Multiobjecitve structural optimization using improved heat transfer search

• Published in: Knowledge-based systems 2021-05, Vol.219, p.106811, Article 106811
• Main Authors: Kumar, Sumit, Tejani, Ghanshyam G., Pholdee, Nantiwat, Bureerat, Sujin
• Format: Article
• Language: English
• Subjects: Algorithms; Design modifications; Design optimization; Discrete design; Heat transfer; Meta-heuristics; Modified binomial crossover; Multiobjective problem; Multiple objective analysis; Rank tests; Statistical analysis; Structural design; Structural optimization; Thermal stability; Truss design; Weight reduction
• ISSN: 0950-7051; 1872-7409
• DOI: 10.1016/j.knosys.2021.106811

The present study proposed a novel and effective optimizer for multiobjective structural optimization problems termed multiobjective heat transfer search with modified binomial crossover (MOHTS-BX). A novel reproduction phase called a modified binomial crossover mode based on modified binomial crossover has been introduced to the basic HTS optimizer to enhance its global diversification and local intensification. The MOHTS-BX optimizer works on the thermodynamic principles in which system molecules (corresponds to design solutions) exchange energy within its molecules and concurrently with the molecules of surrounding (treated as the best solution) to accomplish thermal stability. This energy trade is performed through four phases namely conduction, convection, radiation, and modified binomial crossover modes. Seven benchmark structural design problems have been explored with the MOHTS-BX optimizer to examine its fitness and efficacy. For viability, discrete design variables are accounted with the objectives of structure weight reduction and maximization of nodal displacements. To exemplify the efficacy and pertinence of the proposed MOHTS-BX algorithm, four multiobjective mechanical design optimization problems, and the CF test problems from the CEC2009 competition were also accounted. The outcomes of the proposed optimizer are compared with four other distinguished multiobjective optimizers while the performance is validated by some indicators i.e. Pareto front-Hypervolume, Front Spacing-to-Extent, Inverted Generational Distance, etc. Findings show that MOHTS-BX can effectively yield a set of non-dominated solutions. Friedman’s rank test is applied for the experiment work statistical analysis. The conclusion drawn elucidates the superiority of the proposed optimizer to the others. •The proposed MOHTS-BX is inspired by thermal equilibrium principle.•A modified binomial crossover phase is added to the basic MOHTS algorithm.•Five challenging Multi-objective structural design problems considered.•Hypervolume and Spacing-to-Extant indicators are used for performance evaluation.•Friedman’s rank test confirms MOHTS-BX efficiency, especially for large structures.