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Table no. 3. Quantum rotation gate lookup table [14] Now, evaluate the fitness and store the best solution among the twenty solutions of generation 0, f (b). Now repeat observe, repair, evaluate processes and store the best solution among the twenty solutions of generation 1, f (x). Now compare the corresponding Q-bits of all the parameters of best solution f (b) and Once we determine the number of Q-bits per variable, i.e., in this case of plane turning, the cutting speed v is a parameter which varies from 30 to180 m/min, requires eight Q-bits, but for all the other cutting parameters such as feed rate f, depth of cut d, tool nose radius 7, we need only four Q-bits. Randomly generate population of parameters, in this case population size is 20, and assign equal probabilities to a and B of each Q-bit of every parameter and conduct probabilistic measurement for observed states of Q-bits, by generating a random number and comparing it with | |’. If rand > | BI? consider the Q-bit as 1 otherwise as 0. Now, boundary violations are checked and repaired, if necessary, by using a repair algorithm.

Table 4 no. 3. Quantum rotation gate lookup table [14] Now, evaluate the fitness and store the best solution among the twenty solutions of generation 0, f (b). Now repeat observe, repair, evaluate processes and store the best solution among the twenty solutions of generation 1, f (x). Now compare the corresponding Q-bits of all the parameters of best solution f (b) and Once we determine the number of Q-bits per variable, i.e., in this case of plane turning, the cutting speed v is a parameter which varies from 30 to180 m/min, requires eight Q-bits, but for all the other cutting parameters such as feed rate f, depth of cut d, tool nose radius 7, we need only four Q-bits. Randomly generate population of parameters, in this case population size is 20, and assign equal probabilities to a and B of each Q-bit of every parameter and conduct probabilistic measurement for observed states of Q-bits, by generating a random number and comparing it with | |’. If rand > | BI? consider the Q-bit as 1 otherwise as 0. Now, boundary violations are checked and repaired, if necessary, by using a repair algorithm.

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Table no 1. Boundary conditions of plane turning Where v is the cutting speed (m/min), f is the feed rate (mm/rev), d is the depth of cut (mm), r is the mentioned different machining conditions sixteen
Table no 1. Boundary conditions of plane turning Where v is the cutting speed (m/min), f is the feed rate (mm/rev), d is the depth of cut (mm), r is the mentioned different machining conditions sixteen
305|Page Table no 2 Ranges of cutting parameters
305|Page Table no 2 Ranges of cutting parameters
III. QUANTUM INSPIRED EVOLUTIONARY ALGORITHM Quantum Inspired Evolutionary Algorithm is >ssentially a stochastic population based evolutionary ulgorithm that exploits some principles of quantum mechanics, such as Q-bits, superposition, quantum sates and quantum measurement [12].In conventional AAs, encoding the solutions onto chromosomes uses many different representations, which may be senerally grouped into three classes: symbolic, inary, and numeric. In contrast, a Quantum Inspired Evolutionary Algorithm uses novel probabilistic ‘epresentation called as Q-bit. Q — bit is a smallest init of information that can be in superposition of yasis states in a quantum system. Q- Bits are senerally represented by a vector in Hilbert space with |O> and |1> as basis states. The Q-bit can be ‘epresented as:
III. QUANTUM INSPIRED EVOLUTIONARY ALGORITHM Quantum Inspired Evolutionary Algorithm is >ssentially a stochastic population based evolutionary ulgorithm that exploits some principles of quantum mechanics, such as Q-bits, superposition, quantum sates and quantum measurement [12].In conventional AAs, encoding the solutions onto chromosomes uses many different representations, which may be senerally grouped into three classes: symbolic, inary, and numeric. In contrast, a Quantum Inspired Evolutionary Algorithm uses novel probabilistic ‘epresentation called as Q-bit. Q — bit is a smallest init of information that can be in superposition of yasis states in a quantum system. Q- Bits are senerally represented by a vector in Hilbert space with |O> and |1> as basis states. The Q-bit can be ‘epresented as:
Table no 4. QIEA determined surface roughness
Table no 4. QIEA determined surface roughness
The average surface roughness as determined by QIEA algorithm is presented in Table no 4. The average surface roughness predicted by QIEA is compared with the estimations of real coded genetic algorithm (RCGA) and_ differential evolution algorithm (DEA). The results demonstrates that, QIEA has outperformed RCGA and improved results over DEA in achieving better surface quality, for plane turning, in every given operating environment. The below shown figure no 1, depicts the improved performance of Quantum Inspired Evolutionary Algorithm over Real Coded Genetic Algorithm and Differential Evolution Algorithm Table no 6. QIEA determined optimal feed rate erformance of Quantum Inspired Evolutionary
The average surface roughness as determined by QIEA algorithm is presented in Table no 4. The average surface roughness predicted by QIEA is compared with the estimations of real coded genetic algorithm (RCGA) and_ differential evolution algorithm (DEA). The results demonstrates that, QIEA has outperformed RCGA and improved results over DEA in achieving better surface quality, for plane turning, in every given operating environment. The below shown figure no 1, depicts the improved performance of Quantum Inspired Evolutionary Algorithm over Real Coded Genetic Algorithm and Differential Evolution Algorithm Table no 6. QIEA determined optimal feed rate erformance of Quantum Inspired Evolutionary
Table no 5. QIEA determined optimal cutting speed
Table no 5. QIEA determined optimal cutting speed
Table no 7. QIEA determined optimal depth
Table no 7. QIEA determined optimal depth
Table no 8. QIEA determined nose radius The other two cutting parameters, depth of cut and tool nose radius are presented in the Table no 7 and 8. At minimal settings of these two parameters the improved surface finish is achieved by the algorithm. With help of these results of QIEA estimations of plane turning, it is observed that at higher cutting speeds and at lower feed rate, and at minimal settings of other two parameters, best surface roughness can be achieved.
Table no 8. QIEA determined nose radius The other two cutting parameters, depth of cut and tool nose radius are presented in the Table no 7 and 8. At minimal settings of these two parameters the improved surface finish is achieved by the algorithm. With help of these results of QIEA estimations of plane turning, it is observed that at higher cutting speeds and at lower feed rate, and at minimal settings of other two parameters, best surface roughness can be achieved.
The other two cutting parameters, depth of cut and
The other two cutting parameters, depth of cut and
Figure no2. Standard deviation of DEA vs.QIEA Table no 9. Standard deviation - DEA vs. QIEA
Figure no2. Standard deviation of DEA vs.QIEA Table no 9. Standard deviation - DEA vs. QIEA
Since the performance details of the Real Coded Genetic Algorithm (RCGA) and_ Differential Evolution Algorithm on this plane turning optimization of problem, in terms of its standard deviation, mean of worst and mean of best is not reported [], in this work an effort is made to bring out such results for DEA and compare the same with Quantum Inspired Evolutionary Algorithm The thirty independent experiments carried out on Quantum Inspired Evolutionary Algorithm are compared with the thirty independent runs carried out on Differential Evolution Algorithm (DEA) for the same problem in order to ascertain the performance of QIEA.
Since the performance details of the Real Coded Genetic Algorithm (RCGA) and_ Differential Evolution Algorithm on this plane turning optimization of problem, in terms of its standard deviation, mean of worst and mean of best is not reported [], in this work an effort is made to bring out such results for DEA and compare the same with Quantum Inspired Evolutionary Algorithm The thirty independent experiments carried out on Quantum Inspired Evolutionary Algorithm are compared with the thirty independent runs carried out on Differential Evolution Algorithm (DEA) for the same problem in order to ascertain the performance of QIEA.
Table no 10. Mean of worst and best DEA vs. QIEA
Table no 10. Mean of worst and best DEA vs. QIEA
The comparisons are drawn between QIEA and DEA by presenting the contrasts of different statistical parameters i.e., standard deviation, mean of worst, mean of best. The results of such independent experimental runs of DEA and QIEA are presented in Table no 9 and 10. The following figure no 2, depicts the comparison of standard deviation between Quantum Inspired Evolutionary Algorithm and Differential Evolution Algorithm. The comparisons are drawn between QIEA and DEA
The comparisons are drawn between QIEA and DEA by presenting the contrasts of different statistical parameters i.e., standard deviation, mean of worst, mean of best. The results of such independent experimental runs of DEA and QIEA are presented in Table no 9 and 10. The following figure no 2, depicts the comparison of standard deviation between Quantum Inspired Evolutionary Algorithm and Differential Evolution Algorithm. The comparisons are drawn between QIEA and DEA
V. CONCLUSION
V. CONCLUSION
Related topics:
Computer ScienceSurface Finish
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