Fuzzy logic controlled genetic algorithms versus tuned genetic algorithms: an agile manufacturing application

Abstract

This paper presents a comparison of the performance of a fuzzy logic controlled genetic algorithm (FLC-GA) and a parameter tuned genetic algorithm (TGA) for an agile manufacturing application. These strategies are benchmarked using a GA that utilizes a canonical static parameter set. In the FLC-GA, fuzzy logic controllers dynamically schedule parameters of the object-level GA. A fuzzy knowledge-base is automatically identified and tuned using a high-level GA. In the TGA, a high-level GA is used to determine an optimal static parameter set for the object-level GA. The object-level GA supports a global evolutionary optimization of design, manufacturing, and supplier planning decisions for manufacturing of printed circuit assemblies in an agile environment. We demonstrate that high-level system identification (for the FLC-G A) or tuning (for the TGA) performed with small object-level search spaces, can be successfully extended to more elaborate object-level search spaces, without employing additional identification or tuning. The TGA performs superior searches, but incurs large search times. The FLC-GA performs faster searches than a TGA, and is slower than the GA that utilizes a canonical static parameter set. However, search quality of the FLC-GA is comparable to that of the GA which utilizes a canonical static parameter set.

Evaluating Reverse Logistics Networks with Centralized Centers: An Adaptive Genetic Algorithm Approach Based on Fuzzy Logic Controller

“…Many conventional studies have developed various adaptive schemes for regulating the rate [16][17][18][19][22][23][24][25][26]. Of them, several adaptive schemes using FLCs have been successfully adopted for improving the performance of GAs [16,18,27]. Gen and Cheng [18] surveyed various adaptive schemes using several FLCs.…”

“…Gen and Cheng [18] surveyed various adaptive schemes using several FLCs. Subbu et al [27] developed a fuzzy logic-controlled genetic algorithm (FLC-GA) using a fuzzy knowledge base. The developed FLC-GA automatically regulates the rates of the crossover and mutation operators.…”

“…Song et al [16] suggested the two FLCs to automatically regulate the rates of the crossover and mutation operators. The suggested two FLCs are used as the input variables of the GA. For successfully applying FLCs to GAs, Subbu et al [27] and Song et al [16] proposed the production of well-formed fuzzy sets and rules. Therefore, the GAs which are controlled by these types of FLCs are more efficient in terms of the search speed and solution quality than the GAs without them [16,18,27].…”

“…International Journal of Intelligent Information Systems 2015; 4(2-1): [25][26][27][28][29][30][31][32][33][34][35][36][37][38] The membership functions of the fuzzy input and output linguistic variables are shown in Figures 8 and 9 Negative larger, NL Negative large, NM Negative medium, NS Negative small, ZE Zero, PS Positive small, PM Positive medium, PL Positive the same as those of the the output is the change in the mutation the crossover and is summarized in Figure 10.…”

“…In Figs 12 and 13, the used products collected from all the customers are sent to the collection center 3, the recovery International Journal of Intelligent Information Systems 2015; 4(2-1): [25][26][27][28][29][30][31][32][33][34][35][36][37][38] formers have adaptive schemes to automatically regulate a balance between exploitation and exploration during genetic search process. The appropriate control of the search process aGA approaches has influence on their performances, that is, the performances of the aGA and aGA approaches are more efficient in terms of the optimal solution than that of the GA approach.…”