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Firefly algorithm is a population-based algorithm, because the search process is simple and easy to implement, it has received extensive attention from scholars. However, the algorithm has low accuracy and is easy to fall into local optimum. Therefore, an improved firefly algorithm based on gender difference (GDFA) is proposed to enhance the performance of the algorithm in this paper. In the basic firefly algorithm, fireflies have no gender distinction, which is not consistent with reality. According to this point, fireflies are divided into two subgroups based on gender. In addition, different update equations are designed for two subgroups on the basis of their moving ways. Male fireflies are guided by two randomly selected female fireflies to focus on global search and female fireflies move to the best male firefly to implement local search, which aim to balance the exploration and exploitation of the algorithm. To further improve the search accuracy, the chaos search is conducted near the best position of the population. Several simulation experiments and constrained optimization problems are carried out to verify the effectiveness of GDFA, and the time complexity of GDFA is analyzed. Finally, the convergence analysis of proposed algorithm is given based on Markov chain theory.

In this paper, a hybridization model based on culture algorithm and Artificial Bee Colony is proposed. The objective of the hybrid model is mainly to get benefit of the previous knowledge gained by predecessor foragers which help bees searching for food sources in potential positions. The proposed CB-ABC focuses on the kind of information in the belief space that should be stored to reduce promising solutions’ area. Moreover, CB-ABC divides the population into two groups of individuals, one group updates by the heritage of best previous solutions and the other group updates by self-adaptive information. The performance of the new algorithm has been validated on a variety of numerical testbench functions (ranging from CEC 2005 and CEC 2017) and compared to standard ABC and other variants named ABCM, HPA and CABCA as well. The proposed algorithm proves its success as well when applied on Wireless Sensor Network (WSN) to locate them accurately. To validate the performance of CB-ABC it is compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Cuckoo Search (CS), Firefly Algorithm (FA) and Parallel Firefly Algorithm (PFA). CB-ABC shows the least average error value compared to the standard ABC as well as the other algorithms previously mentioned in the comparison. Moreover, CB-ABC succeeds in reducing the number of iterations as well as function evaluations to 17% of those of the standard ABC to 20% of those obtained by ABCM during solving WSN localization problem. CB-ABC’s parametric study (heritage size and offspring ratio) has been carried out as well.

Production distribution network (PDN) planning problems in multi echelon status are commonly complex with dynamic relationships that cause several uncertainties in different parameters of the network. In this paper, we formulate a multi echelon PDN to deliver products to customers with uncertain demand in the least time with uncertain delivery lead time. The proposed network is including multi supplier, producer, potential depot, retailer and inland and outland customer in multi time period horizon. A stochastic multi objective model with maximizing total profit of the system and minimizing total delivery lead time is designed. We apply chance constraints approach to cover the uncertainty of the model and introduce two heuristic methods named selective firefly algorithm (SFA) and ranking genetic algorithm (RGA) in order to solve several sized especially real world instances. Finally, the performance of two proposed algorithms is examined with solving several sized instances. The results indicate average improvement 6.12% and 8.93% with applying SFA and RGA, respectively.

Several methods based on the behaviour of biological systems have been proposed during the last years to solve a wide range of problems. Ant algorithms and Firefly algorithms are two methods of this type. The Ant-Tree for Colour Quantisation algorithm was recently proposed to quantise colour images. It is a clustering-based method which creates a tree structure where the pixels of the image are grouped in subtrees according to their similarity. A threshold value determines if the similarity is sufficient to associate a pixel to a subtree. The computation of that threshold includes a parameter in ( 0 , 1 ] , whose value must be set taking into account the number of colours of the quantised image. Since this parameter influences the quality of the final image, it is recommended to perform several tests with different values of that parameter and then select the best result. In order to reduce the influence of said parameter and improve the resulting image, this paper proposes to combine the Ant-Tree for Colour Quantisation algorithm with the Firefly algorithm. Computational experiments show that the new method generates better images than some popular colour quantisation methods such as Median-cut, Octree, Neuquant, Wu’s method or the Variance-based method.

Oil Spills are one the major environmental hazards in modern era. It needs to be understood; its origin and trajectory duly analyzed and tracked; so as to confine it in a limited spatial domain of least contamination. In this study, we propose a Modified Firefly Algorithm (PropFA) which is closely based on Firefly Algorithm (Yang, 2008). Unlike Firefly Algorithm, no parameter tuning is required in PropFA as all parameters are dynamically calculated based on present, past and expected objective function values; thus maintaining a proper balance between “exploration” and “exploitation”. The proposed algorithm is evaluated over 18 Classical Benchmark Functions, 14 Benchmark Functions of CEC-2005 (Real-Parameter Optimization) and 28 Benchmark Functions of CEC-2013 (Special Session on Real-Parameter Optimization). PropFA is compared to modern evolutionary and metaheuristic algorithms like PSO, CMA-ES, RC-MEMETIC, EDA etc. The numerical results show that the PropFA enhances the performance of Firefly Algorithm; successfully avoids local minima across dimensions and has a fast convergence. PropFA is then used to model a system for distributed control of swarm UAVs (UnManned Aerial Vehicles) to efficiently search, confine and track the shape and area of irregular dynamic oil spills for fast containment and trajectory estimation. The proposed method is also used to predict boom placement locations around the spill for its efficient confinement. Experimental results revealed that the proposed method as applied on a swarm UAV size of 30 was able to search, fully confine and track an expanding oil spill as large as 16 km 2 for 9 min.

Nowadays, the use of metaheuristic algorithms has dramatically increased in order to achieve the optimal solution in solving continuous optimization problems. In this paper, a new metaheuristic algorithm that is inspired by farmland fertility in nature is presented; this algorithm divides into several parts of the farmland, and to optimize solutions of each section with optimal efficiency of two types in internal and external memory. In order to evaluate the farmland fertility, we simulated it on 20 main function of mathematical optimization that is important to evaluate this type of algorithms and the results displayed. This farmland fertility has been compared with other metaheuristic algorithms such as; artificial bee colony (ABC), firefly algorithm (FA), harmony search (HS), particle swarm optimization (PSO), differential evolution (DE), bat algorithm (BA), and improved PSO and the results are displayed clearly. Simulations show that the farmland fertility often acts better than other metaheuristic algorithms. The farmland fertility in problems with smaller dimensions problems has been able to act as a strong metaheuristic algorithm and it has optimized problems nicely. Furthermore, the farmland fertility in problems with larger dimensions has been able to perform better than other algorithms. The effectiveness of other algorithms decreases significantly with number of dimensions and the farmland fertility obtains better results than other algorithms.

This paper presents a chaotic crow search algorithm (CCSA) for solving fractional optimization problems (FOPs). To refine the global convergence speed and enhance the exploration/exploitation tendencies, the proposed CCSA integrates chaos theory (CT) into the CSA. CT is introduced to tune the parameters of the standard CSA, yielding four variants, with the best chaotic variant being investigated. The performance of the proposed CCSA is validated on twenty well-known fractional benchmark problems. Moreover, it is validated on a fractional economic environmental power dispatch problem by attempting to minimize the ratio of total emissions to total fuel cost. Finally, the proposed CCSA is compared with the standard CSA, particle swarm optimization (PSO), firefly algorithm (FFA), dragonfly algorithm (DA) and grey wolf algorithm (GWA). Additionally, the efficiency of the proposed CCSA is justified using the non parametric Wilcoxon signed-rank test. The experimental results prove that the proposed CCSA outperforms other algorithms in terms of quality and reliability.

This paper presents an efficient non-iterative hybrid empirical mode decomposition (EMD) and kernel ridge regression (KRR) for significantly accurate short-term wind speed and wind power prediction. The original non-linear and non-stationary wind speed and wind power time series data are decomposed using EMD to isolate the mutual effects between different components. The proposed EMD-KRR model is tested for predicting wind speed and wind power time series data over a time horizon spanning intervals of 10 min, 30 min, 1 h and 3 h ahead, respectively. Further the performance of EMD-KRR prediction model is compared with two other widely used non-iterative prediction models like EMD based Random vector functional link network (RVFL), and EMD based Extreme Learning Machine (ELM). Also an iterative Mutated Firefly Algorithm with Global Optima concept (MFAGO) optimized RVFL network is used for comparison to prove the advantages of non-iterative models over the iterative ones. The performance metrics of the proposed EMD-KRR and EMD-RVFL confirm the effectiveness and precision in producing an accurate forecast of both wind speed and wind power in comparison to all other prediction models using the wind power data from three real world wind farms. Further a fast reduced version of the EMD-KRR is presented in the paper to reduce the computational overhead substantially using randomly selected support vectors from the data set while resulting in a reasonably accurate forecast.

Multi-objective evolutionary algorithms (MOEAs) have shown good performance on many benchmark and real world multi-objective optimization problems. However, MOEAs may suffer from some difficulties when solving big data optimization problems with thousands of variables. Firefly algorithm (FA) is a new meta-heuristic, which has been proved to be a good optimization tool. In this paper, we present a hybrid multi-objective FA (HMOFA) for big data optimization. A set of big data optimization problems, including six single objective problems and six multi-objective problems, are tested in the experiments. Computational results show that HMOFA achieves promising performance on all test problems.

In this paper, a new multi-objective dimension-based firefly algorithm (MODFA) is proposed for solving the constrained multi-objective optimal power flow (MOOPF) problem with multiple and contradictory objectives in power systems. In our suggested MODFA algorithm, a constrained Pareto-dominant approach (CPA) is offered for guaranteeing zero violations of various inequality constraints on state variables in the constrained MOOPF problem. In addition to that, the CPA and the dimension-based technology (DT) are federated together to update the information of the non-dominant firefly to speed up the convergence of multiple target search. Crowding distance and non-dominated sorting based on the violation of constraints are also regarded as measures to sustain well-distributed Pareto optimal solution (POS) set. Furthermore, a fuzzy affiliation is utilized to pick the best compromise solution (BCS) from the obtained POS. The IEEE30-bus system, the IEEE57-bus system, and the IEEE118-bus system with nine cases are implemented to validate the performance of the proposed MODFA by considering the active power losses, the emission, and the total fuel cost. The numerous simulation results optimized by the MODFA, which are compared with frequently-used NSGA-III, NSGA-II, and MOPSO algorithm, show the capability of the MODFA for obtaining POS with uniform distribution and high quality. Additionally, three performance metrics are considered to evaluate approximation, distribution, and diversity of POS found by MODFA.

Optimization in computationally expensive numerical problems with limited function evaluations provides computational advantages over constraints based on runtime requirements and hardware resources. Convergence success of a metaheuristic optimization algorithm depends on directing and balancing of its exploration and exploitation abilities. Firefly and particle swarm optimization are successful swarm intelligence algorithms inspired by nature. In this paper, a hybrid algorithm combining firefly and particle swarm optimization (HFPSO) is proposed. The proposed algorithm is able to exploit the strongpoints of both particle swarm and firefly algorithm mechanisms. HFPSO try to determine the start of the local search process properly by checking the previous global best fitness values. In experiments, several dimensional CEC 2015 and CEC 2017 computationally expensive sets of numerical and engineering, mechanical design benchmark problems are used. The proposed HFPSO is compared with standard particle swarm, firefly and other recent hybrid and successful algorithms in limited function evaluations. Runtimes and convergence accuracies are statistically measured and evaluated. The solution results quality of this study show that the proposed HFPSO algorithm provides fast and reliable optimization solutions and outperforms others in unimodal, simple multimodal, hybrid, and composition categories of computationally expensive numerical functions.

This article proposes a novel metaheuristic algorithm called Volleyball Premier League (VPL) inspired by the competition and interaction among volleyball teams during a season. It also mimics the coaching process during a volleyball match. To solve global optimization problems using the volleyball metaphor, there are terms such as substitution, coaching, and learning, which are captured in the VPL algorithm. The proposed algorithm is benchmarked on 23 well-known test functions, which are categorized into three groups, namely unimodal, multimodal and fixed-dimension multimodal functions. The solutions obtained using the VPL have been compared with other metaheuristic algorithms including Particle Swarm Optimization (PSO), Differential Evolution (DE), Genetic Algorithm (GA), Artificial Bee Colony (ABC), Firefly Algorithm (FA), Harmony Search (HS), Sin Cosine Algorithm (SCA), Soccer League Competition (SLC), and League Championship Algorithm (LCA). In addition, VPL has been used to solve three classical engineering design optimization problems. Results show that VPL algorithm possesses a strong capability to produce superior performance over the other well-known metaheuristic algorithms. The results of the experiments also show that the VPL is effectively applicable to solve problems with complex search space.

The Firefly algorithm is a population-based optimization algorithm. It has become popular in the field of optimization and has been applied to engineering practices. Recent works have failed to address how to find the global minimum because their algorithm was trapped in the local minimum. Also, they were not able to provide a balance between exploration and exploitation. In this paper, the Tidal Force formula has been applied to modify the Firefly algorithm, which describes the effect of a massive body that gravitationally affects another massive body. The proposed algorithm brings a new strategy into the optimization field. It is applied by using exploitation (Tidal Force) and keeping a balance between the exploration and exploitation on function suitability. Plate shaped, Steep Ridges, Unimodal and Multimodal benchmark functions were used to compare experimental results. The study findings indicate that the Tidal Force Firefly algorithm outperforms the other existing modified Firefly algorithms.

The capacitated vehicle routing problem (CVRP) is a classical combinatorial optimization problem, which has received much attention due to its main challenges as distribution, logistics, and transportation. This proposed attempts to find the vehicle routes with minimizing traveling distance, in which the excellent solution delivers a set of customers in one visit by capacitated vehicle. For solving the CVRP problem, a cooperative hybrid firefly algorithm (CVRP-CHFA) is proposed in this paper with multiple firefly algorithm (FA) populations. Each FA is hybridized with two types of local search (i.e., Improved 2-opt as a local search and 2-h-opt as a mutation operator) and genetic operators. The proposed algorithms (FAs) communicate from time to time for exchanging some solutions (fireflies). The main aim of the hybridization and communication strategies is to maintain the diversity of populations to prevent the proposed algorithm from falling into local optima and overcome the drawbacks of a single swarm FA. The experiments are conducted on 108 instances from eight standard benchmarks. The results revealed that the proposed CVRP-CHFA got promising results compared to other well-known methods. Moreover, the proposed CVRP-CHFA significantly outperformed the recent three hybrid firefly algorithms.

Intelligent optimization algorithms based on swarm principles have been widely researched in recent times. The Firefly Algorithm (FA) is an intelligent swarm algorithm for global optimization problems. In literature, FA has been seen as an efficient and robust optimization algorithm. FA is an algorithm that has obtained good to best results in complex problems. Therefore, there are many instances of modification, and hybridization of FA with other optimizing algorithms, but further improvements are still possible. This research first proposes a new modification of FA by introducing a novel and unique stepping ahead parameter. The concept is based on being proactive rather than reactive, which is the normal behavior of a standard FA. The notion behind stepping ahead is to send a firefly ahead then the best known position to look for even better solution. Second, a new design of a hybrid of the newly modified FA with Covariance Matrix Adaptation Evolution Strategy (CMAES) to improve the exploitation further while maintaining good exploration in the fireflies is presented. The main use of CMAES in this hybrid algorithm is to provide diversity to fireflies, as a result it improves exploitation. Traditionally, hybridization has combined two or more algorithms in terms of structure only, and consideration was not given to the increase in time complexity or diversity. In this paper, the two algorithms are not run in separate cycles rather CMAES is placed inside the FA. Through this novelty, CMAES is initiated inside FA loop and an extra loop is avoided and at the same time CMAES diversifies FA solutions. The structure of algorithm together with the strength of individual solution are used. The newly established modified FA and hybrid are used to solve selected sixty five global optimization benchmark problems together with eight real-world problems from CEC 2011. The proposed algorithms have outperformed FA algorithm in both benchmark and real-world problems. The optimal solutions found by FA in benchmark problems was 69.2% while FA-Step algorithm achieved 73.9% and FA-CMAES algorithm obtained 92.3%. In real-world problem, FA obtained 37.5%, FA-Step was 50% while FA-CMAES was 75%. The results invariably show that the proposed algorithms perform significantly better than the standalone methods as well as the algorithms from the literature.

Graphical abstractDisplay Omitted HighlightsUncertainty is considered in RFID network planning.RFID network planning is modelled as a multi-objective optimization.A novel multi-objective firefly algorithm is proposed to solve multi-objective RFID network planning.Numerical experiments show the effectiveness of the proposed firefly algorithm for multi-objective RFID network planning. Radio frequency identification (RFID) is widely used for item identification and tracking. Due to the limited communication range between readers and tags, how to configure a RFID system in a large area is important but challenging. To configure a RFID system, most existing results are based on cost minimization through using 0/1 identification model. In practice, the system is interfered by environment and probabilistic model would be more reliable. To make sure the quality of the system, more objectives, such as interference and coverage, should be considered in addition to cost. In this paper, we propose a probabilistic-based multi-objective optimization model to address these challenges. The objectives to be optimized include number of readers, interference level and coverage of tags. A decomposition-based firefly algorithm is designed to solve this multi-objective optimization problem. Virtual force is integrated into random walk to guide readers moving in order to enhance exploitation. Numerical simulations are introduced to demonstrate and validate our proposed method. Comparing with existing methods, such as Non-dominated Sorting Genetic Algorithm-II and Multi-objective Particle Swarm Optimization approaches, our proposed method can achieve better performance in terms of quality metric and generational distance under the same computational environment. However, the spacing metric of the proposed method is slightly inferior to those compared methods.

This paper proposes the Firefly algorithm and Cuckoo Search (CS) algorithm based optimal location and the capacity of UPFC to improve the dynamic stability of the power system.Here, the FA technique optimizes the maximum power loss line as the suitable location of the UPFC. The affected location parameters and dynamic stability constraints are restored into secure limits using the optimum capacity of the UPFC, which in turn, has been optimized with reduced cost by using the CS algorithm. The attained capacity of the UPFC has been located in the affected location and the power flow of the system is analyzed.The proposed method is implemented in the MATLAB/Simulink platform and tested under IEEE 30 and IEEE 14 standard bench mark system. The advantage of the proposed method is capability and robustness to solve the complex optimization problem.In the results, system bus voltage, power loss, real and reactive power flow were analyzed. Then the proposed methods effectiveness was tested by the comparison analysis with the ABC-GSA, GSA-BAT & BAT-FF algorithms. The comparison results proved that the proposed method is the most effective technique to maintain the dynamic stability of the power system, which is competent over the other techniques. In this document, the Firefly Algorithm (FA) and Cuckoo Search (CS) algorithm based on optimal location and the capacity of UPFC to improve the dynamic stability of the power system are proposed. The novelty of the proposed method is exemplified in the improved searching ability, random reduction and reduced complexity. In this regard, the generator fault affects the system dynamic stability constraints such as voltage, power loss, real and reactive power. Here, the FA technique optimizes the maximum power loss line as the suitable location of the UPFC. The affected location parameters and dynamic stability constraints are restored into secure limits using the optimum capacity of the UPFC, which in turn, has been optimized with reduced cost by using the CS algorithm. The attained capacity of the UPFC has been located in the affected location and the power flow of the system analyzed. The proposed method is implemented in the MATLAB/Simulink platform and tested under IEEE 30 and IEEE 14 standard bench mark system. The proposed method performance is evaluated by comparison with those of different techniques such as ABC-GSA, GSA-Bat, Bat-FA and CS algorithms. The comparison results invariably prove the effectiveness of the proposed method and confirm its potential to solve the related problems.

This is a novel effort towards effective characterization of cervix lesions from CECT images.Two different approaches have been adopted for designing multi-objective binary GWO algorithms.For the utilized cases, Non-dominated Sorting based GWO dominates the other meta-heuristics based methods compared with.Cervix lesions are up to 91.1% accurately classified as benign and malignant with only five features selected by NSGWO.Efficiency of NSGWO is further verified on high-dimensional microarray gene expression datasets available online. Cervical cancer is one of the vital and most frequent cancers, but can be cured effectively if diagnosed in the early stage. This is a novel effort towards effective characterization of cervix lesions from contrast enhanced CT-Scan images to provide a reliable and objective discrimination between benign and malignant lesions. Performance of such classification models mostly depends on features used to represent samples in a training dataset. Selection of optimal feature subset here is NP-hard; where, randomized algorithms do better. In this paper, Grey Wolf Optimizer (GWO), which is a population based meta-heuristic inspired by the leadership hierarchy and hunting mechanism of grey wolves has been utilized for feature selection. The traditional GWO is applicable for continuous single objective optimization problems. Since, feature selection is inherently multi-objective; this paper proposes two different approaches for multi-objective binary GWO algorithms. One is a scalarized approach to multi-objective GWO (MOGWO) and the other is a Non-dominated Sorting based GWO (NSGWO). These are used for wrapper based feature selection that selects optimal textural feature subset for improved classification of cervix lesions. For experiments, contrast enhanced CT-Scan (CECT) images of 62 patients have been used, where all lesions had been recommended for surgical biopsy by specialist. Gray-level co-occurrence matrix based texture features are extracted from two-level decomposition of wavelet coefficients of cervix regions extracted from CECT images. The results of proposed approaches are compared with mostly used meta-heuristics such as genetic algorithm (GA) and firefly algorithm (FA) for multi-objective optimization. With better diversification and intensification, GWO obtains Pareto solutions, which dominate the solutions obtained by GA and FA when assessed on the utilized cervix lesion cases. Cervix lesions are up to 91% accurately classified as benign and malignant with only five features selected by NSGWO. A two-tailed t-test was conducted by hypothesizing the mean F-score obtained by the proposed NSGWO method at significance level=0.05. This confirms that NSGWO performs significantly better than other methods for the real cervix lesion dataset in hand. Further experiments were conducted on high dimensional microarray gene expression datasets collected online. The results demonstrate that the proposed method performs significantly better than other methods selecting relevant genes for high-dimensional, multi-category cancer diagnosis with an average of 12.82% improvement in F-score value.

Hand gesture is considered as one of the natural ways to interact with computers. The utility of hand gesture-based application is a recent trend and is an effective method for human–computer interaction. Though many static and other intelligent approaches using Machine learning (ML) are developed, still there is a marginal tradeoff between the computational cost and accuracy. In this paper, a Lightboost based Gradient boosting machine (LightGBM) is proposed for efficient hand gesture recognition. The hyper-parameters of the LightGBM are optimized with an improved memetic firefly algorithm. We have introduced a perturbation operator and incorporated it in the proposed memetic firefly algorithm for avoiding the local optimal solution in the traditional firefly algorithm. With comparative analysis among the proposed method and other competitive ML methods, the performance of the proposed method is found to be better in terms of various performance metrics such as accuracy, precision, recall, F1 score, and ROC–AUC. The proposed memetic firefly-based boosting approach is dominant over all the other considered methods with an accuracy of 99.36% and is robust for accurate hand gesture recognition.

Firefly algorithm (FA) is an effective meta-heuristic method, which has drawn extensive attentions and inspired many variants. However, it is difficult to balance the relation of the exploration and exploitation of FA. To this end, FA with self-adaptive strategy (SAFA) is proposed to improve the performance of the algorithm for constrained engineering design problems. Firstly, the characteristics of the standard FA are analyzed, and the essence of FA easily trapped into the local optima is revealed by theory analysis and case study. Secondly, a self-adaptive strategy for attraction model is proposed to improve the exploitation ability, and another self-adaptive strategy for stochastic model is developed to ensure a better balance between the exploration and exploitation. Thirdly, a self-adaptive penalty function is presented to treat constraint conditions. Then, the initial parameter setting is investigated, and the proper initial parameter value corresponding to the optimal performance of SAFA is obtained. And then, SAFA and other FA variants are used to optimize a set of classical and Congress on Evolutionary Computation (CEC) 2015 benchmark functions. Meanwhile, the contributions of self-adaptive strategies for attraction model and stochastic model are investigated with experimental analysis, respectively Finally, SAFA and other meta-heuristic methods are employed to solve constrained engineering design problems. The effects of SAFA with standard and self-adaptive penalty function are compared. Numerical experimental results show that self-adaptive strategies for attraction model and stochastic model improve the performance of FA, and SAFA obtains the best solutions on most of the benchmark functions. In addition, SAFA has the advantage in solving problems with different kind of dimensions, and maintains reasonable population diversity throughout the iteration compared with the other FA variants. Meanwhile, SAFA can reduce the computational complexity and ensure the solution accuracy in solving constrained optimization problems.

The production and use of energy from hydropower generation play a vital role in the economy. Besides, the presence of uncertainty further increases the complexity in optimizing the reservoir operation. A synthetic streamflow generation based on historical inflow records was employed using the Thomas–Fiering model for handling the uncertainty and variability of reservoir inflows. However, under the circumstances of water deficiency, the hydropower output is significantly reduced. In this study, an investigation of a parameter free Jaya algorithm as an optimization method for reservoir operation was carried out. When deriving the optimal operational rule a hedging strategy is introduced to attenuate the impact of reduced water supply. This strategy can effectively counterbalance the lack of water supply with reservoir storage requirements. The higher amount of hydropower generated by the proposed algorithm than the other algorithms used in this study, such as genetic algorithm (GA), the ant colony algorithm (ACO), the bat algorithm (BA), the particle swarm optimization (PSO) algorithm, chicken swarm optimization (CSO) algorithm, grasshopper optimization algorithm (GOA), equilibrium optimizer (EO) and firefly algorithm (FA), has shown its efficiency in the reservoir system. Several reservoir performance indices, such as total hydropower generation, reliability, and resilience, were used to access the proposed algorithm and other algorithms efficiency

Grey Wolf Optimizer (GWO) is a recently developed population-based metaheuristic algorithm which imitates the behaviour of grey wolves for survival. Initially, GWO was proposed to solve continuous optimization problems where it performed well. In recent years, numerous versions of GWO are available in the literature and GWO has been widely used to solve engineering problems. This paper presents a novel discrete GWO algorithm (D-GWO) to solve complex discrete travelling salesman problem (TSP). The 2-opt algorithm is incorporated into it to improve the performance of the proposed algorithm. To inspect the performance of D-GWO, the results of the proposed algorithm are compared with well-known metaheuristic algorithms such as Bat Algorithm(BA), Discrete Firefly Algorithm, Imperialist Competitive Algorithm and some other classical algorithms over several known TSP instances. In order to obtain unbiased and rigorous comparison, descriptive statistics such as mean and standard deviation are used as well as statistical tests such as Friedman test and Holm’s test are also conducted. The D-GWO is implemented in MATLAB environment. The computational result carried out in this study has shown that D-GWO outperforms significantly over other alternative algorithms.

As a unified model for describing biomedical knowledge, a biomedical ontology is of help to solve the issues of data heterogeneity in different biomedical databases. However, these ontologies might model same biomedical knowledge differently, yielding the heterogeneity problem. To address the biomedical ontology heterogeneity problem, it is necessary to match the heterogeneous concept pairs between two ontologies. How to reduce the computational complexity is a challenging problem when matching large-scale biomedical ontologies, which directly affects the matching efficiency and the alignment’s quality. To face this challenge, this work proposes a large-scale biomedical ontology partitioning and matching framework. In our proposal, a central concepts based ontology partitioning algorithm is first used to divide the ontology into several disjoint segments, which borrows the idea from the social network and Firefly Algorithm (FA). The proposed algorithm is able to partition the ontologies with low computation complexity, and at the same time, ensure the semantic completeness and the decent scale of each segment. Then, an Adaptive Compact Evolutionary Algorithm (ACEA) based matching technique is utilized to determine the ontology segment alignments, which can efficiently match the similar ontology segments. The experiment utilizes the biomedical testing cases provided by Ontology Alignment Evaluation Initiative (OAEI) to test our approach’s effectiveness, and the experimental results show that the alignments obtained by our method significantly outperforms the state-of-the-art biomedical ontology matching techniques.

This paper represents a hybrid Firefly and Self-Regulating Particle Swarm Optimization (FSRPSO) algorithm to solve optimal Combined Heat and Power Economic Dispatch (CHPED) problem. Valve point effect on fuel cost function of pure generation units, electrical power losses in transmission systems and feasible operating zones are taken into account in the CHPED problem. The CHPED refers to minimize total costs of fuel for electricity and heat generation supply to load demand. The proposed FSRPSO attempts to determine the start of the local search process properly by checking the previous global best. Thus, the FSRPSO is able to exploit strong points of both Firefly Algorithm (FA) and SRPSO mechanisms in order to balance between exploration and exploitation phases. Besides, for the sake of validation the proposed hybrid method, the FSRPSO is examined on 21 well-known benchmarks, and also a real engineering case i.e., two power systems for evaluating its performance compared with the SRPSO, FA, PSO, and other state-of-the-art algorithms. The obtained optimization results show that the proposed FSRPSO provides fast, mature and reliable optimum solutions and outperform other compared algorithms in diverse categories of benchmarks along with the studied CHPED problem.

The success of portfolio construction depends primarily on the future performance of stock markets. Recent developments in machine learning have brought significant opportunities to incorporate prediction theory into portfolio selection. However, many studies show that a single prediction model is insufficient to achieve very accurate predictions and affluent returns. In this paper, a novel portfolio construction approach is developed using a hybrid model based on machine learning for stock prediction and mean–variance (MV) model for portfolio selection. Specifically, two stages are involved in this model: stock prediction and portfolio selection. In the first stage, a hybrid model combining eXtreme Gradient Boosting (XGBoost) with an improved firefly algorithm (IFA) is proposed to predict stock prices for the next period. The IFA is developed to optimize the hyperparameters of the XGBoost. In the second stage, stocks with higher potential returns are selected, and the MV model is employed for portfolio selection. Using the Shanghai Stock Exchange as the study sample, the obtained results demonstrate that the proposed method is superior to traditional ways (without stock prediction) and benchmarks in terms of returns and risks.

Surface roughness is a major concern to the present manufacturing sector without the wastage of material. Hence, in order to achieve good surface roughness and reduce production time, optimization is necessary. In this study optimization techniques based on swarm intelligence (SI) namely firefly algorithm (FA), particle swarm optimization (PSO) and a newly introduced metaheuristic algorithm namely bat algorithm (BA) has been implemented for optimizing machining parameters namely cutting speed, feed rate, depth of cut and tool flank wear and cutting tool vibrations in order to achieve minimum surface roughness. Two parameters Ra and Rt have been considered for evaluating the surface roughness. The performance of BA algorithm has been compared with FA algorithm and PSO, which is a commonly and widely used optimization algorithm in machining. The results conclude that BA produces better optimization, when compared to FA and PSO. Based on the literature review carried out, this work is a first attempt at using a metaheuristic algorithm namely BA in machining applications.

Graphical abstractDisplay Omitted HighlightsThis paper applies meta-heuristic algorithms to software testing problem.Different meta-heuristics were employed to analyze their performances on test data generation.A control parameter sensitivity analysis was performed on the algorithms.Various fitness functions based on different coverage approaches were compared. Cost of testing activities is a major portion of the total cost of a software. In testing, generating test data is very important because the efficiency of testing is highly dependent on the data used in this phase. In search-based software testing, soft computing algorithms explore test data in order to maximize a coverage metric which can be considered as an optimization problem. In this paper, we employed some meta-heuristics (Artificial Bee Colony, Particle Swarm Optimization, Differential Evolution and Firefly Algorithms) and Random Search algorithm to solve this optimization problem. First, the dependency of the algorithms on the values of the control parameters was analyzed and suitable values for the control parameters were recommended. Algorithms were compared based on various fitness functions (path-based, dissimilarity-based and approximation level+branch distance) because the fitness function affects the behaviour of the algorithms in the search space. Results showed that meta-heuristics can be effectively used for hard problems and when the search space is large. Besides, approximation level+branch distance based fitness function is generally a good fitness function that guides the algorithms accurately.

Display OmittedThis reconfigured network obtained by the proposed Fuzzy-Firefly algorithm gives the following compared to other techniques:(i)Lower Real Power Loss(ii)Lower Reactive Power Loss(iii)Improvement of voltage profiles(iv)Lower energy costs A new load flow method has been suggested for unbalanced distribution networks.Improvement in terms of loss reductions, minimum voltages, energy cost, CPU time, iteration number.The outcomes obtained by PM is better than that of other existing approaches. This paper addresses a method to optimize the unbalanced distribution networks (UDNs) for keeping up the voltage profile with respect to the consequence of solving the multi-objective reconfiguration using the Firefly algorithm in a fuzzy domain with a load flow method proposed in this paper. The objectives to be minimized are the total network power losses, the deviation of bus voltage and load equalizing in the feeders with network reconfiguration. Every goal is moved into the fuzzy domain utilizing its membership function and fuzzified independently. The proposed method for network reconfiguration has been implemented in 25-node and 19-node UDNs. The outcomes obtained by the suggested method of these two unbalanced networks have been compared with that of obtained by Genetic algorithm (GA), ABC algorithm, PSO algorithm and GA-PSO algorithm using the same objective function. The juxtaposition of the proposed method with the available methods is also presented.