A branch-and-bound algorithm based on NSGAII for multi-objective mixed integer nonlinear optimization problems. Issue 6 (3rd June 2022)

• Record Type: Journal Article
• Authors: Jaber, A.; Lafon, P.; Younes, R.
• Is Part Of: Engineering optimization. Volume 54:Issue 6(2022)
• Page Start: 1004
• Page End: 1022
• Publication Date: 2022-06-03
• Subjects: Multi-criteria -- branch-and-bound algorithm -- NSGAII -- MINLP problems -- mixed variables; Engineering design -- Periodicals; Mathematical optimization -- Periodicals; 620.0042
• DOI: 10.1080/0305215X.2021.1904918
• Languages: English
• ISSNs: 0305-215X

Abstract

Abstract : Solving Multi-Objective Mixed Integer NonLinear Programming (MO-MINLP) problems is a point of interest for many researchers as they appear in several real-world applications, especially in the mechanical engineering field. Many researchers have proposed using hybrids of metaheuristics with mono-objective branch and bound. Others have suggested using heuristics with Multi-Criteria Branch and Bound (MCBB). A general hybrid approach is proposed based on MCBB and Non-dominated Sorting Genetic Algorithm 2 (NSGAII) to enhance the approximated Pareto front of MO-MINLP problems. A computational experiment based on statistical assessment is presented to compare the performance of the proposed algorithm (BnB-NSGAII) with NSGAII using well-known metrics from the literature. To evaluate the computational efficiency, a new metric, the Investment Ratio (IR), is proposed that relates the quality of solution to the consumed effort. Experimental results on five real-world mechanical engineering problems and two mathematical ones indicate that BnB-NSGAII could be a competitive alternative for solving MO-MINLP problems.