**4. Genetic Algorithm (GA)**

Genetic Algorithm is a similar approximation method as survival of the fittest. This nature-inspired metaheuristic process follows some fundamental rules [12].


Each fitness functions are considered as individual chromosome and they are the various sequence of binary alphabets (1 and 0). This algorithm is very useful to find out the global solution to a problem set.

primary objective is to achieve maximum MRR. It can be used for rough cutting. In another case for finishing operation, more emphasis should be given on TWR instead of MRR. In this condition, the tool wear can highly affect the final geometry of the product. As the genetic algorithm is generally subjected to minimizing the function, so in the case of maximizing the MRR the negative sign have been neglected. In **Figure 10** the two contradictory objectives are simultaneously optimized by using GA have been plotted. From this plot, the boundary condition for

*Nature Inspired Metaheuristic Approach for Best Tool Work Combination for EDM Process*

*DOI: http://dx.doi.org/10.5772/intechopen.96725*

**Current (Amp)**

**POT (μSec)**

1 4.5 15.90 88.58 864.64 101.16 1045.33 1216.031 3 16.4 14.82 89.36 771.19 198.54 1436.91 149.4018 4 0.2 9.78 87.15 859.08 126.59 1100.34 961.2881 5 15.7 14.17 86.24 783.69 199.16 1437.78 149.4643 6 15.6 11.71 88.01 774.51 194.3 1167.08 185.9447 7 2.2 7.04 86.72 826.90 158.6 1074.93 1057.663 8 4.5 2.85 88.46 819.28 151.4 1201.48 827.6156 9 2.5 5.09 88.50 828.50 102.9 1145.31 683.333 10 6.3 0.67 88.23 800.79 119.1 1233.71 531.6255 11 2.1 12.68 88.31 849.67 123.3 1070.92 1178.077 12 6.0 1.13 88.41 828.16 150.9 1154.8 633.3167 13 4.7 1.85 88.44 814.21 158.9 1305.47 875.2726 14 1.1 11.47 88.40 860.27 129.6 1072.24 1070.531 15 3.7 14.69 88.50 863.51 101.2 1049.99 1138.689 16 11.2 5.36 88.51 786.64 175.8 1111.97 461.5821 17 1.2 8.81 88.59 851.90 158.6 1074.93 1057.913

**Machining Time (min)** **MRR (cm<sup>3</sup> / min)**

**TWR (gm/min)**

**Figure 10.** *Plot functions for GA.*

> **Exp. No**

**Table 2.**

**145**

*Combination of factors and responses.*

**Tool Density (g/cm<sup>3</sup> )**

**W/P Density (g/cm<sup>3</sup> )**

On average, better new generations are formed with better genes. Every successive generation will have a 'partial better solution' than the previous generations. Ultimately, when the newly created offspring does not have a noticeable difference from the previous generations, then the algorithm will terminate at a converged solution.

### **4.1 Results using the Genetic algorithm**

It has been aimed to find out the single parametric combination for this contradictory parameters by using multi-response optimization. In order to find out the two contradictory parameters like high MRR and low TWR. The boundary condition for this genetic algorithm is used as follows:

• Population

Population type: Double vector

• Stopping Criteria

Generations: 100 x number of variables


Here the total number of iterations required for optimization is 127 and which gives 16 combinations for the control parameters along with responses. Optimization terminated as the average change in the spread of Pareto solutions has been reached to its tolerance value. Based on the conflicting nature of the objectives, multi-response optimization is carried out in order to achieve the goal by a single parametric combination. As the EDM process is complex machining, it can have two general situations while it is used for commercial purposes. In one case the

*Nature Inspired Metaheuristic Approach for Best Tool Work Combination for EDM Process DOI: http://dx.doi.org/10.5772/intechopen.96725*

#### **Figure 10.** *Plot functions for GA.*

R for validation and testing both has the values more than 0.9 the training shows a

Genetic Algorithm is a similar approximation method as survival of the fittest. This nature-inspired metaheuristic process follows some fundamental rules [12].

• A good individual will reproduce again and again and will survive in nature for

Each fitness functions are considered as individual chromosome and they are the various sequence of binary alphabets (1 and 0). This algorithm is very useful to find

On average, better new generations are formed with better genes. Every successive generation will have a 'partial better solution' than the previous generations. Ultimately, when the newly created offspring does not have a noticeable difference from the previous generations, then the algorithm will terminate at a converged

It has been aimed to find out the single parametric combination for this contradictory parameters by using multi-response optimization. In order to find out the two contradictory parameters like high MRR and low TWR. The boundary

Here the total number of iterations required for optimization is 127 and which gives 16 combinations for the control parameters along with responses. Optimization terminated as the average change in the spread of Pareto solutions has been reached to its tolerance value. Based on the conflicting nature of the objectives, multi-response optimization is carried out in order to achieve the goal by a single parametric combination. As the EDM process is complex machining, it can have two general situations while it is used for commercial purposes. In one case the

• Every living being in any ecosystem struggle for food and mates.

a long time than those week individuals.

*Computational Optimization Techniques and Applications*

out the global solution to a problem set.

**4.1 Results using the Genetic algorithm**

Population type: Double vector

Generations: 100 x number of variables

condition for this genetic algorithm is used as follows:

• Those victorious individuals will compete and fittest offspring will be produced. Those individuals also are known as 'King' in the system.

good result.

solution.

• Population

**144**

• Stopping Criteria

Time limit: infinity Stall generations: 100

Function tolerance: 1x10�<sup>4</sup> Constrain tolerance: 1x10�<sup>3</sup>

**4. Genetic Algorithm (GA)**

primary objective is to achieve maximum MRR. It can be used for rough cutting. In another case for finishing operation, more emphasis should be given on TWR instead of MRR. In this condition, the tool wear can highly affect the final geometry of the product. As the genetic algorithm is generally subjected to minimizing the function, so in the case of maximizing the MRR the negative sign have been neglected. In **Figure 10** the two contradictory objectives are simultaneously optimized by using GA have been plotted. From this plot, the boundary condition for

