An Improved Golden Eagle Optimizer Using Chaotic Initialization and Adaptive Weighting for Robust Optimization Performance

This paper introduces the Improved Golden Eagle Optimizer IGEO , an enhanced version of the Golden Eagle Optimizer GEO incorporating chaotic initialization with the Logistic Map and an adaptive weighting mechanism to improve exploration–exploitation balance. IGEO was benchmarked on 23 test functions and six engineering design problems. Results show that on unimodal benchmarks, IGEO exactly reached the global optimum in 71 of cases 5 7 , compared to 29 for GEO and IGEO3 and 14 for IGEO2. It also achieved the lowest Mean Absolute Error MAE and Standard Deviation SD in 83 of cases, converging faster on average 787 iterations than GEO 905 and IGEO3 892 . For multimodal functions, IGEO matched benchmark optima in 31 of cases 5 16 and secured the best results in another 50 , achieving the lowest MAE and SD in 56 of functions and the fastest convergence in 81 13 16 . In engineering design tests, IGEO provided the most cost effective or lightweight solutions in five of six problems, including a 1.4 cost reduction in pressure vessel design, while remaining competitive in the gear train problem with errors on the order of 10 ¹6. These findings demonstrate IGEO's robustness, accuracy, and efficiency for solving complex optimization challenges. Amadou Baldeh | Emmanuel Asuming Frimpong | Francis Boafo Effah | Elvis Twumasi "An Improved Golden Eagle Optimizer Using Chaotic Initialization and Adaptive Weighting for Robust Optimization Performance" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-9 | Issue-6 , December 2025, URL: https://www.ijtsrd.com/papers/ijtsrd97405.pdf Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/97405/an-improved-golden-eagle-optimizer-using-chaotic-initialization-and-adaptive-weighting-for-robust-optimization-performance/amadou-baldeh