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14
A comprehensive review of firefly algorithms
, 2013
"... 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 ..."
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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.
Upgraded Firefly Algorithm for Portfolio Optimization Problem
"... AbstractPortfolio selection is a wellknown intractable research problem in the area of economics and finance. There are many definitions of the problem that by introduction of additional constraints try to make it closer to the realword conditions. Firefly algorithm is one of the latest swarm int ..."
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AbstractPortfolio selection is a wellknown intractable research problem in the area of economics and finance. There are many definitions of the problem that by introduction of additional constraints try to make it closer to the realword conditions. Firefly algorithm is one of the latest swarm intelligence metaheuristics that was very successfully applied to both, unconstrained and constrained hard optimization problems. In this paper we adjusted firefly algorithm to the portfolio optimization problem and since the results were not completely satisfactory, we modified it so that better exploitation/exploration balance was achieved. We tested our improved algorithm on unconstrained portfolio problem, as well as on the problem formulation with cardinality and bounding constraints. We used official benchmark data sets from the ORLibrary, and included data from Hang Seng in Hong Kong, DAX 100 in Germany and FTSE 100 in UK with 31, 85 and 89 assets respectively. Our upgraded algorithm proved to be uniformly better than the original one. Additionally, we compared it on the same data set to five other optimization metaheuristics from the literature and our upgraded firefly algorithm was better in most cases measured by all performance indicators.
Synchronous firefly algorithm for cluster head selection inWSN,”The
 ScientificWorld Journal,
, 2015
"... Wireless Sensor Network (WSN) consists of small lowcost, lowpower multifunctional nodes interconnected to efficiently aggregate and transmit data to sink. Clusterbased approaches use some nodes as Cluster Heads (CHs) and organize WSNs efficiently for aggregation of data and energy saving. A CH c ..."
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Wireless Sensor Network (WSN) consists of small lowcost, lowpower multifunctional nodes interconnected to efficiently aggregate and transmit data to sink. Clusterbased approaches use some nodes as Cluster Heads (CHs) and organize WSNs efficiently for aggregation of data and energy saving. A CH conveys information gathered by cluster nodes and aggregates/compresses data before transmitting it to a sink. However, this additional responsibility of the node results in a higher energy drain leading to uneven network degradation. Low Energy Adaptive Clustering Hierarchy (LEACH) offsets this by probabilistically rotating cluster heads role among nodes with energy above a set threshold. CH selection in WSN is NPHard as optimal data aggregation with efficient energy savings cannot be solved in polynomial time. In this work, a modified firefly heuristic, synchronous firefly algorithm, is proposed to improve the network performance. Extensive simulation shows the proposed technique to perform well compared to LEACH and energyefficient hierarchical clustering. Simulations show the effectiveness of the proposed method in decreasing the packet loss ratio by an average of 9.63% and improving the energy efficiency of the network when compared to LEACH and EEHC.
HeuristicBased Firefly Algorithm for Bound Constrained Nonlinear Binary Optimization
, 2014
"... Firefly algorithm (FA) is a metaheuristic for global optimization. In this paper, we address the practical testing of a heuristicbased FA (HBFA) for computing optima of discrete nonlinear optimization problems, where the discrete variables are of binary type. An important issue in FA is the formula ..."
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Firefly algorithm (FA) is a metaheuristic for global optimization. In this paper, we address the practical testing of a heuristicbased FA (HBFA) for computing optima of discrete nonlinear optimization problems, where the discrete variables are of binary type. An important issue in FA is the formulation of attractiveness of each firefly which in turn affects its movement in the search space. Dynamic updating schemes are proposed for two parameters, one from the attractiveness term and the other from the randomization term. Three simple heuristics capable of transforming real continuous variables into binary ones are analyzed. A new sigmoid ‘erf ’ function is proposed. In the context of FA, three different implementations to incorporate the heuristics for binary variables into the algorithm are proposed. Based on a set of benchmark problems, a comparison is carried out with other binary dealing metaheuristics. The results demonstrate that the proposed HBFA is efficient and outperforms binary versions of differential evolution (DE) and particle swarm optimization (PSO). The HBFA also compares very favorably with angle modulated version of DE and PSO. It is shown that the variant of HBFA based on the sigmoid ‘erf ’ function with ‘movements in continuous space ’ is the best, both in terms of computational requirements and accuracy. 1
Firefly Algorithm in Determining Maximum Load Utilization Point and Its Enhancement through Optimal Placement of FACTS Device
, 2016
"... Abstract In a Power System, load is the most uncertain and extremely time varying unit. Hence it is important to determine the system's supreme acceptable loadability limit called maximum loadability point to accommodate the sudden variation of load demand. Nowadays the enhancement of the maxi ..."
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Abstract In a Power System, load is the most uncertain and extremely time varying unit. Hence it is important to determine the system's supreme acceptable loadability limit called maximum loadability point to accommodate the sudden variation of load demand. Nowadays the enhancement of the maximum loadability point is essential to meet the rapid growth of load demand by improvising the system's load utilization capacity. Flexible AC Transmission system devices (FACTS) with their speed and flexibility will play a key role in enhancing the controllability and power transfer capability of the system. Considering the theme of FACTS devices in the loadability limit enhancement, in this paper maximum loadability limit determination and its enhancement are prepared with the help of swarm intelligence based metaheuristic Firefly Algorithm(FFA) by finding the optimal loading factor for each load and optimally placing the SVC (Shunt Compensation) and TCSC (Series Compensation) FACTS devices in the system. To illuminate the effectiveness of FACTS devices in the loadability enhancement, the line contingency scenario is also concerned in the study. The study of FACTS based maximum system load utilization acceptability point determination is demonstrated with the help of modified IEEE 30 bus, IEEE 57 Bus and IEEE 118 Bus test systems. The results of FACTS devices involvement in determining the maximum loading point enhance the load utilization point in normal state and also help to overcome the system violation in transmission line contingency state. Also the firefly algorithm in determining the maximum loadability point provides better search capability with faster convergence rate compared to that of Particle swarm optimization (PSO) and Differential evolution algorithm.
Firefly Algorithm for Cardinality Constrained MeanVariance Portfolio Optimization Problem with Entropy Diversity Constraint
"... Portfolio optimization (selection) problem is an important and hard optimization problem that, with the addition of necessary realistic constraints, becomes computationally intractable. Natureinspired metaheuristics are appropriate for solving such problems; however, literature review shows that t ..."
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Portfolio optimization (selection) problem is an important and hard optimization problem that, with the addition of necessary realistic constraints, becomes computationally intractable. Natureinspired metaheuristics are appropriate for solving such problems; however, literature review shows that there are very few applications of natureinspired metaheuristics to portfolio optimization problem. This is especially true for swarm intelligence algorithms which represent the newer branch of natureinspired algorithms. No application of any swarm intelligence metaheuristics to cardinality constrained meanvariance (CCMV) portfolio problem with entropy constraint was found in the literature. This paper introduces modified firefly algorithm (FA) for the CCMV portfolio model with entropy constraint. Firefly algorithm is one of the latest, very successful swarm intelligence algorithm; however, it exhibits some deficiencies when applied to constrained problems. To overcome lack of exploration power during early iterations, we modified the algorithm and tested it on standard portfolio benchmark data sets used in the literature. Our proposed modified firefly algorithm proved to be better than other stateoftheart algorithms, while introduction of entropy diversity constraint further improved results.
Fusing Swarm Intelligence and SelfAssembly for Optimizing Echo State Networks
"... Optimizing a neural network's topology is a difficult problem for at least two reasons: the topology space is discrete, and the quality of any given topology must be assessed by assigning many different sets of weights to its connections. These two characteristics tend to cause very "roug ..."
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Optimizing a neural network's topology is a difficult problem for at least two reasons: the topology space is discrete, and the quality of any given topology must be assessed by assigning many different sets of weights to its connections. These two characteristics tend to cause very "rough." objective functions. Here we demonstrate how selfassembly (SA) and particle swarm optimization (PSO) can be integrated to provide a novel and effective means of concurrently optimizing a neural network's weights and topology. Combining SA and PSO addresses two key challenges. First, it creates a more integrated representation of neural network weights and topology so that we have just a single, continuous search domain that permits "smoother" objective functions. Second, it extends the traditional focus of selfassembly, from the growth of predefined target structures, to functional selfassembly, in which growth is driven by optimality criteria defined in terms of the performance of emerging structures on predefined computational problems. Our model incorporates a new way of viewing PSO that involves a population of growing, interacting networks, as opposed to particles. The effectiveness of our method for optimizing echo state network weights and topologies is demonstrated through its performance on a number of challenging benchmark problems.
Big Network Analytics Based on Nonconvex Optimization
, 2015
"... The scientific problems that Big Data faces may be network scientific problems. Network analytics contributes a great deal to networked Big Data processing. Many network issues can be modeled as nonconvex optimization problems and consequently they can be addressed by optimization techniques. In the ..."
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The scientific problems that Big Data faces may be network scientific problems. Network analytics contributes a great deal to networked Big Data processing. Many network issues can be modeled as nonconvex optimization problems and consequently they can be addressed by optimization techniques. In the pipeline of nonconvex optimization techniques, evolutionary computation gives an outlet to handle these problems efficiently. Because, network community discovery is a critical research agenda of network analytics, in this chapter we focus on the evolutionary computation based nonconvex optimization for network community discovery. The single and multiple objective optimization models for the community discovery problem are thoroughly investigated. Several experimental studies are shown to demonstrate the effectiveness of optimization based approach for big network community analytics.
Multipopulation Firefly Algorithm
"... This paper proposes a metaheuristic MultiPopulation Firefly Algorithm (MPFA) for singlemodal optimization using two multipopulation models, i.e., one is based on the island model while the other on the mainlandisland model. The unique characteristics of each subpopulation is evolved independ ..."
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This paper proposes a metaheuristic MultiPopulation Firefly Algorithm (MPFA) for singlemodal optimization using two multipopulation models, i.e., one is based on the island model while the other on the mainlandisland model. The unique characteristics of each subpopulation is evolved independently and the diversity of the entire population is effectively increased. Subpopulations communicate with each other to exchange information in order to expand the search range of the entire population. In line with this, each subpopulation explores a specific part of the search space and contributes its part for exploring the global search space. The main goal of this paper was to analyze the performance between MPFA and the original Firefly Algorithm (FA). Experiments were performed on a CEC 2014 benchmark suite consisting of 16 singleobjective functions and the obtained results show improvements in most of them.
Mathematical Modelling and Parameter Optimization of Pulsating Heat Pipes
"... Proper heat transfer management is important to key electronic components in microelectronic applications. Pulsating heat pipes (PHP) can be an efficient solution to such heat transfer problems. However, mathematical modelling of a PHP system is still very challenging, due to the complexity and mul ..."
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Proper heat transfer management is important to key electronic components in microelectronic applications. Pulsating heat pipes (PHP) can be an efficient solution to such heat transfer problems. However, mathematical modelling of a PHP system is still very challenging, due to the complexity and multiphysics nature of the system. In this work, we present a simplified, twophase heat transfer model, and our analysis shows that it can make good predictions about startup characteristics. Furthermore, by considering parameter estimation as a nonlinear constrained optimization problem, we have used the firefly algorithm to find parameter estimates efficiently. We have also demonstrated that it is possible to obtain good estimates of key parameters using very limited experimental data.