A solution to statistical and multidisciplinary design optimization problems using hGWO-SA algorithm
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ORIGINAL ARTICLE
A solution to statistical and multidisciplinary design optimization problems using hGWO-SA algorithm Ashutosh Bhadoria3 • Sanjay Marwaha1 • Vikram Kumar Kamboj2 Received: 29 October 2019 / Accepted: 23 July 2020 Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract 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. Keywords Benchmark test functions Engineering optimization Grey wolf optimizer Meta-heuristic search
1 Introduction Multidisciplinary design optimization and system design optimization are emerging area for the solution of design and optimization problems incorporating a number of disciplines. In recent years, with the advancement in technology a new era of problem solving methods are emerging making use of computers, which are becoming common approach for solving complex problems. The problem solving methods with direct human involvement are sluggish, so computer-aided design is widely adopted emphasizing on use of computer for design problems. The computer-aided design not only & Vikram Kumar Kamboj [email protected] 1
Sant Longowal Institute of Engineering and Technology, Longowal, Sangrur, India
2
Schulich School of Engineering, University of Calgary, Alberta, Canada
3
Sant Longowal Institute of Engineering and Technology, Sangrur, Punjab, India
emphasizes the simulating a system, but also plans optimal design with high accuracy, low cost, high speed and reliability. Optimization algorithms are considered to be one of the best tools for solving engineering problems and to find the optimal results. These approaches consider the problem as black box and find the optimal solution. The optimization p
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