Showing papers on "Firefly algorithm published in 2015"

Earthworm optimisation algorithm: a bio-inspired metaheuristic algorithm for global optimisation problems

Abstract: Earthworms can aerate the soil with their burrowing action and enrich the soil with their waste nutrients. Inspired by the earthworm contribution in nature, a new kind of bio-inspired metaheuristic algorithm, called earthworm optimisation algorithm (EWA), is proposed in this paper. The EWA method is inspired by the two kinds of reproduction (Reproduction 1 and Reproduction 2) of the earthworms. Reproduction 1 generates only one offspring by itself. Reproduction 2 is to generate one or more than one offspring at one time, and this can successfully be done by nine improved crossover operators. In addition, Cauchy mutation (CM) is added to EWA method. Nine different EWA methods with one, two and three offsprings based on nine improved crossover operators are respectively proposed. The results show that EWA23 performs the best and it can find the better fitness on most benchmarks than others.

TSA: Tree-seed algorithm for continuous optimization

Abstract: This paper presents a new intelligent optimizer based on the relation between trees and their seeds for continuous optimization. The new method is in the field of heuristic and population-based search. The location of trees and seeds on n-dimensional search space corresponds with the possible solution of an optimization problem. One or more seeds are produced from the trees and the better seed locations are replaced with the locations of trees. While the new locations for seeds are produced, either the best solution or another tree location is considered with the tree location. This consideration is performed by using a control parameter named as search tendency (ST), and this process is executed for a pre-defined number of iterations. These mechanisms provide to balance exploitation and exploration capabilities of the proposed approach. In the experimental studies, the effects of control parameters on the performance of the method are firstly examined on 5 well-known basic numeric functions. The performance of the proposed method is also investigated on the 24 benchmark functions with 2, 3, 4, 5 dimensions and multilevel thresholding problems. The obtained results are also compared with the results of state-of-art methods such as artificial bee colony (ABC) algorithm, particle swarm optimization (PSO), harmony search (HS) algorithm, firefly algorithm (FA) and the bat algorithm (BA). Experimental results show that the proposed method named as TSA is better than the state-of-art methods in most cases on numeric function optimization and is an alternative optimization method for solving multilevel thresholding problem.

Adaptive firefly algorithm with chaos for mechanical design optimization problems

Abstract: Proposing an extension of firefly algorithmEmployment of picewise chaos, for an further enhanced diversityMaking use of a simple but effective constraint handling methodMaking use of an improved local search procedure Firefly algorithm (FA) is a newer member of bio-inspired meta-heuristics, which was originally proposed to find solutions to continuous optimization problems Popularity of FA has increased recently due to its effectiveness in handling various optimization problems To enhance the performance of the FA even further, an adaptive FA is proposed in this paper to solve mechanical design optimization problems, and the adaptivity is focused on the search mechanism and adaptive parameter settings Moreover, chaotic maps are also embedded into AFA for performance improvement It is shown through experimental tests that some of the best known results are improved by the proposed algorithm

A highly accurate firefly based algorithm for heart disease prediction

Abstract: Rough sets and firefly algorithms is proposed to find optimal attribute reductions.Interval type-2 fuzzy logic system is used to predict heart disease.Proposed system is effective with results of fewer features and higher accuracy. This paper proposes a heart disease diagnosis system using rough sets based attribute reduction and interval type-2 fuzzy logic system (IT2FLS). The integration between rough sets based attribute reduction and IT2FLS aims to handle with high-dimensional dataset challenge and uncertainties. IT2FLS utilizes a hybrid learning process comprising fuzzy c-mean clustering algorithm and parameters tuning by chaos firefly and genetic hybrid algorithms. This learning process is computationally expensive, especially when employed with high-dimensional dataset. The rough sets based attribute reduction using chaos firefly algorithm is investigated to find optimal reduction which therefore reduces computational burden and enhances performance of IT2FLS. Experiment results demonstrate a significant dominance of the proposed system compared to other machine learning methods namely Naive Bayers, support vector machines, and artificial neural network. The proposed model is thus useful as a decision support system for heart disease diagnosis.

Chaotic fruit fly optimization algorithm

Abstract: Display Omitted Development of new method named chaotic fruit fly optimization algorithm (CFOA).Fruit fly algorithm (FOA) is integrated with ten different chaos maps.Novel algorithm is tested on ten different well known benchmark problems.CFOA is compared with FOA, FOA with Levy distribution, and similar chaotic methods.Experiments show superiority of CFOA in terms of obtained statistical results. Fruit fly optimization algorithm (FOA) is recently presented metaheuristic technique that is inspired by the behavior of fruit flies. This paper improves the standard FOA by introducing the novel parameter integrated with chaos. The performance of developed chaotic fruit fly algorithm (CFOA) is investigated in details on ten well known benchmark problems using fourteen different chaotic maps. Moreover, we performed comparison studies with basic FOA, FOA with Levy flight distribution, and other recently published chaotic algorithms. Statistical results on every optimization task indicate that the chaotic fruit fly algorithm (CFOA) has a very fast convergence rate. In addition, CFOA is compared with recently developed chaos enhanced algorithms such as chaotic bat algorithm, chaotic accelerated particle swarm optimization, chaotic firefly algorithm, chaotic artificial bee colony algorithm, and chaotic cuckoo search. Overall research findings show that FOA with Chebyshev map show superiority in terms of reliability of global optimality and algorithm success rate.

A novel hybrid gravitational search and pattern search algorithm for load frequency control of nonlinear power system

Abstract: Selection of objective function and controller structure is vital for controller design.An objective function using ITAE, damping ratio and settling times is proposed.The concept is applied to design an hGSA-PS-based PI/PID controller for LFC.Nonlinear interconnected power system model with GRC, GDB and time delay is considered. In this paper, a hybrid gravitational search algorithm (GSA) and pattern search (PS) technique is proposed for load frequency control (LFC) of multi-area power system. Initially, various conventional error criterions are considered, the PI controller parameters for a two-area power system are optimized employing GSA and the effect of objective function on system performance is analyzed. Then GSA control parameters are tuned by carrying out multiple runs of algorithm for each control parameter variation. After that PS is employed to fine tune the best solution provided by GSA. Further, modifications in the objective function and controller structure are introduced and the controller parameters are optimized employing the proposed hybrid GSA and PS (hGSA-PS) approach. The superiority of the proposed approach is demonstrated by comparing the results with some recently published modern heuristic optimization techniques such as firefly algorithm (FA), differential evolution (DE), bacteria foraging optimization algorithm (BFOA), particle swarm optimization (PSO), hybrid BFOA-PSO, NSGA-II and genetic algorithm (GA) for the same interconnected power system. Additionally, sensitivity analysis is performed by varying the system parameters and operating load conditions from their nominal values. Also, the proposed approach is extended to two-area reheat thermal power system by considering the physical constraints such as reheat turbine, generation rate constraint (GRC) and governor dead band (GDB) nonlinearity. Finally, to demonstrate the ability of the proposed algorithm to cope with nonlinear and unequal interconnected areas with different controller coefficients, the study is extended to a nonlinear three unequal area power system and the controller parameters of each area are optimized using proposed hGSA-PS technique.

Binary flower pollination algorithm and its application to feature selection

Abstract: The problem of feature selection has been paramount in the last years, since it can be as important as the classification step itself. The main goal of feature selection is to find out the subset of features that optimize some fitness function, often in terms of a classifier’s accuracy or even the computational burden for extracting each feature. Therefore, the approaches to feature selection can be modeled as optimization tasks. In this chapter, we evaluate a binary-constrained version of the Flower Pollination Algorithm (FPA) for feature selection, in which the search space is a boolean lattice where each possible solution, or a string of bits, denotes whether a feature will be used to compose the final set. Numerical experiments over some public and private datasets have been carried out and comparison with Particle Swarm Optimization, Harmony Search and Firefly Algorithm has demonstrated the suitability of the FPA for feature selection.

Smart Artificial Firefly Colony Algorithm-Based Support Vector Regression for Enhanced Forecasting in Civil Engineering

Abstract: Advanced data mining techniques are potential tools for solving civil engineering (CE) problems. This study proposes a novel smart artificial firefly colony algorithm-based support vector regression (SAFCA-SVR) system that integrates firefly algorithm (FA), chaotic maps, adaptive inertia weight, Levy flight, and least squares support vector regression (LS-SVR). First, adaptive approach and randomization methods are incorporated in FA to construct a novel and highly effective metaheuristic algorithm for global optimization. The enhanced FA is then used to optimize parameters in LS-SVR model. The proposed system is validated by comparing its performance with those of empirical methods and previous works via cross-validation algorithm and hypothesis test through the real-world engineering cases. Specifically, high-performance concrete, resilient modulus of subgrade soils, and building cooling load are used as case studies. The SAFCA-SVR achieved 8.8%–91.3% better error rates than those of previous works. Analytical results confirm that using the proposed hybrid system significantly improves the accuracy in solving CE problems.

A hybrid discrete firefly algorithm for solving multi-objective flexible job shop scheduling problems

Abstract: Firefly algorithm FA is a nature-inspired optimisation algorithm that can be successfully applied to continuous optimisation problems. However, lot of practical problems are formulated as discrete optimisation problems. In this paper a hybrid discrete firefly algorithm HDFA is proposed to solve the multi-objective flexible job shop scheduling problem FJSP. FJSP is an extension of the classical job shop scheduling problem that allows an operation to be processed by any machine from a given set along different routes. Three minimisation objectives - the maximum completion time, the workload of the critical machine and the total workload of all machines are considered simultaneously. This paper also proposes firefly algorithms discretisation which consists of constructing a suitable conversion of the continuous functions as attractiveness, distance and movement, into new discrete functions. In the proposed algorithm discrete firefly algorithm DFA is combined with local search LS method to enhance the searching accuracy and information sharing among fireflies. The experimental results on the well-known benchmark instances and comparison with other recently published algorithms shows that the proposed algorithm is feasible and an effective approach for the multi-objective flexible job shop scheduling problems.

Hybridizing firefly algorithms with a probabilistic neural network for solving classification problems

Abstract: Hybridizes the firefly algorithm with simulated annealing, where simulated annealing is applied to control the randomness step inside the firefly algorithm.A Levy flight is embedded within the firefly algorithm to better explore the search space.A combination of firefly, Levy flight and simulated annealing is investigated to further improve the solution. Classification is one of the important tasks in data mining. The probabilistic neural network (PNN) is a well-known and efficient approach for classification. The objective of the work presented in this paper is to build on this approach to develop an effective method for classification problems that can find high-quality solutions (with respect to classification accuracy) at a high convergence speed. To achieve this objective, we propose a method that hybridizes the firefly algorithm with simulated annealing (denoted as SFA), where simulated annealing is applied to control the randomness step inside the firefly algorithm while optimizing the weights of the standard PNN model. We also extend our work by investigating the effectiveness of using Levy flight within the firefly algorithm (denoted as LFA) to better explore the search space and by integrating SFA with Levy flight (denoted as LSFA) in order to improve the performance of the PNN. The algorithms were tested on 11 standard benchmark datasets. Experimental results indicate that the LSFA shows better performance than the SFA and LFA. Moreover, when compared with other algorithms in the literature, the LSFA is able to obtain better results in terms of classification accuracy.

Elephant Search Algorithm for optimization problems

Abstract: A novel bio-inspired optimization algorithm called Elephant Search Algorithm (ESA) is proposed in this paper. ESA emerges from the hybridization of evolutionary mechanism and dual balancing of exploitation and exploration. The design of ESA is inspired by the behavioral characteristics of elephant herds; hence the name Elephant Search Algorithm which divides the search agents into two groups representing the dual search patterns. The male elephants are search agents that outreach to different dimensions of search space afar; the female elephants form groups of search agents doing local search at certain close proximities. By computer simulation, ESA is shown to outperform other metaheuristic algorithms over some benchmarking optimization functions. In terms of fitness values in optimization, ESA is ranked after Firefly algorithm showing superior performance over the other ones. The performance of ESA is most stable when compared to all other metaheuristic algorithms.

Firefly Optimization Algorithm for Feature Selection

Abstract: In this paper, a system for feature selection based on firefly algorithm (FFA) optimization is proposed. Data sets ordinarily includes a huge number of attributes, with irrelevant and redundant attributes. Redundant and irrelevant attributes might reduce the classification accuracy because of the large search space. The main goal of attribute reduction is to choose a subset of relevant attributes from a huge number of available attributes to obtain comparable or even better classification accuracy from using all attributes. A system for feature selection is proposed in this paper using a modified version of the firefly algorithm (FFA) optimization. The modified FFA algorithm adaptively balance the exploration and exploitation to quickly find the optimal solution. FFA is a new evolutionary computation technique, inspired by the flash lighting process of fireflies. The FFA can quickly search the feature space for optimal or near-optimal feature subset minimizing a given fitness function. The proposed fitness function used incorporate both classification accuracy and feature reduction size. The proposed system was tested on eighteen data sets and proves advance over other search methods as particle swarm optimization (PSO) and genetic algorithm (GA) optimizers commonly used in this context using different evaluation indicators.

Optimal control of hydraulically driven parallel robot platform based on firefly algorithm

Abstract: A new cascade load force control design for a parallel robot platform is proposed. A parameter search for a proposed cascade controller is difficult because there is no methodology to set the parameters and the search space is broad. A parameter search based on firefly algorithm (FA) is suggested to effectively search the parameters of the cascade controller. We used unified mathematical model of hydraulic actuator of parallel robot platform. These equations are readily applicable to various types of proportional valves, and they unify the cases of critical center, overlapped and underlapped valves. These unified model equations are useful for nonlinear controller design. The optimal results are compared to those obtained from other metaheuristic algorithms: GA, PSO and CS. A comparative study is also made between proposed optimal tuned cascade control using FA and well-tuned PID controller. Simulation results show the advantages of the proposed optimal tuned cascade controller using FA to solve a formulated tracking problem.

Comments on Optimized gray-scale image watermarking using DWT-SVD and Firefly Algorithm

Abstract: False positive detection is the major issue with the SVD-based image watermarking.It analyzes the security weakness of the scheme.Some feasible solutions to address the above issue are suggested. This study shows that a recently proposed image watermarking scheme in the paper "Optimized gray-scale image watermarking using DWT-SVD and Firefly Algorithm" (Agarwal, Mishra, Sharma, & Bedi, 2014) has a fundamental flaw in its design. The extracted watermark is not the embedded watermark, actually it is determined by the reference watermark, which leads to false positive detection problem. Hence, it invalidates the objective of the scheme and cannot be used for the protection of rightful ownership. The weakness of the scheme designing is illustrated with an example.

Modified cuckoo optimization algorithm (MCOA) to solve graph coloring problem

Abstract: Novel discrete approach for combinational optimization based on cuckoo optimization algorithm (COA).Redefining the difference concept between two habitats as a differential list of movements.Proposed method enable to solve non-permutation problems.Modifying egg laying and immigration phase of COA in the proposed discrete cuckoo optimization algorithm (DCOA).High quality results obtained for graph coloring problems. In recent years, various heuristic optimization methods have been developed. Many of these methods are inspired by swarm behaviors in nature, such as particle swarm optimization (PSO), firefly algorithm (FA) and cuckoo optimization algorithm (COA). Recently introduced COA, has proven its excellent capabilities, such as faster convergence and better global minimum achievement. In this paper a new approach for solving graph coloring problem based on COA was presented. Since COA at first was presented for solving continuous optimization problems, in this paper we use the COA for the graph coloring problem, we need a discrete COA. Hence, to apply COA to discrete search space, the standard arithmetic operators such as addition, subtraction and multiplication existent in COA migration operator based on the distance's theory needs to be redefined in the discrete space. Redefinition of the concept of the difference between the two habitats as the list of differential movements, COA is equipped with a means of solving the discrete nature of the non-permutation. A set of graph coloring benchmark problems are solved and its performance is compared with some well-known heuristic search methods. The obtained results confirm the high performance of the proposed method.