Dear Janak,

There are many multi - objective optimization techniques are available to solve your problem. As you don't have much of the experimental run you can't use Genetic Algorithm, simualated annealing, PSO, so as per my knowledge you can use Grey relational analysis or principal component analysis etc.

Good wishes

Hello Reddy Sir,

Thank-you very much for your suggestions

Regards

Janak

Dear Janak Sir,

For the publication of research manuscript in good and high impact factor journals, novelty and advancement of the research area is preferred as compared to optimization techniques. There are number of multi-parametric and hybrid optimization techniques are availablesuch as Grey relational analysis (GRA), PCA, MOORA, TLBO etc.

Some of them is more effective (for solving linear/non linear problem) compared to other. you can use any one of them as per your requirements.

The list of optimization techniques reported in literatures are given below:

1. Ant colony optimization (ACO) ; I Dorigo and Stutzle (2004)

2. Artificial immune system optimization; Cutello and Nicosia (2002)

3. Bacterial foraging optimization ; Kim, Abraham and Cho (2007)

4. Bee optimization ; Karaboga and Bosturk (2007) Pham et al (2006)

5. Cuckoo algorithm ; Yang and Deb (2009, 2010)

6. Differential evolution (DE) ; Storn and Price (1995, 1997)

7. Firefly optimization ; Yang (2010)

8. Fish optimization ; Huang and Zhou (2008)

9.Genetic algorithms (GA) ;Haupt and Haupt (2004)

10.Particle swarm optimization (PSO), Binary Particle Swarm Optimization (BPSO); Eberhart and Kennedy (1995)

11.Raindrop optimization ; Shah-Hosseini (2009)

12.Simulated annealing ; Kirkpatrick, Gelatt and Vecchi (1983) 13.Biogeography-based optimization (BBO),

14. Chemical reaction optimization (CRO)

15. A group search optimizer (GSO),

16. Imperialist algorithm

17. Swine flow Optimization Algorithm.

18. Teaching Learning Based Optimization(TLBO)

19. Bayesian Optimization Algorithms (BOA)

20. Population-based incremental learning (PBIL)

21. Evolution strategy with covariance matrix adaptation (CMA-ES)

22. Charged system search Optimization Algorithm

23. Continuous scatter search (CSS) Optimization Algorithm

24. Tabu search Continuous Optimization

25. Evolutionary programming

26. League championship algorithm

27. Harmony search Optimization algorithm

28. Gravitational search algorithm Optimization

29. Evolution strategies Optimization

30. Firework algorithm, Ying Tan, 2010

31. Big-bang big-crunch Optimization algorithm, OK Erol, 2006

32. Artificial bee colony optimization (ABC), Karaboga,2005 33. Backtracking Search Optimization algorithm (BSA)

34. Differential Search Algorithm (DSA) (A modernized particle swarm optimization algorithm)

35. Hybrid Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA)

36. Multi-objective bat algorithm (MOBA) Binary Bat Algorithm (BBA)

37. Flower Pollination Algorithm 38. The Wind Driven Optimization (WDO) algorithm

39. Grey Wolf Optimizer (GWO)

40. Generative Algorithms

41. Hybrid Differential Evolution Algorithm With Adaptive Crossover Mechanism

42.Lloyd's Algorithm

43.One Rank Cuckoo Search (ORCS) algorithm: An improved cuckoo search optimization algorithm

44. Huffman Algorithm

45. Active-Set Algorithm (ASA)

46. Random Search Algorithm

47. Alternating Conditional Expectation algorithm (ACE)

48. Normalized Normal Constraint (NNC) algorithm

There are many other optimization algorithm recently invented.

Thankyou very much Sandeep sir , for your valuable suggestions. It will be definitely help me alot.

Regards

Janak

I do not think that only multi-objective method will help you to get your work published in good journal. It is only one of aspects only. Main contributions in terms of new knowledge and/or method to the field. especial

Yes , patil sir ,

But i have Multi-objective problem on drilling process, for solve that problem i want some suggestions which i got in earlier answer.

Nd thank-you for your response patil sir