scholarly journals Multi-objective minmax robust combinatorial optimization with cardinality-constrained uncertainty

2018 ◽  
Vol 267 (2) ◽  
pp. 628-642 ◽  
Author(s):  
Andrea Raith ◽  
Marie Schmidt ◽  
Anita Schöbel ◽  
Lisa Thom

Author(s):  
Rahul Roy ◽  
Satchidananda Dehuri ◽  
Sung Bae Cho

The Combinatorial problems are real world decision making problem with discrete and disjunctive choices. When these decision making problems involve more than one conflicting objective and constraint, it turns the polynomial time problem into NP-hard. Thus, the straight forward approaches to solve multi-objective problems would not give an optimal solution. In such case evolutionary based meta-heuristic approaches are found suitable. In this paper, a novel particle swarm optimization based meta-heuristic algorithm is presented to solve multi-objective combinatorial optimization problems. Here a mapping method is considered to convert the binary and discrete values (solution encoded as particles) to a continuous domain and update it using the velocity and position update equation of particle swarm optimization to find new set of solutions in continuous domain and demap it to discrete values. The performance of the algorithm is compared with other evolutionary strategy like SPEA and NSGA-II on pseudo-Boolean discrete problems and multi-objective 0/1 knapsack problem. The experimental results confirmed the better performance of combinatorial particle swarm optimization algorithm.



Author(s):  
Naiyu Tian ◽  
Dantong Ouyang ◽  
Yiyuan Wang ◽  
Yimou Hou ◽  
Liming Zhang


SIMULATION ◽  
2013 ◽  
Vol 90 (2) ◽  
pp. 182-204 ◽  
Author(s):  
F Tao ◽  
Y J Laili ◽  
L Zhang ◽  
Z H Zhang ◽  
AY C Nee


2016 ◽  
Vol 163 (1-2) ◽  
pp. 1-23 ◽  
Author(s):  
Christoph Buchheim ◽  
Jannis Kurtz


2018 ◽  
Vol 6 (3) ◽  
pp. 207-209
Author(s):  
Arie M. C. A. Koster ◽  
Michael Poss


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