Firefly Algorithm, Lévy Flights and Global Optimization

By Xin-She Yang. SGAI Conferences, 2010

1,002 citations recorded by the source corpus.

Nature-inspired algorithms such as Particle Swarm Optimization and Firefly Algorithm are among the most powerful algorithms for optimization. In this paper, we intend to formulate a new metaheuristic algorithm by combining Levy flights with the search strategy via the Firefly Algorithm. Numerical studies and results suggest that the proposed Levy-flight firefly algorithm is superior to existing metaheuristic algorithms. Finally implications for further research and wider applications will be discussed.

Subfields: Artificial Intelligence, Computer Networks and Communications, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/472f68cf07113fca0a237b083c124ccb9b3bed4a

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

In Computer Networks and Communications

In Molecular Biology

Citations in the corpus

Review of Swarm Intelligence-based Feature Selection Methods (2020, methodology): “The Firefly Algorithm (FA) was introduced by Xin-She Yang, in 2010 (Yang 2010), that, the main idea was inspired by the optical connection between fireflies.”
Grasshopper Optimisation Algorithm: Theory and application (2017, methodology): “The literature shows that there are many recent swarm intelligence optimisation techniques such as Dolphin Echolocation (DEL) [18, 19], Firefly Algorithm (FA) [20, 21], Bat Algorithm (BA) [22], and Grey Wolf Optimizer (GWO) [3].”
New inspirations in swarm intelligence: a survey (2011, methodology): “…algorithm (MHSA, Section 2.7.1) and the BA (Section 2.8.1), to date, no other application to real world problems was found, besides the original works for solving the symmetric travelling salesman problem (TSP) (Feng et al., 2009), and for solving benchmark functions (Yang, 2010b), respectively.”
The Ant Lion Optimizer (2015, methodology): “In addition the ALO algorithm is compared with several recent algorithms such as the States of Matter Search (SMS) algorithm [63,64], Bat Algorithm (BA) [65], Flower Pollination Algorithm [66] , Cuckoo Search (CS) algorithm [26,27], and Firefly Algorithm (FA) [24,25].”
Butterfly optimization algorithm: a novel approach for global optimization (2018, methodology): “Up to now, researchers have only used very limited characteristics inspired by nature and there is room for more algorithm development (Yang 2010a; Onwubolu and Babu 2004; Wolpert and Macready 1997).”
A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments (2020, background): “The ALO algorithm also revealed robust effectiveness and more active convergence speed (quicker) when matched with the states of matter search (SMS) optimization algorithm [66], bat algorithm (BA) [67], genetic algorithm (GA) [68], flower pollination (FP) optimization algorithm [69], cuckoo search (”