Studying the effect of using low-discrepancy sequences to initialize population-based optimization algorithms

Author:

Omran Mahamed G. H.,al-Sharhan Salah,Salman Ayed,Clerc Maurice

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Computational Mathematics,Control and Optimization

Reference31 articles.

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3. Brest, J., Greiner, S., Boskovic, B., Mernik, M., Zumer, V.: Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans. Evol. Comput. 10(6), 646–657 (2006)

4. Brits, R., Engelbrecht, A.P., van den Bergh, F.: A niching particle swarm optimizer. In: Proceedings of the Fourth Asia Pacific Conference on Simulated Evolution and Learning, pp. 692–696 (2002)

5. Das, S., Suganthan, P.: Problem definitions and evaluation criteria for CEC 2011 competition on testing evolutionary algorithms on real world optimization problems. Technical report, Jadavpur University, Nanyang Technological University (2010)

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