Comparative Study of Firefly Algorithm and Particle Swarm Optimization for Noisy Non- Linear Optimization Problems
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TLDR
In this paper, two types of meta-heuristics called Particle Swarm Optimization (PSO) and Firefly algorithms were devised to find optimal solutions of noisy non-linear continuous mathematical models.Abstract:
There are various noisy non-linear mathematical optimization problems that can be effectively solved by Metaheuristic Algorithms. These are iterative search processes that efficiently perform the exploration and exploitation in the solution space, aiming to efficiently find near optimal solutions. Considering the solution space in a specified region, some models contain global optimum and multiple local optima. In this context, two types of meta-heuristics called Particle Swarm Optimization (PSO) and Firefly algorithms were devised to find optimal solutions of noisy non-linear continuous mathematical models. Firefly Algorithm is one of the recent evolutionary computing models which is inspired by fireflies behavior in nature. PSO is population based optimization technique inspired by social behavior of bird flocking or fish schooling. A series of computational experiments using each algorithm were conducted. The results of this experiment were analyzed and compared to the best solutions found so far on the basis of mean of execution time to converge to the optimum. The Firefly algorithm seems to perform better for higher levels of noise.read more
Citations
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A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems
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Significant wave height forecasting via an extreme learning machine model integrated with improved complete ensemble empirical mode decomposition
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Multi-layer perceptron hybrid model integrated with the firefly optimizer algorithm for windspeed prediction of target site using a limited set of neighboring reference station data
Ravinesh C. Deo,Mohammad Ali Ghorbani,Mohammad Ali Ghorbani,Saeed Samadianfard,Tek Narayan Maraseni,Mehmet Bilgili,Mustafa Biazar +6 more
TL;DR: In this paper, a multilayer perceptron (MLP) hybrid model integrated with the Firefly Optimizer algorithm was used to predict wind speed at target sites in north-west Iran using a limited set of historical (monthly) data (2004-2014) for a group of neighboring stations.
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Smart Artificial Firefly Colony Algorithm-Based Support Vector Regression for Enhanced Forecasting in Civil Engineering
TL;DR: Analytical results confirm that using the proposed hybrid system significantly improves the accuracy in solving CE problems and is validated by comparing its performance with those of empirical methods and previous works via cross‐validation algorithm and hypothesis test.
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A novel efficient substitution-box design based on firefly algorithm and discrete chaotic map
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References
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Proceedings ArticleDOI
Particle swarm optimization
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.
Journal ArticleDOI
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
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.
Journal ArticleDOI
Application of the Firefly Algorithm for Solving the Economic Emissions Load Dispatch Problem
TL;DR: A solution to this famous problem of economic emission load dispatch is described using a new metaheuristic nature-inspired algorithm, called firefly algorithm, which was developed by Dr. Xin-She Yang at Cambridge University in 2007.