Original Fruit Fly Optimization Algorithm Research Articles

Mechanical Incipient Fault Detection and Performance Analysis Using Adaptive Teager-VMD Method

For large rotating machinery with low speed and heavy load, the incipient fault characteristics of rolling bearings are particularly weak, making it difficult to identify them effectively by direct signal processing methods. To resolve this issue, we propose a novel approach to detecting incipient fault features that combines signal energy enhancement and signal decomposition. First, the structure of a conventional Teager algorithm is modified to further increase the energy of the micro-impact component and hence the impact amplitude. Then, a kind of composite chaotic mapping is constructed to extend the original fruit fly optimization algorithm (FOA) framework, improving the FOA’s randomness and search power. The effective intrinsic mode functions (IMFs) are determined by searching for the optimal combination values of the key parameters of the variational mode decomposition (VMD) with the improved chaotic FOA (ICFOA). The kurtosis index is then used to select the IMFs that are most relevant to the fault characteristics information. Finally, the sensitive components are analyzed to identify multiple early fault characteristics and determine detailed information about the faults. Moreover, the approach is evaluated by a simulation signal and a measured signal. The comprehensive evaluation indicates that the approach has clear advantages over other excellent methods in extracting the incipient fault feature information of the equipment and has great potential for application in engineering.

A Cooperation of the Multileader Fruit Fly and Probabilistic Random Walk Strategies with Adaptive Normalization for Solving the Unconstrained Optimization Problems

A swarm-based nature-inspired optimization algorithm, namely, the fruit fly optimization algorithm (FOA), hasa simple structure and is easy to implement. However, FOA has a low success rate and a slow convergence, because FOA generates new positions around the best location, using a fixed search radius. Several improved FOAs have been proposed. However, their exploration ability is questionable. To make the search process smooth, transitioning from the exploration phase to the exploitation phase, this paper proposes a new FOA, constructed from a cooperation of the multileader and the probabilistic random walk strategies (CPFOA). This involves two population types working together. CPFOAs performance is evaluated by 18 well-known standard benchmarks. The results showed that CPFOA outperforms both the original FOA and its variants, in terms of convergence speed and performance accuracy. The results show that CPFOA can achieve a very promising accuracy, when compared with the well-known competitive algorithms. CPFOA is applied to optimize twoapplications: classifying the real datasets with multilayer perceptron and extracting the parameters of a very compact T-S fuzzy system to model the Box and Jenkins gas furnace data set. CPFOA successfully find parameters with a very high quality, compared with the best known competitive algorithms.

Dynamic Gaussian bare-bones fruit fly optimizers with abandonment mechanism: method and analysis

The Fruit Fly Optimization Algorithm (FOA) is a recent algorithm inspired by the foraging behavior of fruit fly populations. However, the original FOA easily falls into the local optimum in the process of solving practical problems, and has a high probability of escaping from the optimal solution. In order to improve the global search capability and the quality of solutions, a dynamic step length mechanism, abandonment mechanism and Gaussian bare-bones mechanism are introduced into FOA, termed as BareFOA. Firstly, the random and ambiguous behavior of fruit flies during the olfactory phase is described using the abandonment mechanism. The search range of fruit fly populations is automatically adjusted using an update strategy with dynamic step length. As a result, the convergence speed and convergence accuracy of FOA have been greatly improved. Secondly, the Gaussian bare-bones mechanism that overcomes local optimal constraints is introduced, which greatly improves the global search capability of the FOA. Finally, 30 benchmark functions for CEC2017 and seven engineering optimization problems are experimented with and compared to the best-known solutions reported in the literature. The computational results show that the BareFOA not only significantly achieved the superior results on the benchmark problems than other competitive counterparts, but also can offer better results on the engineering optimization design problems.

Multi-population following behavior-driven fruit fly optimization: A Markov chain convergence proof and comprehensive analysis

Fruit fly optimization algorithm (FOA) is a well-known optimization algorithm with a well-designed structure and superiority of fewer parameters, more effortless adjustment, and competitive and fast computation time. Up till now, FOA has been effectively applied to numerous fields, such as financial forecast and medicine, and it achieves favorable results. However, some aspects need to be enhanced when dealing with some function optimization cases. This method is inclined to falling into local optima with a slow convergence. In this paper, the following behavior-driven multi-population FOA is proposed to relieve these drawbacks, which combines the following mechanism of artificial fish swarm algorithm with the unique searching ability of different types of fruit flies. The chaotic global disturbance is introduced to improve the global exploration ability of the original FOA and reduce the probability of advanced FOA falling into the local extreme. The proposed FOA, it is compared with the advanced FOA with several well-established algorithms and the latest improved optimizers in different dimensions horizontally and vertically to substantiate the effectiveness of the. It is also applied advanced FOA to two practical engineering optimization projects. Systematic analysis and experimental data indicate that the advanced FOA variant outperforms the original FOA and the latest improved algorithms. An online repository will support this research at http://aliasgharheidari.com for any communication and guidance for future works.

Boosted hunting-based fruit fly optimization and advances in real-world problems

Fruit fly optimization algorithm (FOA) is a well-established meta-heuristic method with a clear core concept, a straightforward computation, and a code framework that is easy to build. In the case of large scale and multifaceted practical problems, the optimization effect of FOA may be unsatisfactory, and it is prone to stagnation. In this paper, to enrich the exploration and exploitation capability of the classic FOA, an effective whale-inspired hunting strategy is introduced to replace the random search plan of the original FOA, which we named it as WFOA. The proposed WFOA is compared with 9 state-of-the-art FOA’s variants on a comprehensive set of 23 benchmark set and 30 IEEE CEC 2014 functions and advanced algorithms on a set of 21 test functions to validate its effectiveness. In addition, the effectiveness of WFOA is also verified on 20 IEEE CEC 2011 benchmark problems for tackling real-world problems. The statistical data shows that developed components effectively expand the exploration and exploitation capacity of the original FOA.

Multiple Swarm Fruit Fly Optimization Algorithm Based Path Planning Method for Multi-UAVs

The path planning of unmanned aerial vehicles (UAVs) in the threat and countermeasure region is a constrained nonlinear optimization problem with many static and dynamic constraints. The fruit fly optimization algorithm (FOA) is widely used to handle this kind of nonlinear optimization problem. In this paper, the multiple swarm fruit fly optimization algorithm (MSFOA) is proposed to overcome the drawback of the original FOA in terms of slow global convergence speed and local optimum, and then is applied to solve the coordinated path planning problem for multi-UAVs. In the proposed MSFOA, the whole fruit fly swarm is divided into several sub-swarms with multi-tasks in order to expand the searching space to improve the searching ability, while the offspring competition strategy is introduced to improve the utilization degree of each calculation result and realize the exchange of information among various fruit fly sub-swarms. To avoid the collision among multi-UAVs, the collision detection method is also proposed. Simulation results show that the proposed MSFOA is superior to the original FOA in terms of convergence and accuracy.

A new fruit fly optimization algorithm enhanced support vector machine for diagnosis of breast cancer based on high-level features

BackgroundIt is of great clinical significance to develop an accurate computer aided system to accurately diagnose the breast cancer. In this study, an enhanced machine learning framework is established to diagnose the breast cancer. The core of this framework is to adopt fruit fly optimization algorithm (FOA) enhanced by Levy flight (LF) strategy (LFOA) to optimize two key parameters of support vector machine (SVM) and build LFOA-based SVM (LFOA-SVM) for diagnosing the breast cancer. The high-level features abstracted from the volunteers are utilized to diagnose the breast cancer for the first time.ResultsIn order to verify the effectiveness of the proposed method, 10-fold cross-validation method is used to make comparison among the proposed method, FOA-SVM (model based on original FOA), PSO-SVM (model based on original particle swarm optimization), GA-SVM (model based on genetic algorithm), random forest, back propagation neural network and SVM. The main novelty of LFOA-SVM lies in the combination of FOA with LF strategy that enhances the quality for FOA, thus improving the convergence rate of the FOA optimization process as well as the probability of escaping from local optimal solution.ConclusionsThe experimental results demonstrate that the proposed LFOA-SVM method can beat other counterparts in terms of various performance metrics. It can very well distinguish malignant breast cancer from benign ones and assist the doctor with clinical diagnosis.

Fusion Recognition of Shearer Coal-Rock Cutting State Based on Improved RBF Neural Network and D-S Evidence Theory

Accurate recognition of coal-rock cutting state is a prerequisite for intelligent operation of shearer, so as to achieve safe and efficient production in coal mines. This paper takes the sound signal, Y-axis and Z-axis vibration signals as analytic objects and proposes a fusion recognition method for shearer coal-rock cutting state via the combination of improved radical basis function neural network (RBFNN) and Dempster-Shafer (D-S) evidence theory. First of all, on the basis of original fruit fly optimization algorithm (FOA), the location updating mechanism of moth-flame optimization (MFO) is used to improve the convergence performance and exploration ability of FOA. Thus, a hybrid optimization algorithm of MFO-FOA is accordingly designed and some simulations are conducted to verify the effectiveness and superiority. Then, the optimal network parameters of RBFNN are found out by using proposed MFO-FOA to realize the excellent generalization ability and predictive performance. Moreover, the collected signals are decomposed by variational mode decomposition, and the envelope entropy and kurtosis are used to extract the features of first three intrinsic mode function components. The feature vectors obtained from three-type sensor data are utilized to construct the RBFNN classifiers. Besides, the D-S evidence theory with evidence correlation coefficient is introduced to fuse the preliminary identification results of three RBFNN classifiers. Finally, a self-designed experimental platform for shearer cutting coal-rock is built and some experiments are provided. The experimental results based on measured data demonstrate that the proposed method can effectively identify the coal-rock cutting state with higher accuracy.

A new improved fruit fly optimization algorithm IAFOA and its application to solve engineering optimization problems

Nature-inspired algorithms are widely used in mathematical and engineering optimization. As one of the latest swarm intelligence-based methods, fruit fly optimization algorithm (FOA) was proposed inspired by the foraging behavior of fruit fly. In order to overcome the shortcomings of original FOA, a new improved fruit fly optimization algorithm called IAFOA is presented in this paper. Compared with original FOA, IAFOA includes four extra mechanisms: 1) adaptive selection mechanism for the search direction, 2) adaptive adjustment mechanism for the iteration step value, 3) adaptive crossover and mutation mechanism, and 4) multi-sub-swarm mechanism. The adaptive selection mechanism for the search direction allows the individuals to search for global optimum based on the experience of the previous iteration generations. According to the adaptive adjustment mechanism, the iteration step value can change automatically based on the iteration number and the best smell concentrations of different generations. Besides, the adaptive crossover and mutation mechanism introduces crossover and mutation operations into IAFOA, and advises that the individuals with different fitness values should be operated with different crossover and mutation probabilities. The multi-sub-swarm mechanism can spread optimization information among the individuals of the two sub-swarms, and quicken the convergence speed. In order to take an insight into the proposed IAFOA, computational complexity analysis and convergence analysis are given. Experiment results based on a group of 29 benchmark functions show that IAFOA has the best performance among several intelligent algorithms, which include five variants of FOA and five advanced intelligent optimization algorithms. Then, IAFOA is used to solve three engineering optimization problems for the purpose of verifying its practicability, and experiment results show that IAFOA can generate the best solutions compared with other ten algorithms.

A fruit fly optimization algorithm with a traction mechanism and its applications

The original fruit fly optimization algorithm, as well as some of its improved versions, may fail to find the function extremum when it falls far from the origin point or in the negative range. To address this problem, in this article, we propose a new fruit fly optimization algorithm, named as the traction fruit fly optimization algorithm, which is mainly based on the combination of “traction population” and dynamic search radius. In traction fruit fly optimization algorithm, traction population consists of the worst individual recorded in the iterative process, the individual in the center of the interval, and the best fruit flies individual through different transformations, which is used to avoid the algorithm stopping at a local optimal. Moreover, our dynamic search radius strategy will ensure a wide search range in the early stage and enhance the local search capability in the latter part of the algorithm. Extensive experiment results show that traction fruit fly optimization algorithm is superior t...

An improvement in fruit fly optimization algorithm by using sign parameters

The fruit fly optimization algorithm (FOA) has been developed by inspiring osphresis and vision behaviors of the fruit flies to solve continuous optimization problems. As many researchers know that FOA has some shortcomings, this study presents an improved version of FOA to remove with these shortcomings in order to improve its optimization performance. According to the basic version of FOA, the candidate solutions could not take values those are negative as well as stated in many studies in the literature. In this study, two sign parameters are added into the original FOA to consider not only the positive side of the search space, but also the whole. To experimentally validate the proposed approach, namely signed FOA, SFOA for short, 21 well-known benchmark problems are considered. In order to demonstrate the effectiveness and success of the proposed method, the results of the proposed approach are compared with the results of the original FOA, results of the two different state-of-art versions of particle swarm optimization algorithm, results of the cuckoo search optimization algorithm and results of the firefly optimization algorithm. By analyzing experimental results, it can be said that the proposed approach achieves more successful results on many benchmark problems than the compared methods, and SFOA is presented as more equal and fairer in terms of screening the solution space.

Adaptive Wavelet Threshold Denoising Method for Machinery Sound Based on Improved Fruit Fly Optimization Algorithm

As the sound signal of a machine contains abundant information and is easy to measure, acoustic-based monitoring or diagnosis systems exhibit obvious superiority, especially in some extreme conditions. However, the sound directly collected from industrial field is always polluted. In order to eliminate noise components from machinery sound, a wavelet threshold denoising method optimized by an improved fruit fly optimization algorithm (WTD-IFOA) is proposed in this paper. The sound is firstly decomposed by wavelet transform (WT) to obtain coefficients of each level. As the wavelet threshold functions proposed by Donoho were discontinuous, many modified functions with continuous first and second order derivative were presented to realize adaptively denoising. However, the function-based denoising process is time-consuming and it is difficult to find optimal thresholds. To overcome these problems, fruit fly optimization algorithm (FOA) was introduced to the process. Moreover, to avoid falling into local extremes, an improved fly distance range obeying normal distribution was proposed on the basis of original FOA. Then, sound signal of a motor was recorded in a soundproof laboratory, and Gauss white noise was added into the signal. The simulation results illustrated the effectiveness and superiority of the proposed approach by a comprehensive comparison among five typical methods. Finally, an industrial application on a shearer in coal mining working face was performed to demonstrate the practical effect.

Cutting State Diagnosis for Shearer through the Vibration of Rocker Transmission Part with an Improved Probabilistic Neural Network.

In order to achieve more accurate and reliable identification of shearer cutting state, this paper employs the vibration of rocker transmission part and proposes a diagnosis method based on a probabilistic neural network (PNN) and fruit fly optimization algorithm (FOA). The original FOA is modified with a multi-swarm strategy to enhance the search performance and the modified FOA is utilized to optimize the smoothing parameters of the PNN. The vibration signals of rocker transmission part are decomposed by the ensemble empirical mode decomposition and the Kullback-Leibler divergence is used to choose several appropriate components. Forty-five features are extracted to estimate the decomposed components and original signal, and the distance-based evaluation approach is employed to select a subset of state-sensitive features by removing the irrelevant features. Finally, the effectiveness of the proposed method is demonstrated via the simulation studies of shearer cutting state diagnosis and the comparison results indicate that the proposed method outperforms the competing methods in terms of diagnosis accuracy.

Hybridization of Fruit Fly Optimization Algorithm and Firefly Algorithm for Solving Nonlinear Programming Problems

We propose a novel hybrid algorithm named, FOA-FA to solve the nonlinear programming problems (NLPPs). The main feature of the hybrid algorithm is to integrate the strength of fruit fly optimization algorithm (FOA) in handling continuous optimization and the merit of firefly algorithm (FA) in achieving robust exploration. The methodology of the proposed algorithm consists of two phases. The first one employs a variation on original FOA employing a new adaptive radius mechanism (ARM) for exploring the whole scope around the fruit flies locations to overcome the drawbacks of original FOA which has been continues for the nonnegative orthant problems. The second one incorporates FA to update the previous best locations of fruit flies to avoid the premature convergence. The hybrid algorithm speeds up the convergence and improves the algorithm’s performance. The proposed FOA-FA algorithm is tested on several benchmark problems and two engineering applications. The numerical comparisons have demonstrated its effectiveness and efficiency.

Multi-swarm fruit fly optimization algorithm for structural damage identification

In this paper, the Multi-Swarm Fruit Fly Optimization Algorithm (MFOA) is presented for structural damage identification using the first several natural frequencies and mode shapes. We assume damage only leads to the decrease of element stiffness. The differences on natural frequencies and mode shapes of damaged and intact state of a structure are used to establish the objective function, which transforms a damage identification problem into an optimization problem. The effectiveness and accuracy of MFOA are demonstrated by three different structures. Numerical results show that the MFOA has a better capacity for structural damage identification than the original Fruit Fly Optimization Algorithm (FOA) does.

Parameter identification of BIPT system using chaotic-enhanced fruit fly optimization algorithm

Bidirectional inductive power transfer (BIPT) system facilitates contactless power transfer between two sides and across an air-gap, through weak magnetic coupling. Typically, this system is nonlinear high order system which includes nonlinear switch components and resonant networks, developing of accurate model is a challenging task. In this paper, a novel technique for parameter identification of a BIPT system is presented by using chaotic-enhanced fruit fly optimization algorithm (CFOA). The fruit fly optimization algorithm (FOA) is a new meta-heuristic technique based on the swarm behavior of the fruit fly. This paper proposes a novel CFOA, which employs chaotic sequence to enhance the global optimization capacity of original FOA. The parameter identification of the BIPT system is formalized as a multi-dimensional optimization problem, and an objective function is established minimizing the errors between the estimated and measured values. All the 11 parameters of this system (Lpi, LT, Lsi, Lso, CT, Cs, M, Rpi, RT, Rsi and Rso) can be identified simultaneously using measured input–output data. Simulations show that the proposed parameter identification technique is robust to measurements noise and variation of operation condition and thus it is suitable for practical application.

Fruit fly optimization algorithm based on differential evolution and its application on gasification process operation optimization

The expression of the smell concentration judgment value is significantly important in the application of the fruit fly optimization algorithm (FOA). The original FOA can only solve problems that have optimal solutions in zero vicinity. To make FOA more universal for the continuous optimization problems, especially for those problems with optimal solutions that are not zero. This paper proposes an improved fruit fly optimization algorithm based on differential evolution (DFOA) by modifying the expression of the smell concentration judgment value and by introducing a differential vector to replace the stochastic search. Through numerical experiments based on 12 benchmark instances, experimental results show that the improved DFOA has a stronger global search ability, faster convergence, and convergence stability in high-dimensional functions than the original FOA and evolutionary algorithms from literature. The DFOA is also applied to optimize the operation of the Texaco gasification process by maximizing the syngas yield using two decision variables, i.e., oxygen–coal ratio and coal concentration. The results show that DFOA can quickly get the optimal output, demonstrating the effectiveness of DFOA.

An improved fruit fly optimization algorithm and its application to joint replenishment problems

Fruit fly optimization algorithm (FOA) is one of the recent evolutionary computation approaches. This paper presents an effective and improved FOA (IFOA) for optimizing numerical functions and solving joint replenishment problems (JRPs). In the proposed IFOA, a new method of maintaining the population diversity is developed to enhance the exploration ability. Fruit flies with better fitness values use vision to fly toward a new location, and the others fly randomly in initial search space based on swarm collaboration. In addition, a new parameter to avoid the acquisition of local optimal solution is introduced to implement intelligent searching. Random perturbation is added to the updated initial location to jump out of the local optimum. Comparisons are carried out using 18 benchmark functions to verify the performance of the IFOA. Experimental results show that IFOA has better comprehensive performance than the original FOA, differential evolution algorithm, and particle swarm optimization algorithm. The IFOA is also utilized to solve the typical JRPs that have been proven as non-deterministic polynomial hard problems. Comparative examples reveal that the proposed IFOA can find better solutions than the current best algorithm; thus, it is a potential tool for various complex optimization problems.

On a novel multi-swarm fruit fly optimization algorithm and its application

Swarm intelligence is a research field that models the collective behavior in swarms of insects or animals. Recently, a kind of Drosophila (fruit fly) inspired optimization algorithm, called fruit fly optimization algorithm (FOA), has been developed. This paper presents a variation on original FOA technique, named multi-swarm fruit fly optimization algorithm (MFOA), employing multi-swarm behavior to significantly improve the performance. In the MFOA approach, several sub-swarms moving independently in the search space with the aim of simultaneously exploring global optimal at the same time, and local behavior between sub-swarms are also considered. In addition, several other improvements for original FOA technique is also considered, such as: shrunk exploring radius using osphresis, and a new distance function. Application of the proposed MFOA approach on several benchmark functions and parameter identification of synchronous generator shows an effective improvement in its performance over original FOA technique.

Fruit fly optimization algorithm based on bacterial chemotaxis

In this paper,attraction and exclusion operations of bacterial chemotaxis were introduced into original Fruit Fly Optimization Algorithm(FOA),and FOA based on Bacterial Chemotaxis(BCFOA) was proposed.Exclusion(to escape the worst individual) or attraction(to be attracted by the best individual) was decided to perform by judging the fitness variance is zero or no,so that the problem of premature convergence caused by the loss of population diversity,which resulted from the fact that individuals only were attracted by the best one in FOA,was solved.The experimental results show that the new algorithm has the advantages of better global searching ability,and faster and more precise convergence.