Kısıtlı Mühendislik Tasarım Problemleri için Balina Güdümlü Fırtına Optimizasyon Algoritması

Abstract

Bu çalışmada, kısıtlı mühendislik tasarım problemlerinin çözümü için Elektrik Fırtınası Optimizasyonu (ESO) üzerine kurulu Balina Güdümlü Fırtına (BGF) adlı hibrit bir meta-sezgisel yöntem önerilmektedir. BGF, ESO’nun keşif–sömürü dengesini, Balina Optimizasyon Algoritmasının (WOA) spiral güncelleme mekanizmasıyla zenginleştirilmiş yerel arama adımıyla birleştirir. Kısıtlı aramada fizibiliteyi öncelemek için aday çözümler Deb’in fizibilite kurallarıyla değerlendirilmekte; yerel optimumlara takılmayı azaltmak ve çeşitliliği korumak amacıyla durgunluk eşiği aşıldığında popülasyonun en kötü %20’sine OBL tabanlı yeniden konumlandırma uygulanmaktadır. Yöntem, Kumar vd. benchmark setindeki dört problem üzerinde sınanmıştır: RC23 (düzeltilmiş kademeli konik kasnak), RC24 (sıfır tuzağını engelleyen ek kısıtlı robot tutucu), RC29 (gaz iletim kompresörü) ve RC32 (Himmelblau). Tüm algoritmalar popülasyon=50 ve iterasyon=1000 ile 30 bağımsız koşuda çalıştırılmış; Min./Ort./Maks./Std. istatistikleri ve yakınsama eğrileri üzerinden WOA, JADE, TLO, EO ve ESO ile karşılaştırılmıştır. Sonuçlar, BGF’nin RC23, RC29 ve RC32’de en iyi ortalama performans ve en düşük saçılımı sağlayarak yüksek kararlılık sunduğunu, RC24’te ise rekabetçi olmakla birlikte JADE/TLO’nun gerisinde kaldığını göstermektedir. RC23 ve RC24’te literatürde gözlenen formülasyon hataları mühendislik tutarlılığı korunarak düzeltilmiş ve RC24’e minimum kuvvet eşiği eklenmiştir. Uygulama Python ortamında gerçekleştirilmiş, her koşu aynı başlangıç ayarlarıyla yürütülmüştür. Elde edilen bulgular BGF’nin kısıtlı optimizasyon için güçlü bir aday olduğunu, ancak keşif ağırlıklı problemlerde ek geliştirmelere açık kaldığını ortaya koymaktadır.

Keywords

• Kısıtlı optimizasyon
• Meta-sezgisel algoritmalar
• Elektrik Fırtınası Optimizasyonu
• Balina Optimizasyon Algoritması
• Karşıtlık tabanlı öğrenme
• Mühendislik tasarım optimizasyonu

Ethical Statement

Bu çalışma insan veya hayvan denek içermediğinden etik kurul izni ve aydınlatılmış onam gerekmemektedir. Yazar çıkar çatışması olmadığını ve çalışma için dış fon desteği alınmadığını beyan eder.

Whale-Guided Storm Optimization Algorithm for Constrained Engineering Design Problems

Abstract

This study proposes the Whale-Guided Storm (BGF) algorithm, a hybrid metaheuristic based on Electric Storm Optimization (ESO) for constrained engineering design. BGF enhances ESO by adding a local-search step using the Whale Optimization Algorithm (WOA) spiral update. Solutions are assessed with Deb’s feasibility rules; when stagnation is detected, opposition-based learning (OBL) repositions the worst 20% of the population to preserve diversity and reduce local-optimum trapping. BGF is tested on four constrained benchmarks from Kumar et al.: RC23 (corrected stepped conical pulley), RC24 (robot gripper with an added constraint to avoid the zero-trap), RC29 (gas transmission compressor), and RC32 (Himmelblau). All algorithms run with population=50 and iterations=1000 for 30 independent trials; min/mean/max/std statistics and convergence curves compare BGF against WOA, JADE, TLO, EO, and ESO. Results show BGF achieves the best mean performance and the lowest dispersion on RC23, RC29, and RC32, while on RC24 it is competitive but behind JADE/TLO. Formulation issues reported in the literature for RC23 and RC24 are corrected, and a minimum-force threshold is added to RC24. The Python implementation uses identical initial settings across runs. Overall, BGF is a strong candidate for constrained optimization, but remains open to further improvements on exploration-dominant problems.

Keywords

• Constrained optimization
• Metaheuristic algorithms
• Electric Storm Optimization
• Whale Optimization Algorithm
• Opposition-Based Learning
• Engineering design optimization

Ethical Statement

This study does not involve human participants or animals. Therefore, ethics committee approval and informed consent are not required. The author declares no conflict of interest. This research received no external funding.

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