Population set-based global optimization algorithms: some modifications and numerical studies

By M. Montaz Ali, Aimo A. Törn. Computers & Operations Research, 2004

449 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Numerical Analysis

View Paper: https://www.semanticscholar.org/paper/2f74a8c2f257b369a50ef4bc555a08fd6a0914f0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Swagatam Das
2. Ponnuthurai Nagaratnam Suganthan
3. Ferrante Neri
4. Shahryar Rahnamayan
5. Janez Brest
6. Ville Tirronen
7. Mirjam Sepesy Maucec
8. Pinar Çivicioglu

In Computational Theory and Mathematics

1. Ponnuthurai Nagaratnam Suganthan
2. Swagatam Das
3. Ferrante Neri
4. Janez Brest
5. Ville Tirronen
6. Mirjam Sepesy Maucec
7. Shahryar Rahnamayan
8. Pinar Çivicioglu

In Numerical Analysis

1. Janez Brest
2. Ponnuthurai Nagaratnam Suganthan
3. Magdy M. A. Salama
4. Shahryar Rahnamayan
5. Hamid R. Tizhoosh
6. Swagatam Das
7. Mirjam Sepesy Maucec
8. Charoenchai Khompatraporn

Citations in the corpus

• Recent advances in differential evolution: a survey and experimental analysis (2010, background): “In Ali and Törn (2004), instead of applying a randomization, a fitness-based adaptation has been proposed.”
• Differential Evolution as Applied to Electromagnetics (2011, background): “solution [ 30-32 ]. To properly deal with these drawbacks or char­”
• Opposition-Based Differential Evolution (2008, methodology): “• Differential amplification factor, [2], [4], [13], [15], [18].”
• Population size reduction for the differential evolution algorithm (2008, methodology): “They introduced an auxiliary population of NP individuals alongside the original population (noted in [1], a notation using sets is used—population set-based methods).”
• Opposition versus randomness in soft computing techniques (2008, unknown): “5 [13,29,30,32,38,40] Crossover probability constant (Cr) 0.”
• Quasi-oppositional Differential Evolution (2007, unknown): “5 [1], [2], [5], [16], [22] • Crossover probability constant, C r = 0 .”
• A Numerical Evaluation of Several Stochastic Algorithms on Selected Continuous Global Optimization Test Problems (2005, methodology): “Although no theoretical convergence results are known for the population set based methods considered here, empirical evidence suggests that the probability of finding the global minimum increases with the size of the population set (Ali and To¨rn, 2004).”
• Differential Evolution: A Survey of the State-of-the-Art (2011, methodology): “Therefore, researchers naturally started to consider some techniques such as self-adaptation to automatically find an optimal set of control parameters for DE [S28], [3], [10], [48], [76], [84].”
• Evolutionary optimization as applied to inverse scattering problems (2009, background): “Despite the basic version of the DE, many different versions of the algorithm exist [1, 33, 121].”
• Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems (2006, methodology): “Starting with a number of guessed solutions, the multipoint algorithm updates one or more solutions in a synergistic manner in the hope of steering the population toward the optimum [8], [9].”