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

DOI10.1007/s10589-013-9559-2zbMath1315.90031OpenAlexW1986459292MaRDI QIDQ377716

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

Publication date: 7 November 2013

Published in: Computational Optimization and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10589-013-9559-2

zbMATH Keywords

pseudo-random sequencesquasi-random sequenceslow-discrepancy sequencesdifferential evolutionparticle swarm optimizationpopulation-based optimization algorithms

Mathematics Subject Classification ID

Nonconvex programming, global optimization (90C26) Stochastic programming (90C15) Approximation methods and heuristics in mathematical programming (90C59) Population dynamics (general) (92D25) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20) Randomized algorithms (68W20)

Uses Software

• TOMS659
• ABC
• CEC 05

Cites Work

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• A new class of random number generators
• A hybrid meta-heuristic for global optimisation using low-discrepancy sequences of points
• Differential evolution -- a simple and efficient heuristic for global optimization over continuous spaces
• A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm
• Application of deterministic low-discrepancy sequences in global optimization
• Algorithm 659
• On the Systematic Search in a Hypercube

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