S-shaped versus V-shaped transfer functions for binary Particle Swarm Optimization

By Seyedali Mirjalili, Andrew Lewis. Swarm and Evolutionary Computation, 2013

1,033 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/140fc48ed4641864cdb93034c0d5c0dfea6b66cf

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. Seyedali Mirjalili
2. Ali Asghar Heidari
3. Shahrzad Saremi
4. Tansel Dökeroğlu
5. Hossam Faris
6. Ibrahim Aljarah
7. Amir H. Gandomi
8. Andrew Lewis

In Computational Theory and Mathematics

1. Ali Asghar Heidari
2. Seyedali Mirjalili
3. Hossam Faris
4. Ibrahim Aljarah
5. Shahrzad Saremi
6. Tansel Dökeroğlu
7. Amir H. Gandomi
8. Majdi M. Mafarja

In Numerical Analysis

1. Seyedali Mirjalili
2. Shahrzad Saremi
3. Andrew Lewis
4. S. Mirjalili
5. Xin-She Yang
6. Ali Asghar Heidari
7. Seyedeh Zahra Mirjalili
8. Majdi M. Mafarja

Citations in the corpus

• An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems (2018, background): “C According to Mirjalili and Lewis [68], one of the most efficient ways to convert a continuous algorithm to a binary version is to utilize transfer functions (TF).”
• Biogeography-based optimisation with chaos (2014, neutral): “The former group is suitable for benchmarking exploration, whereas the latter group is use to examine exploitation [36, 37, 39].”
• Let a biogeography-based optimizer train your Multi-Layer Perceptron (2014, background): “It is not enough to compare algorithms based on the mean and standard deviation values [15]; a statistical test is necessary to prove that a proposed new algorithm presents a significant improvement over other existing methods for a particular problem [38].”
• Binary bat algorithm (2013, methodology): “This method has a drawback as the particles are forced to take values of 0 or 1 [24].”
• Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems (2015, methodology): “The Wilcoxon non-parametric statistical test [39, 56] is conducted in this work.”
• The Ant Lion Optimizer (2015, neutral): “The Wilcoxon rank-sum test [67,68] is conducted in this work.”
• Evolutionary population dynamics and grey wolf optimizer (2014, background): “The selected test functions are presented in Tables 1 and 2 [51–55].”
• Grasshopper Optimisation Algorithm: Theory and application (2017, methodology): “For verification of results, seven algorithms were employed from the literature including well-known and recent ones: PSO, SMS [25, 26], BA [22], FPA [27], CS [23, 24], FA [20, 21], GA, DE, and Gravitational Search Algorithm (GSA)[44].”