Multiobjective cuckoo search for design optimization

By Xin-She Yang, Suash Deb. Computers & Operations Research, 2013

877 citations recorded by the source corpus.

How researchers see this paper

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

Cuckoo search: recent advances and applications (2013, methodology): “As a further extension, Yang and Deb [44] produced a multiobjective cuckoo search (MOCS) for design engineering appplications.”
Nature-Inspired Optimization Algorithms: Challenges and Open Problems (2020, methodology): “…literature, the benchmarking practice seems to use a set of test functions with diﬀer-ent properties (such as mode shapes, separability and optima locality), there are many such benchmark test functions [Jamil and Yang(2013)] and some test suites designed by diﬀerent conferences or research groups.”
An efficient modified grey wolf optimizer with Lévy flight for optimization tasks (2017, background): “Referring to the investigations of Deb and Lee in [54, 55], different values of β can significantly affect the shape of LF distribution.”
Bio inspired computing - A review of algorithms and scope of applications (2016, neutral): “Cuckoo search (Yang & Deb, 2009; Gandomi et al., 2013; Yang & Deb, 2013) was developed for addressing single or multi-objective problems under complex nonlinear constraints.”