Society and civilization: An optimization algorithm based on the simulation of social behavior
By Tapabrata Ray, Kim-Meow Liew. IEEE Transactions on Evolutionary Computation, 2003
556 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/4ab51909a0649d0a9f6eb7bdf70f31660fd2101a
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In Artificial Intelligence
- Seyedali Mirjalili
- Swagatam Das
- Ponnuthurai Nagaratnam Suganthan
- Xin-She Yang
- Pinar Çivicioglu
- Efrén Mezura-Montes
- Carlos Artemio Coello Coello
- Seyed Mohammad Mirjalili
In Sociology and Political Science
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- David Camacho
- Carlos Artemio Coello Coello
- Francisco Herrera
- Dervis Karaboga
- Swagatam Das
- Leandro dos S. Coelho
- Emmanuel Prempain
Citations in the corpus
- A ranking selection-based particle swarm optimizer for engineering design optimization problems (2008, methodology): “1991), a society and civilization algorithm ( Ray and Liew 2003 ), and a improved PSO (He et al. 2004).”
- Differential evolution with dynamic stochastic selection for constrained optimization (2008, methodology): “DSS-MDE FSA [25] He et al. [26] Ray and Liew [ 22 ] Deb [5] Deb [27]”
- Constraint-handling in nature-inspired numerical optimization: Past, present and future (2011, background): “Additionally, other mechanisms such as Pareto ranking in different search spaces [55–58], the shrinking of the search space [59] and the use of non-dominated sorting and clustering techniques to generate collaboration among sub-populations [60], have been proposed to tackle the disadvantages of this”
- Pelican Optimization Algorithm: A Novel Nature-Inspired Algorithm for Engineering Applications (2022, background): “Decision variables, constraints, and objective functions are the three main parts of each optimization problem for modeling [2].”
- Northern Goshawk Optimization: A New Swarm-Based Algorithm for Solving Optimization Problems (2021, background): “An optimization problem consists of three main parts: decision variables, constraints (equality and inequality), and objective functions [1].”
- Lévy-Flight Moth-Flame Algorithm for Function Optimization and Engineering Design Problems (2016, unknown): “[44], CS [11, 45], HCPS [46], SCA [47], (μ + λ) ES [5, 48], and ABC [9, 49].”
- Artificial bee colony algorithm for large-scale problems and engineering design optimization (2010, methodology): “The basic idea of the SCA technique is that social interactions enable individuals to adapt and improve faster than biological evolution based on genetic inheritance alone.”
- Structural optimization for post-buckling behavior using particle swarms (2006, result): “The results are compared with the ones previously obtained by other authors (He et al. 2004; Ray and Liew 2003; Coello 2000; Arora 1989).”