InterCriteria Analysis of ACO and GA Hybrid Algorithms

Abstract

In this paper, the recently proposed approach for multicriteria decision making-InterCriteria Analysis (ICA)-is presented. The approach is based on the apparatus of the index matrices and the intuitionistic fuzzy sets. The idea of InterCriteria Analysis is applied to establish the relations and dependencies of considered parameters based on different criteria referred to various metaheuristic algorithms. A hybrid scheme using Genetic Algorithm (GA) and Ant Colony Optimization (ACO) is used for parameter identification of E. coli MC4110 fed-batch cultivation process model. In the hybrid GA-ACO, the GA is used to find feasible solutions to the considered optimization problem. Further ACO exploits the information gathered by GA. This process obtains a solution, which is at least as good as-but usually better than-the best solution devised by GA. Moreover, a comparison with both the conventional GA and ACO identification results is presented. Based on ICA the obtained results are examined and conclusions about existing relations and dependencies between model parameters of the E. coli process and algorithms parameters and outcomes, such as number of individuals, number of generations, value of the objective function and computational time, are discussed.

A Hybrid Approach for Modular Neural Network Design Using Intercriteria Analysis and Intuitionistic Fuzzy Logic

“…In this paper, we introduce a hybrid combination between the intercriteria analysis (ICA, see [13][14][15][16][17][18]) method and modular neural network models. The ICA employs the apparatus of the intuitionistic fuzzy sets (IFS) for detecting possible correlations between pairs of criteria.…”

Two Applications of Inter-Criteria Analysis with Belief Functions

“…This permits to remove all redundant criteria of the original MCDM problem and thus solving a simplified (almost) equivalent MCDM problem faster and at lower computational cost. ICrA has been developed originally by A t a n a s s o v, M a v r o v and A t a n a s s o v a [9], A t a n a s s o v, A t a n a s s o v a and G l u h c h e v [10] and A t a n a s s o v et al [11], based on Intuitionistic Fuzzy Sets [12], and it has been applied in different fields like medicine [13][14][15], optimization [16][17][18][19][20] workforce planning [21], competitiveness analysis [22], radar detection [23], ranking [24][25][26][27], etc. In this paper we improve ICrA approach thanks to belief functions introduced by Shafer in [28] from original Dempster's works [29].…”

Inter-Criteria Analysis Based on Belief Functions for GPS Surveying Problems

“…Such analysis can permit (when possible) to reduce the complexity of large multiple criteria decision-making (MCDM) problems [2]. Until now the classical 3 ICrA has been applied in different fields: medicine [16], [17], optimization [18]- [21], workforce planning [22], competitiveness analysis [23], radar detection [24], ranking [25]- [27], etc. In this section we just recall the basic principles of classical ICrA.…”