için yeni bir çok amaçlı optimizasyon modeli geliştirmeyi amaçlamaktadır. Jaya algoritmasına karşıt tabanlı öğrenme (OBL) adı verilen akıllı bir strateji eklenmiş ve sonuç olarak karşıt tabanlı Jaya Algoritması (OBJA) önerilmiştir. OBL, popülasyonun daha iyi başlatılması ve popülasyonun yerel optimuma düşmemesi için nesil sıçrama oranı uygulanmaktadır. Önerilen model, iteratif tabanlı değişken karşıtlık sıçrama oranlarını kullanarak karşıt tabanlı optimizasyona yenilikçi bir yaklaşım sunmaktadır. Bu uyarlamalı strateji, optimizasyon sürecinin hem başlatma hem de nesil aşamalarında popülasyon çeşitliliğini artırmaya ve yerel optimal noktalardan etkili bir şekilde kaçınmaya önemli ölçüde katkıda bulunmaktadır. Karşıt sıçrama oranı sistematik olarak değiştirilerek algoritmanın yakınsama hızı, çözüm kalitesi ve hesaplama verimliliği üzerindeki etkisi değerlendirilmiştir. Deneysel sonuçlar, iteratif tabanlı değişken karşıt sıçrama oranının, OBJA'nın arama alanını arama ve araştırma yeteneğini önemli ölçüde artırarak üstün dengeleme çözümlerine yol açtığını göstermektedir. Bu nedenle, 9 ve 19 aktivite problemine yönelik hesaplamalı deneyler, iteratif tabanlı değişken karşıt sıçrama oranının, daha az fonksiyon değerlendirmesi ile yüksek kaliteli çözümler elde edilmesine neden olduğunu ortaya koymaktadır. Ayrıca, OBJA algoritması, bu karmaşık zaman-maliyet ödünleşim optimizasyon problemlerini yapı proje yönetiminde ele alırken NSGAII, MOPSO ve basit Jaya algoritmasından daha başarılı olduğunu kanıtlamıştır.

Keywords

• Zaman-maliyet ödünleşim problemi
• Jaya algoritması
• Karşıt tabanlı öğrenme
• iteratif tabanlı değişen karşıtlık sıçrama oranı

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