Engineering Optimization: An Introduction with Metaheuristic Applications

By Xin-She Yang. 2010

1,610 citations recorded by the source corpus.

Subfields: Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/4e236279862c1e264c4f3f33f2a91723d240b2ac

How researchers see this paper

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

In Industrial and Manufacturing Engineering

Citations in the corpus

Multi-Objective Flower Algorithm for Optimization (2014, neutral): “Nature-inspired algorithms have shown their promising performance and have thus become popular and widely used, and these algorithms are mostly swarm intelligence based [4, 8, 22, 29, 30, 31, 32, 12, 13].”
A swarm optimization algorithm inspired in the behavior of the social-spider (2013, background): “This information encoded as small vibrations is a critical aspect for the collective coordination among members [20].”
Firefly algorithm with chaos (2013, methodology): “Fourthly, FA includes two important limit cases and it is possible to fine-tune the algorithm so to combine the advantages of both limit cases for exploring the search space more efficiently [4].”
Firefly Algorithms for Multimodal Optimization (2009, background): “For example, Yang described a multimodal function which looks like a standing-wave pattern [11]”
Engineering optimisation by cuckoo search (2010, background): “Yang’s second test function is also multimodal but it has a singularity f(x) = ( d ∑ i=1 ǫi|xi| ) exp [ − d ∑ i=1 sin(x2i ) ] , (12) which has a unique global minimum f∗ = 0 at x∗ = (0, 0, ..., 0) in the domain −2π ≤ xi ≤ 2π where i = 1, 2, ..., d (Yang 2010).”
Firefly Algorithm, Lévy Flights and Global Optimization (2010, background): “For example, the author introduced a forest function [19]”
Cuckoo search: recent advances and applications (2013, background): “It is possible to combine diﬀerent objectives into a single objective, though multiobjective optimization can give far more information and insight into the problem.”
A sunflower optimization (SFO) algorithm applied to damage identification on laminated composite plates (2018, background): “We can thus learn the success of problem-solving from nature and develop nature-inspired heuristic and/or metaheuristic algorithms [25].”
Flower Pollination Algorithm for Global Optimization (2012, background): “For this reason, the literature of metaheuristics has expanded tremendously in the last two decades [18, 20].”