TY - JOUR ID - 133423 TI - A Hybrid Generalized Reduced Gradient-Based Particle Swarm Optimizer for Constrained Engineering Optimization Problems JO - Journal of Soft Computing in Civil Engineering JA - SCCE LA - en SN - AU - Varaee, Hesam AU - Safaeian Hamzehkolaei, Naser AU - Safari, Mahsa AD - Assistant Professor, Department of Civil Engineering, Ale Taha Institute of Higher Education, Tehran, Iran AD - Assistant Professor, Department of Civil Engineering, Bozorgmehr University of Qaenat, Qaen, Iran AD - MSc, Department of Civil Engineering, Ale Taha Institute of Higher Education, Tehran, Iran Y1 - 2021 PY - 2021 VL - 5 IS - 2 SP - 86 EP - 119 KW - Hybrid global-local search engine KW - Particle Swarm Optimization (PSO) KW - Generalized reduced gradient (GRG) algorithm KW - k-nearest neighbors (k-NN) algorithm KW - Purely uniform distributed swarm DO - 10.22115/scce.2021.282360.1304 N2 - A hybrid algorithm is presented that combines strong points of Particle Swarm Optimization (PSO) and Generalized Reduced Gradient (GRG) algorithm to keep a good compromise between exploration and exploitation. The hybrid PSO-GRG quickly approximates the optimum solution using PSO as a global search engine in the first phase of the search process. The solution accuracy is then improved during the second phase of the search process using the GRG algorithm to probe locally for a proper solution(s) in the vicinity of the current best position obtained by PSO. The k-nearest neighbors (k-NN)-based Purely Uniform Distributed (PUD) initial swarm is also applied to increase the convergence speed and reduce the number of function evaluations (NFEs). Hybridization between both algorithms allows the proposed algorithm to accelerate throughout the early stages of optimization using the high exploration power of PSO whereas, promising solutions will possess a high probability to be exploited in the second phase of optimization using the high exploitation ability of GRG. This prevents PUD-based hybrid PSO-GRG from becoming trapped in local optima while maintaining a balance between exploration and exploitation. The competence of the algorithm is compared with other state-of-the-art algorithms on benchmark optimization problems having a wide range of dimensions and varied complexities. Appraising offered algorithm performance revealed great competitive results on the Multiple Comparison Test (MCT) and Analysis of Variance (ANOVA) test. Results demonstrate the superiority of hybrid PSO-GRG compared to standard PSO in terms of fewer NFEs, fast convergence speed, and high escaping ability from local optima. UR - https://www.jsoftcivil.com/article_133423.html L1 - https://www.jsoftcivil.com/article_133423_56bc12f498d3ee4f3bf36a86e367074c.pdf ER -