Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems

By Amir H. Gandomi, Xin-She Yang, Amir Hossein Alavi. Engineering computations, 2011

2,172 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/91de962e115bcf65eaf8579471a818ba8c5b0ea6

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. Amir H. Gandomi
4. Laith Mohammad Abualigah
5. Seyed Mohammad Mirjalili
6. Huiling Chen
7. Arpan Kumar Kar
8. Xin-She Yang

In Computational Theory and Mathematics

1. Seyedali Mirjalili
2. Ali Asghar Heidari
3. Amir H. Gandomi
4. Huiling Chen
5. Laith Mohammad Abualigah
6. Arpan Kumar Kar
7. Seyed Mohammad Mirjalili
8. Xin-She Yang

In Industrial and Manufacturing Engineering

1. Seyed Mohammad Mirjalili
2. Suash Deb
3. Arpan Kumar Kar
4. Amir H. Gandomi
5. Seyedali Mirjalili
6. Laith Mohammad Abualigah
7. Fotios Mantoglou
8. P. Mergos

Citations in the corpus

• Dung beetle optimizer: a new meta-heuristic algorithm for global optimization (2022, methodology): “The objective function of this problem can be described as follows: where The results of the DBO algorithm are compared with other optimization approaches which include HHO algorithm [17], SSA [22], MVO algorithm [40], PSO-DE [42], MBA [43], and CS algorithm [44] in previous literature.”
• The Ant Lion Optimizer (2015, neutral): “This table shows that the proposed ALO algorithm finds a design with the optimal value identical to those of Artificial Bee Colony (ABC) algorithm [74], MBA [74], CS [72], and Interior Search Algorithm (ISA) [79].”
• Discrete cuckoo search algorithm for the travelling salesman problem (2014, background): “Metaheuristics are usually simple to implement, but they often can solve complex problems and can thus be adapted to solve many real-world optimization problems, from the fields of operations research, engineering to artificial intelligence [10, 11, 12, 36].”
• Optimum design of reinforced concrete retaining walls with the flower pollination algorithm (2019, positive): “A great number of metaheuristic algorithms exist including genetic algorithm (GA) (Holland 1975), particle swarm optimization (PSO) (Kennedy 2011), firefly algorithm (FA) (Yang 2010), cuckoo search (CS) (Gandomi et al. 2013) and many others.”
• INFO: An efficient optimization algorithm based on weighted mean of vectors (2022, methodology): “9 ≥ tf ≤ 5, The problem was optimized by the adaptive response surface method (ARSM) [83], CS [76], improved ARSM (IARSM) [83], and symbiotic organisms search (SOS) [75].”