Global 0ptimization of pairwise comparisons matrix based on differential evolution algorithm

Pairwise comparisons matrices (PCM) are commonly used when different entities or abstract concepts are compared for making decisions. The compared entities for decision making can be both subjective and objective indicators. The elements in PCM are ratios in case of multiplicative version. Using differential evolution (DE) heuristic algorithm, we can find an optimal solution for a given PCM. The optimization results are fairly good with geometric mean (GM) and eigenvector (EV). The thesis provides an introduction of differential evolution and how to apply it to the global optimization of PC matrices. IDEs and Java/R packages used in the Monte Carlo experiment will be discussed. Some results of considerable importance for PC matrices that have been obtained will also be illustrated in the thesis.

Name thesis:degree_name

Master of Science (MSc) in Computational Sciences

Grantor dc:publisher

Laurentian University of Sudbury

Year dc:date.issued

2019

Author dc:creator

• Duan, Yuqing

• Pairwise comparisons matrices
• differential evolution heuristic
• optmization
• geometric mean
• eigenvector
• Monte Carlo experiment
• Java, R.

Language dc:language.iso

en

Repository record dc:identifier.uri

https://laurentian.scholaris.ca/handle/10219/3552

OAI record oai:identifier

https://laurentian.scholaris.ca/server/oai/request?verb=GetRecord&metadataPrefix=dim&identifier=oai%3Alaurentian.scholaris.ca%3A10219%2F3552