The Experts below are selected from a list of 10494 Experts worldwide ranked by ideXlab platform

Xin-she Yang - One of the best experts on this subject based on the ideXlab platform.

  • Firefly Algorithm and flower pollination Algorithm
    Nature-Inspired Computation and Swarm Intelligence, 2020
    Co-Authors: Xin-she Yang, Yu-xin Zhao
    Abstract:

    Abstract The Firefly Algorithm is a swarm intelligence-based Algorithm, and its nonlinearity in search mechanisms can usually lead to subdivision and multiswarms, which means that it can be potentially more effective than single-swarm Algorithms. This chapter introduces the main ideas of the Firefly Algorithm, followed by the introduction of the flower pollination Algorithm. Both implementation details and examples will be presented to show how these Algorithms work. Suggestions on modifications and multiobjective optimization will also be discussed.

  • Why the Firefly Algorithm Works
    Nature-Inspired Algorithms and Applied Optimization, 2017
    Co-Authors: Xin-she Yang
    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.

  • A Novel Hybrid Firefly Algorithm for Global Optimization.
    PloS one, 2016
    Co-Authors: Li-na Zhang, Xin-she Yang, Li-qiang Liu, Yun-tao Dai
    Abstract:

    Global optimization is challenging to solve due to its nonlinearity and multimodality. Traditional Algorithms such as the gradient-based methods often struggle to deal with such problems and one of the current trends is to use metaheuristic Algorithms. In this paper, a novel hybrid population-based global optimization Algorithm, called hybrid Firefly Algorithm (HFA), is proposed by combining the advantages of both the Firefly Algorithm (FA) and differential evolution (DE). FA and DE are executed in parallel to promote information sharing among the population and thus enhance searching efficiency. In order to evaluate the performance and efficiency of the proposed Algorithm, a diverse set of selected benchmark functions are employed and these functions fall into two groups: unimodal and multimodal. The experimental results show better performance of the proposed Algorithm compared to the original version of the Firefly Algorithm (FA), differential evolution (DE) and particle swarm optimization (PSO) in the sense of avoiding local minima and increasing the convergence rate.

  • cuckoo search and Firefly Algorithm overview and analysis
    2014
    Co-Authors: Xin-she Yang
    Abstract:

    Firefly Algorithm (FA) was developed by Xin-She Yang in 2008, while cuckoo search (CS) was developed by Xin-She Yang and Suash Deb in 2009. Both Algorithms have been found to be very efficient in solving global optimization problems. This chapter provides an overview of both cuckoo search and Firefly Algorithm as well as their latest developments and applications. We analyze these Algorithms and gain insight into their search mechanisms and find out why they are efficient. We also discuss the essence of Algorithms and its link to self-organizing systems. In addition, we also discuss important issues such as parameter tuning and parameter control, and provide some topics for further research.

  • cuckoo search and Firefly Algorithm theory and applications
    2013
    Co-Authors: Xin-she Yang
    Abstract:

    Nature-inspired Algorithms such as cuckoo search and Firefly Algorithm have become popular and widely used in recent years in many applications. These Algorithms are flexible, efficient and easy to implement. New progress has been made in the last few years, and it is timely to summarize the latest developments of cuckoo search and Firefly Algorithm and their diverse applications. This book will review both theoretical studies and applications with detailed Algorithm analysis, implementation and case studies so that readers can benefit most from this book. Application topics are contributed by many leading experts in the field. Topics include cuckoo search, Firefly Algorithm, Algorithm analysis, feature selection, image processing, travelling salesman problem, neural network, GPU optimization, scheduling, queuing, multi-objective manufacturing optimization, semantic web service, shape optimization, and others. This book can serve as an ideal reference for both graduates and researchers in computer science, evolutionary computing, machine learning, computational intelligence, and optimization, as well as engineers in business intelligence, knowledge management and information technology.

Zhou Ling-yu - One of the best experts on this subject based on the ideXlab platform.

  • Elite Orthogonal Learning Firefly Algorithm
    Computer Science, 2015
    Co-Authors: Zhou Ling-yu
    Abstract:

    In order to overcome the shortcomings of Firefly Algorithm such as slow convergence speed and low computational accuracy,an elite orthogonal learning Firefly Algorithm was proposed.An elite Firefly was introduced to construct a guidance vector using the orthogonal learning strategy,which can preserve and discover useful information in the population best positions and direct the swarm to fly toward the global optimal region.At the same time,the method of adaptive step size was used to balance the exploration and exploitation ability of the Algorithm,and the minimum attractive parameter was adopted to guarantee the attraction among the fireflies whose distance is large.We compared the proposed Algorithm with standard Firefly Algorithm and other three improved Firefly Algorithms on six benchmarks,and the results show that the proposed Algorithm obtains quicker convergence speed and better solution accuracy.

Viviana Cocco Mariani - One of the best experts on this subject based on the ideXlab platform.

  • improved Firefly Algorithm approach applied to chiller loading for energy conservation
    Energy and Buildings, 2013
    Co-Authors: Leandro Dos Santos Coelho, Viviana Cocco Mariani
    Abstract:

    Significant energy savings can be achieved by optimizing chiller operation and design in heating, ventilation and cooling (HVAC) systems. In terms of optimization, various metaheuristics have been proposed to the optimal chiller loading problem. New metaheuristics are also emerging recently, between them the Firefly Algorithm. Firefly Algorithm is a nature inspired Algorithm based on the idealized behavior of the flash pattern and characteristics of fireflies. This study proposes a new improved Firefly Algorithm (IFA) based on Gaussian distribution function to the optimal chiller loading design. To testify the performance of the proposed method, the paper adopts two case studies comparing the results of the developed model using IFA with those of traditional Firefly Algorithm and other optimization methods in literature. In this paper, the optimization problem is minimize energy consumption of multi-chiller systems, where the objective function is energy consumption and the optimum parameter is the partial loading ratio of each chiller. The results of both case studies show that the proposed IFA outperform several optimization methods of the literature in terms of minimum energy consumption solution of the optimal chiller loading problem.

Songwei Huang - One of the best experts on this subject based on the ideXlab platform.

  • modified Firefly Algorithm based multilevel thresholding for color image segmentation
    Neurocomputing, 2017
    Co-Authors: Songwei Huang
    Abstract:

    A modified Firefly Algorithm (MFA) is proposed.MFA Algorithm is used for multilevel color image thresholding segmentation.Kapur's entropy, minimum cross entropy and between-class variance are used as objective functions.MFA Algorithm is an effective multilevel thresholding method for color image segmentation. In this paper, a modified Firefly Algorithm (MFA) is proposed to find the optimal multilevel threshold values for color image. Kapur's entropy, minimum cross entropy and between-class variance method is used as the objective functions. To test and analyze the performance of the MFA Algorithm, the presented method are tested on ten test color image and the results are compared with basic Firefly Algorithm (FA), Brownian search based Firefly Algorithm (BFA) and Lvy search based Firefly Algorithm (LFA). The experimental results show that the presented MFA Algorithm outperforms all the other Algorithms in term of the optimal threshold value, objective function, PSNR, SSIM value and convergence. In MFA Algorithm, chaotic map is used to the initialization of Firefly population, which can enhance the diversification. In addition, global search method of particle swarm optimization (PSO) Algorithm is introduced into the movement phase of fireflies. Compared with the other methods, the MFA Algorithm is an effective method for multilevel color image thresholding segmentation.

Mohammad Reza Meybodi - One of the best experts on this subject based on the ideXlab platform.

  • A New Fuzzy Firefly Algorithm with Adaptive Parameters
    International Journal of Computational Intelligence and Applications, 2017
    Co-Authors: Tahereh Hassanzadeh, Mohammad Reza Meybodi, Masoumeh Shahramirad
    Abstract:

    Firefly Algorithm is a swarm based Algorithm that can be used for solving optimization problems. This paper proposed an improved fuzzy adaptive Firefly Algorithm (FAFA). In the proposed FAFA, a fuzzy system is used to adapt Firefly Algorithm’s parameters in order to improve its ability in global and local searches. Also, we used different fireflies initializing intervals and different iteration numbers to show the Algorithm capability to find global optima. Results focus on the two case study categories of function optimization (seven benchmark functions) and presented a novel optimal multilevel thresholding approach for histogram-based image segmentation by using proposed FAFA and Otsu method. Evidence indicates that the optimization results of proposed FAFA approach are so better than the standard FA.

  • An Improved Firefly Algorithm with Directed Movement
    2011
    Co-Authors: Shadi Mashhadi Farahani, Babak Nasiri, Azam Amin Abshouri, Mohammad Reza Meybodi
    Abstract:

    Firefly Algorithm is one of the evolutionary computing models that is inspired by fireflies behavior in nature. Each Firefly's movement is based on absorption of the other one. In this paper for directing Firefly's movement towards global best, it is proposed a new Firefly's movement that guides fireflies to global best in each iteration, if there are no any better local optima in their neighborhood. It is proposed Gaussian distribution to draw out a better randomized position for next iteration. Proposed Algorithm was tested on five standard functions that have ever used for testing the optimization Algorithms. Experimental results show better performance and more accuracy than standard Firefly Algorithm. Keywords-Firefly Algorithm;optimization; Global search; Local search.

  • A Gaussian Firefly Algorithm
    International Journal of Machine Learning and Computing, 2011
    Co-Authors: Shadi Mashhadi Farahani, Babak Nasiri, Azam Amin Abshouri, Mohammad Reza Meybodi
    Abstract:

    Firefly Algorithm is one of the evolutionary optimization Algorithms, and is inspired by fireflies behavior in nature. Each Firefly movement is based on absorption of the other one. In this paper to stabilize Firefly's movement, it is proposed a new behavior to direct fireflies movement to global best if there was no any better solution around them. In addition to increase convergence speed it is proposed to use Gaussian distribution to move all fireflies to global best in each iteration. Proposed Algorithm was tested on five standard functions that have ever used for testing the static optimization Algorithms. Experimental results show better performance and more accuracy than standard Firefly Algorithm.