Why the Firefly Algorithm Works
Xin-She Yang,Xingshi He +1 more
TL;DR: This chapter summarizes the latest developments about the firefly algorithm and its variants as well as their diverse applications and future research directions are highlighted.
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Abstract: Firefly algorithm is a nature-inspired optimization algorithm and there have been significant developments since its appearance about 10 years ago. This chapter summarizes the latest developments about the firefly algorithm and its variants as well as their diverse applications. Future research directions are also highlighted.
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Citations
Metaheuristic Algorithm for Photovoltaic Parameters: Comparative Study and Prediction with a Firefly Algorithm
Mohamed Louzazni,Ahmed Khouya,Khalid Amechnoue,Alessandro Gandelli,Marco Mussetta,Aurelian Craciunescu +5 more
TL;DR: In this article, a Firefly algorithm is proposed for identification and comparative study of five, seven and eight parameters of a single and double diode solar cell and photovoltaic module under different solar irradiation and temperature.
A firefly algorithm for the environmental prize-collecting vehicle routing problem
TL;DR: The effectiveness of the Firefly Algorithm based on Coordinates (FAC) is showed over computational experiments and statistical analysis, in comparison to the performance of other bio-inspired algorithms and a mathematical solver.
55
Traveling salesman problem: a perspective review of recent research and new results with bio-inspired metaheuristics
Eneko Osaba,Xin-She Yang,Javier Del Ser +2 more
- 01 Jan 2020
TL;DR: This chapter aims at making a step forward in the field proposing an experimentation hybridizing three different reputed bio-inspired computational metaheuristics (namely, particle swarm optimization, the firefly algorithm, and the bat algorithm) and the novelty search mechanism.
53
Solving systems of nonlinear equations using a modified firefly algorithm (MODFA)
TL;DR: This paper presents a modified firefly algorithm treating the problem as an optimization problem, which is capable of giving multiple root approximations simultaneously within a reasonable state space and illustrates the viability of the method using benchmark systems found in the literature.
47
Nature-Inspired Optimization Algorithms in Solving Partial Shading Problems: A Systematic Review
Clifford Choe Wei Chang,Tan Ding,Mohammad Arif Sobhan Bhuiyan,Kang Chia Chao,M.M. Ariannejad,Haw Choon Yian +5 more
TL;DR: In-depth study found that nature-inspired swarm search mechanisms are highly suitable to be implemented as MPPT schemes in PV applications, especially in the accuracy and the speed of the search algorithms.
32
References
Particle swarm optimization
James Kennedy,Russell C. Eberhart +1 more
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TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
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Particle Swarm Optimization.
James Kennedy
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TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
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Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces
Rainer Storn,Kenneth Price +1 more
TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
Particle swarm optimization
TL;DR: A snapshot of particle swarming from the authors’ perspective, including variations in the algorithm, current and ongoing research, applications and open problems, is included.
•Book
Nature-Inspired Metaheuristic Algorithms
Xin-She Yang
- 01 Feb 2008
TL;DR: This book reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms in optimization, including genetic algorithms, bee algorithms, particle swarm optimization, simulated annealing, ant colony optimization, harmony search, and firefly algorithms.
4.9K
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