Abstract
This paper presents a novel hybrid algorithm by integrating particle swarm optimization with stochastic ranking for solving standard constrained engineering design problems. The proposed hybrid algorithm uses domain independent characteristics of stochastic ranking and faster convergence of particle swarm optimization. Performance comparison of the proposed algorithm with other popular techniques through comprehensive experimental investigations establishes the effectiveness and robustness of the proposed algorithm for solving engineering design problems.
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Sabat, S.L., Ali, L., Udgata, S.K. (2010). Stochastic Ranking Particle Swarm Optimization for Constrained Engineering Design Problems. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Dash, S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2010. Lecture Notes in Computer Science, vol 6466. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17563-3_79
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DOI: https://doi.org/10.1007/978-3-642-17563-3_79
Publisher Name: Springer, Berlin, Heidelberg
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