Dear Aschkan Omidvar,

Please see the following papers and links.

- An approach to sensitivity analysis of computer models, Part 1. Introduction, input variable selection and preliminary variable assessment.

- FRIESEN, DK. "Sensitivity analysis for heuristic algorithms[Ph. D. Thesis]." (1978).

http://dl.acm.org/citation.cfm?id=908914

http://www.sciencedirect.com/science/article/pii/S0377221797001045

http://www.scielo.mec.pt/pdf/iop/v24n2/v24n2a07.pdf

Dear Aschkan Omidvar

I have did sensitivity analysis of five metaheuristic optimization algorithms in terms of the Initial Condition and algorithm parameters. I have observed that metaheuristics are highly sensitive to both  the initial conditions and the algorithm parameters.

https://www.researchgate.net/publication/280244004_A_comparative_study_of_metaheuristic_optimization_approaches_for_directional_overcurrent_relays_coordination

I have considered the same parameters and run the algorithm 100 times by changing the initial conditions in each run. In each run the algorithms have been guided by different seeds of random numbers as a results, in each run the results were different.

Also, I have considered the same Initial Conditions and run the algorithm 100 times by changing parameters. In each run the algorithms have been guided by different seeds of random numbers as a results, in each run the results were different.

My final conclusion is:

All the metaheuristics are highly sensitive to the initial conditions as well as the algorithm parameters.

Article A comparative study of metaheuristic optimization approaches...

You can check the work on opposition-based initialization and opposition-based jumping in general.

M. Ventresca, S. Rahnamayan, H.R. Tizhoosh, “A Note on Opposition versus Randomness in Soft Computing Techniques,” Journal on Applied Soft Computing - Elsevier, Vol. 10, Mar. 2010, pp. 956-957.

S. Rahnamayan, H.R. Tizhoosh, M.M.A Salama, “Opposition versus Randomness in Soft Computing Techniques,” Journal on Applied Soft Computing - Elsevier, Volume 8, Mar. 2008, pp. 906-918.

Kindly, go through the following publication.

Y. del Valle, G. Venayagamoorthy, S. Mohagheghi, J.-C. Hernandez, R. Harley, Particle swarm optimization: Basic concepts, variants and applications in power systems, IEEE Transactions on Evolutionary Computation, 12 (2) (2008) 171-195.

I hope it will be very helpful to you.

codes are available here;

https://www.researchgate.net/publication/296636431_Codes_in_MATLAB_for_Particle_Swarm_Optimization?ev=prf_pub

Regards,

M.N.Alam

Code Codes in MATLAB for Particle Swarm Optimization

https://www.researchgate.net/publication/297245624_Particle_Swarm_Optimization_Algorithm_and_its_Codes_in_MATLAB

Research Particle Swarm Optimization: Algorithm and its Codes in MATLAB

you can check the following paper:

Amini, M. M., Racer, M., & Ghandforoush, P. (1998). Heuristic sensitivity analysis in a combinatoric environment: An exposition and case study. European journal of operational research, 108(3), 604-617.