S. Kazemzadeh Azad, O. Hasançebi, O. K. Erol,
Volume 1, Issue 3 (9-2011)
Abstract
Engineering optimization needs easy-to-use and efficient optimization tools that can be employed for practical purposes. In this context, stochastic search techniques have good reputation and wide acceptability as being powerful tools for solving complex engineering optimization problems. However, increased complexity of some metaheuristic algorithms sometimes makes it difficult for engineers to utilize such techniques in their applications. Big- Bang Big-Crunch (BB-BC) algorithm is a simple metaheuristic optimization method emerged from the Big Bang and Big Crunch theories of the universe evolution. The present study is an attempt to evaluate the efficiency of this algorithm in solving engineering optimization problems. The performance of the algorithm is investigated through various benchmark examples that have different features. The obtained results reveal the efficiency and robustness of the BB-BC algorithm in finding promising solutions for engineering optimization problems.
T. Payamifar, R. Sojoudizadeh, H. Azizian, L. Rahimi,
Volume 15, Issue 4 (11-2025)
Abstract
This paper presents an Enhanced Prairie Dog Optimization (IPDO) algorithm for solving complex engineering optimization problems. The proposed improvement integrates Lévy flight dynamics into the original PDO framework to enhance exploration-exploitation balance and accelerate convergence. The performance of IPDO is evaluated against seven established metaheuristics across four challenging civil engineering applications: (1) discrete sizing optimization of a 120-bar truss, (2) structural reliability analysis of a cantilever tube, (3) cost optimization of reinforced concrete beams, and (4) hyperparameter tuning of a Support Vector Machine (SVM) for shear strength prediction of steel fiber-reinforced concrete. Experimental results demonstrate that IPDO consistently achieves superior solution quality, robustness, and convergence speed. Notably, in SVM hyperparameter optimization, IPDO attained the lowest mean squared error (1.4881) with zero variance across runs, outperforming all competitors. The algorithm also proved highly effective in structural design and reliability problems, offering a reliable and efficient tool for real-world engineering optimization.
