The firefly algorithm has become an increasingly important tool of Swarm Intelligence that has been applied in almost all areas of optimization, as well as engineering practice. Many problems from various areas have been successfully solved using the firefly algorithm and its variants. In order to use the algorithm to solve diverse problems, the original firefly algorithm needs to be modified or hybridized. This paper carries out a comprehensive review of this living and evolving discipline of Swarm Intelligence, in order to show that the firefly algorithm could be applied to every problem arising in practice. On the other hand, it encourages new researchers and algorithm developers to use this simple and yet very efficient algorithm for problem solving. It often guarantees that the obtained results will meet the expectations.

A comprehensive review of firefly algorithms

Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm

Many problems in science and engineering can be formulated as optimization problems, subject to complex nonlinear constraints. The solutions of highly nonlinear problems usually require sophisticated optimization algorithms, and traditional algorithms may struggle to deal with such problems. A current trend is to use nature-inspired algorithms due to their flexibility and effectiveness. However, there are some key issues concerning nature-inspired computation and swarm intelligence. This paper provides an in-depth review of some recent nature-inspired algorithms with the emphasis on their search mechanisms and mathematical foundations. Some challenging issues are identified and five open problems are highlighted, concerning the analysis of algorithmic convergence and stability, parameter tuning, mathematical framework, role of benchmarking and scalability. These problems are discussed with the directions for future research.

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Nature-Inspired Optimization Algorithms: Challenges and Open Problems.

In the real world, there are a number of optimization problems whose search space is restricted to take binary values; however, there are many continuous metaheuristics with good results in continuous search spaces. These algorithms must be adapted to solve binary problems. This paper surveys articles focused on the binarization of metaheuristics designed for continuous optimization.

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Putting Continuous Metaheuristics to Work in Binary Search Spaces

http://www.epitropakis.co.uk/sites/default/files/pubs/EpitropakisPVINSR2.pdf

Evolving cognitive and social experience in Particle Swarm Optimization through Differential Evolution: A hybrid approach

Parameters setting is an important problem of evolution algorithms,including differential evolution algorithm.It has an effect on the performance of evolution algorithms.According to the problem of parameters of differential evolution,a method is presented,which uses self-adaptive as a scientific evidence to adjust parameters and set F and CR combined with modulated probability.An algorithm is presented,which depends on the fitness of individual and modulated probability set the parameters F and CR automatically.This method can get the optimal control parameters for different optimization problem without user interaction.Moreover,two trial vectors are created by recombination for increased colony diversity and avoided premature convergence.These vectors compete with the parent individual to be the next generation.Experimental results indicate that the proposed algorithm is efficient and feasible.It is superior to other related methods such as DE,jDE,FADE,MPDE,DDE both on the quality of solution and on the convergence rate,especially for high dimension functions.

Self-adapting control parameters in differential evolution

A Selective Approach to Neural Network Ensemble Based on Clustering Technology

Research on the Algorithm of Feature Selection Based on Gini Index for Text Categorization

A Survey of Intrusion-Detection Alert Aggregation and Correlation Techniques

Parameters setting is an important problem of evolution algorithms, include differential evolution algorithm. It has an effect on the performance of evolution algorithms. Although there is only three control parameters in differential evolution (DE) algorithm, the parameters setting is also a difficult problem. Self-adaptation is highly beneficial for adjusting the control parameters, especially when done without any user interaction. This paper presents differential evolution algorithm, called (AdaptDE), which use self-adaptive mechanisms applied to the control parameters. It can get the optimal control parameters for different optimization problem without user interaction. Experimental results indicate that AdaptDE algorithm is efficient and feasible. It is superior to jDE algorithm and FADE algorithm on the quality of solution.

Huang Houkuan

Papers

Self-adapting control parameters in differential evolution

A Selective Approach to Neural Network Ensemble Based on Clustering Technology

Research on the Algorithm of Feature Selection Based on Gini Index for Text Categorization

A Survey of Intrusion-Detection Alert Aggregation and Correlation Techniques

A modified differential evolution algorithm with self-adaptive control parameters