MOGBO: A new Multiobjective Gradient-Based Optimizer for real-world structural optimization problems

M. Premkumar

doi:10.1016/j.knosys.2021.106856

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MOGBO: A new Multiobjective Gradient-Based Optimizer for real-world structural optimization problems

2021-04-01

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M. Premkumar *

Pradeep Jangir

R. Sowmya

DOI：10.1016/j.knosys.2021.106856

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Abstract

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To handle the multiobjective optimization problems of truss-bar design, this paper introduces a new metaheuristic multiobjective optimization algorithm. The proposed algorithm is based on a recently reported single objective version of the gradient-based optimizer (GBO) inspired by the gradients of Newton's equations. The proposed algorithm is called as multiobjective gradient-based optimizer (MOGBO), in which two operators, such as local escaping operator and gradient search rule, and few vector sets are utilized in the search phase and the elitist non-dominated sorting mechanism is used for agent sorting to find Pareto optimal solutions. The proposed MOGBO is a posteriori method, and the traditional crowding distance mechanism is employed to confirm the coverage of the best solutions for the objectives of the given problem. The performance of the proposed MOGBO algorithm is verified and validated on different test cases, including 15 unconstraint benchmark test suites and eight constraint multiobjective truss bar design problems. To prove the superiority of the MOGBO algorithm, the performance is compared with state-of-the-art algorithms, such as multiobjective ant lion optimization (MOALO), multiobjective water cycle algorithm (MOWCA), multiobjective colliding bodies optimization (MOCBO), and non-dominated sorting gray wolf optimizer (NSGWO) in terms of metrics, such as hyper-volume, coverage, inverted generational distance, pure diversity, Spacing, Spread, coverage Pareto front, diversity maintenance, generational distance, and runtime. The solutions obtained by the proposed MOGBO algorithm is highly accurate and requires less runtime than the other selected algorithms. The obtained results also show the efficiency of the MOGBO in solving most of all the complex multiobjective problems. This research will be further backed up with external guidance for the future research at https://premkumarmanoharan.wixsite.com/mysite. (c) 2021 Elsevier B.V. All rights reserved.

Keywords:

Crowding distance

Gradient-based optimizer (GBO)

Multiobjective Gradient-Based Optimizer (MOGBO)

Multiobjective problems

Non-dominated sorting

Structural design

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