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

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-obje...

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Veröffentlicht in:Engineering optimization Jg. 54; H. 6; S. 1004 - 1022
Hauptverfasser: Jaber, A., Lafon, P., Younes, R.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Abingdon Taylor & Francis 03.06.2022
Taylor & Francis Ltd
Schlagworte:
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
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Zusammenfassung: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.
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ISSN:0305-215X
1029-0273
DOI:10.1080/0305215X.2021.1904918