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Evolutionary Many-Objective Algorithms for Combinatorial Optimization Problems: A Comparative Study
- Publication Year :
- 2020
- Publisher :
- SPRINGER, 2020.
-
Abstract
- © 2020, CIMNE, Barcelona, Spain. Many optimization problems encountered in the real-world have more than two objectives. To address such optimization problems, a number of evolutionary many-objective optimization algorithms were developed recently. In this paper, we tested 18 evolutionary many-objective algorithms against well-known combinatorial optimization problems, including knapsack problem (MOKP), traveling salesman problem (MOTSP), and quadratic assignment problem (mQAP), all up to 10 objectives. Results show that some of the dominance and reference-based algorithms such as non-dominated sort genetic algorithm (NSGA-III), strength Pareto-based evolutionary algorithm based on reference direction (SPEA/R), and Grid-based evolutionary algorithm (GrEA) are promising algorithms to tackle MOKP and MOTSP with 5 and 10 while increasing the number of objectives. Also, the dominance-based algorithms such as MaOEA-DDFC as well as the indicator-based algorithms such as HypE are promising to solve mQAP with 5 and 10 objectives. In contrast, decomposition based algorithms present the best on almost problems at saving time. For example, t-DEA displayed superior performance on MOTSP for up to 10 objectives.
- Subjects :
- Optimization problem
Quadratic assignment problem
Computer science
Applied Mathematics
Evolutionary algorithm
Pareto principle
02 engineering and technology
01 natural sciences
Travelling salesman problem
Computer Science Applications
010101 applied mathematics
01 Mathematical Sciences, 08 Information and Computing Sciences, 09 Engineering
Knapsack problem
Genetic algorithm
0202 electrical engineering, electronic engineering, information engineering
sort
020201 artificial intelligence & image processing
0101 mathematics
Algorithm
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....40b97b04bd4dcf4484a941cf28beaa59