An improved differential evolution algorithm with fitness-based adaptation of the control parameters

详细信息    查看全文

• 作者：Arnob Ghosh^a ; ^{arnob008@gmail.com"" rel=""nofollow} ; Swagatam Das ; ^a ; ^{swagatamdas19@yahoo.co.in"" rel=""nofollow} ; Aritra Chowdhury^a ; ^{aritra131288@gmail.com"" rel=""nofollow} ; Ritwik Giri^a ; ^{ritwikgiri@gmail.com"" rel=""nofollow}
• 关键词：Numerical optimization ; Differential evolution ; Genetic algorithms ; Evolutionary programming ; Evolution strategies ; Parameter tuning
• 刊名：Information Sciences
• 出版年：2011
• 出版时间：15 September 2011
• 年：2011
• 卷：181
• 期：18
• 页码：3749-3765
• 全文大小：627 K

文摘

Differential Evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms of current interest. DE operates through the similar computational steps as employed by a standard Evolutionary Algorithm (EA). However, unlike the traditional EAs, the DE-variants perturb the current-generation population members with the scaled differences of randomly selected and distinct population members. Therefore, no separate probability distribution has to be used, which makes the scheme self-organizing in this respect. Scale Factor (F) and Crossover Rate (Cr) are two very important control parameters of DE since the former regulates the step-size taken while mutating a population member in DE and the latter controls the number of search variables inherited by an offspring from its parent during recombination. This article describes a very simple yet very much effective adaptation technique for tuning both F and Cr, on the run, without any user intervention. The adaptation strategy is based on the objective function value of individuals in the DE population. Comparison with the best-known and expensive variants of DE over fourteen well-known numerical benchmarks and one real-life engineering problem reflects the superiority of proposed parameter tuning scheme in terms of accuracy, convergence speed, and robustness.