Cohort Intelligence and Genetic Algorithm Along with Modified Analytical Hierarchy Process to Recommend an Ice Cream to Diabetic Patient

Author(s):  
Suhas Machhindra Gaikwad ◽  
Rahul Raghvendra Joshi ◽  
Anand J. Kulkarni
Compiler ◽  
2015 ◽  
Vol 4 (2) ◽  
Author(s):  
Yuliani Indrianingsih ◽  
Winda Susyanti Naibaho

Samosir regency is a from North Sumatera province which dominantly people is live on agriculture. The most of the farmers using autodidact learning to determining the types of plants that will be process the appropriate with their land, where farmers just learn from their experiences and exchange opinion with another farmers. Based of the problem, the system will be built by Analytical Hierarchy Process (AHP) with Genetic Algorithm. The result of the testing from method Analytical Hierarchy Process (AHP) obtained result with consistency ratio values < 0,1, which is -0,5936 while the result of the testing from Genetic Algorithm obtained value of the fitness is 157,25. Based on the test results, it can be concluded that the method Analytical Hierarchy Process (AHP) obtained the result more consistent with the value of consistency ratio should be < 0,1, so it will be easier to making decision, while on the analysis of Genetic Algorithm, obtained result are probabilistic as always having random process, so that decision making can not be consistent. So, Decision Support System to determination of plants based by contents of soil which are good and suitable is method Analytical Hierarchy Process (AHP).


2019 ◽  
Vol 8 (3) ◽  
pp. 1996-2002

Teaching-learning based optimization (TLBO), biogeography-based optimization (BBO) and fuzzy multiobjective linear programming (FMOLP) are compared in this paper for portfolio optimization. A hybrid approach has been adopted for this comparative study which is a combination of a few methods, such as investor topology, cluster analysis, analytical hierarchy process (AHP) and optimization techniques. Return, risk, liquidity, coefficient of variation (CV) and AHP weighted scores are used as the objective function for optimization.


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