International Journal of Artificial Intelligence and Applications for Smart Devices

12

Authors: Ming-Chang Lee, Jui-Fang Chang and Jung-Fang Chen

We study the problem of fuzzy preference relations in group decision making. Group decision problems need all experts express their preferences using the same preference representation format. However, in real practice, this is not always possible because each expert has their unique characteristics which regard to knowledge, skills, experience, and personality, which implies that different experts may express may express their evaluation by means of different preference representation formats. Therefore, we use the order weighted averaging (OWA) operator and order weighted geometric (OWG) operator in the aggregation of group decision making problems. The main contribution of this paper is the easy and extensible solution to group decision problems. Therefore, we studied literatures about O WA OWG and fuzzy preference relation. We results are created the proposed of solving a multi-criteria decision making problem using OWA and OWD operator. Finally, we give an empirical example where we can see the different results obtained by using the OWA and OWG operator in fuzzy preference relations in group decision making.

16

Authors: Zohreh Davarzani and Mohammad-R Akbarzadeh-T

In this study, a hybrid of Quantum Evolutionary and Artificial Immune Algorithms (QIA) is proposed for solving Multiobjective Flexible Job Shop Scheduling Problem (MFJSSP). This problem is formulated as a three-objective problem which minimizes completion time (makespan), critical machine workload and total work load of all machines. The quantum coding is shown to improve the immune strategy. The proposed algorithm overcomes the problem by increasing the speed of convergence and diversity of population. Three benchmarks of Kacem and Brandimart are examined to evaluate the performance of the proposed algorithm. The experimental results show a better performance in comparison to other approaches

14

Authors: Mahnaz Kadkhoda and M. - R. Akbarzadeh-T

With the increasing use of location-sensitive applications, a variety of localization techniques in wireless sensor networks (WSN) have been introduced to date; each aiming to improve accuracy of location estimation. However, most of these studies do not consider the factor of uncertainty which is naturally present in the localization problem. Focusing on range-free localization, this research examines RSS-based location estimation models. Due to environmental conditions, RSS can be influenced by several stochastic factors at the receiving node. In this research, we propose an algorithm using Probabilistic Fuzzy Logic Systems (PFLS) for the first time to address randomness and uncertainty in the location estimation problem. After optimizing PFLS by Genetic Algorithm (GA), the performance of both PFLS and the GA-optimized PFLS are compared with several other competing strategies under varying noise levels. Experimental results clearly indicate the improved efficiency of the proposed algorithm, particularly at higher noise levels.

18

Authors: Amin Allahyar and Hadi Sadoghi Yazdi

In Linear Discriminant Analysis (LDA), it is assumed that each class has a Gaussian distribution. This assumption rarely holds in the real world problems. However, by removing this assumption, the problem become intractable and cannot be solved in analytic form. Quite recently, a group of evolutionary algorithms is introduced to solve this problem. These algorithms used a combination of fisher criterion and fuzzy membership function as their fitness function. It is widely acknowledged that computing the fitness function in an evolutionary algorithm needs to be very fast. Unfortunately, calculating fisher criterion for each chromosome in iterations of an evolutionary algorithm has a high computational cost. Furthermore it is known that the fuzzy membership function has an assumption of Gaussian distribution, thus using it as a fitness function will have same assumption issue that LDA had previously. In this paper, we suggest a new fisher criterion to incorporate in fitness function and show that it is theoretically faster than previous introduced criterion. In addition we theoretically prove the equality of proposed criterion. Next, in order to eliminate the Gaussian assumption, we offer a substitution for fuzzy membership fitness function which does not have Gaussian assumption. Moreover, the superior speed introduced fitness function theoretically investigated. At last, in order to confirm the effectiveness of proposed fitness functions, comprehensive experiments using twelve UCI repository dataset and two real world problems in face and object recognition is performed and the results is compared in both speed and accuracy.