A branch-and-bound algorithm based on NSGAII for multi-objective mixed integer nonlinear optimization problems

• Published in: Engineering optimization Vol. 54; no. 6; pp. 1004 - 1022
• Main Authors: Jaber, A., Lafon, P., Younes, R.
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 03.06.2022; Taylor & Francis Ltd
• Subjects: Algorithms; branch-and-bound algorithm; Engineering Sciences; Genetic algorithms; Heuristic methods; Mechanical engineering; MINLP problems; Mixed integer; mixed variables; Multi-criteria; Multiple criterion; Multiple objective analysis; Nonlinear programming; NSGAII; Optimization; Sorting algorithms
• ISSN: 0305-215X; 1029-0273
• DOI: 10.1080/0305215X.2021.1904918

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.