A solution to statistical and multidisciplinary design optimization problems using hGWO-SA algorithm

• Published in: Neural computing & applications Vol. 33; no. 8; pp. 3799 - 3824
• Main Authors: Bhadoria, Ashutosh, Marwaha, Sanjay, Kamboj, Vikram Kumar
• Format: Journal Article
• Language: English
• Published: London Springer London 01.04.2021; Springer Nature B.V
• Subjects: Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Design engineering; Design optimization; Exploitation; Heuristic methods; Image Processing and Computer Vision; Multidisciplinary design optimization; Optimization algorithms; Original Article; Probability and Statistics in Computer Science; Search algorithms; Simulated annealing; Trigonometric functions; Grey wolf optimizer; Meta-heuristic search; Benchmark test functions; Engineering optimization
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-020-05229-3

Recently developed grey wolf optimizer (GWO) algorithm has evident behaviour for verdict of global optima, without getting ensnared in premature convergence. However, the exploitation phase of the existing grey wolf optimizer is underprivileged. In the proposed research, a hybrid version of grey wolf optimizer algorithm combined with simulated annealing (named as hGWO-SA) algorithm has been developed for the solution of various nonlinear, highly constrained, non-convex engineering design and optimization problems. In the proposed research, the exploitation phase of the existing grey wolf optimizer has been further enhanced using simulated annealing algorithm, which improves the local search capability of the existing grey wolf optimizer. In order to indorse the results of the proposed algorithm, 65 benchmark problems including CEC2017, CEC2018 and five multidisciplinary design optimization problems are taken into consideration. Experimentally, it has been found that the results of the proposed hybrid GWO-SA algorithm are better than standard grey wolf optimizer algorithm, ant lion optimizer algorithm, moth–flame optimization algorithm, sine–cosine optimization algorithm and other recently reported heuristics, meta-heuristic and hybrid search algorithm and the proposed algorithm indorses its effectiveness in the field of nature-inspired meta-heuristic algorithms.