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.
Improved Golden Eagle Optimizer (IGEO); chaotic initialization; adaptive weighting; exploration–exploitation balance; benchmark functions; engineering optimization.
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