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In this manuscript, a new quantum computing-based optimization algorithm is proposed to solve multiple-objective mixed cost-effective emission dispatch (MEED) problem of electrical power system. The MEED problem aims at maintaining proper balance between emission of pollutants and generation of power. The problem has been formulated here using cubic equation to reduce the nonlinearities of the system. It is transformed to single-objective problem by considering max to max penalty factor. The proposed optimization technique is inspired by the concept of quantum mechanics, gravitational force and firefly algorithm (FA) and is termed as quantum-inspired tidal FA (QITFA). The proposed QITFA is tested on IEEE 14-bus and IEEE 30-bus test system for four different load conditions. The obtained results are compared with the results yielded by some other state-of-the-art methods like Lagrangian relaxation method, particle swarm optimization (PSO), simulated annealing, quantum-behaved bat algorithm and quantum PSO. This paper proves the superiority of the proposed QITFA over all these methods. Further, the obtained results also suggest its effective and efficient implementation in MEED problem.

The Firefly Algorithm (FA) is a highly efficient population-based optimization technique developed by mimicking the flashing behavior of fireflies when mating. This article proposes a method based on Differential Evolution (DE)/current-to-best/1 for enhancing the FA's movement process. The proposed modification increases the global search ability and the convergence rates while maintaining a balance between exploration and exploitation by deploying the global best solution. However, employing the best solution can lead to premature algorithm convergence, but this study handles this issue using a loop adjacent to the algorithm's main loop. Additionally, the suggested algorithm’s sensitivity to the alpha parameter is reduced compared to the original FA. The GbFA surpasses both the original and five-version of enhanced FAs in finding the optimal solution to 30 CEC2014 real parameter benchmark problems with all selected alpha values. Additionally, the CEC 2017 benchmark functions and the eight engineering optimization challenges are also utilized to evaluate GbFA’s efficacy and robustness on real-world problems against several enhanced algorithms. In all cases, GbFA provides the optimal result compared to other methods. Note that the source code of the GbFA algorithm is publicly available at https://www.optim-app.com/projects/gbfa .

Thermal conductivity is a specific thermal property of soil which controls the exchange of thermal energy. If predicted accurately, the thermal conductivity of soil has a significant effect on geothermal applications. Since the thermal conductivity is influenced by multiple variables including soil type and mineralogy, dry density, and water content, its precise prediction becomes a challenging problem. In this study, novel computational approaches including hybridisation of two metaheuristic optimisation algorithms, i.e. firefly algorithm (FF) and improved firefly algorithm (IFF), with conventional machine learning techniques including extreme learning machine (ELM), adaptive neuro-fuzzy interface system (ANFIS) and artificial neural network (ANN), are proposed to predict the thermal conductivity of unsaturated soils. FF and IFF are used to optimise the internal parameters of the ELM, ANFIS and ANN. These six hybrid models are applied to the dataset of 257 soil cases considering six influential variables for predicting the thermal conductivity of unsaturated soils. Several performance parameters are used to verify the predictive performance and generalisation capability of the developed hybrid models. The obtained results from the computational process confirmed that ELM-IFF attained the best predictive performance with a coefficient of determination = 0.9615, variance account for = 96.06%, root mean square error = 0.0428, and mean absolute error = 0.0316 on the testing dataset (validation phase). The results of the models are also visualised and analysed through different approaches using Taylor diagrams, regression error characteristic curves and area under curve scores, rank analysis and a novel method called accuracy matrix. It was found that all the proposed hybrid models have a great ability to be considered as alternatives for empirical relevant models. The developed ELM-IFF model can be employed in the initial stages of any engineering projects for fast determination of thermal conductivity.

The research proposed in this article presents a novel improved version of the widely adopted firefly algorithm and its application for tuning and optimising XGBoost classifier hyper-parameters for network intrusion detection. One of the greatest issues in the domain of network intrusion detection systems are relatively high false positives and false negatives rates. In the proposed study, by using XGBoost classifier optimised with improved firefly algorithm, this challenge is addressed. Based on the established practice from the modern literature, the proposed improved firefly algorithm was first validated on 28 well-known CEC2013 benchmark instances a comparative analysis with the original firefly algorithm and other state-of-the-art metaheuristics was conducted. Afterwards, the devised method was adopted and tested for XGBoost hyper-parameters optimisation and the tuned classifier was tested on the widely used benchmarking NSL-KDD dataset and more recent USNW-NB15 dataset for network intrusion detection. Obtained experimental results prove that the proposed metaheuristics has significant potential in tackling machine learning hyper-parameters optimisation challenge and that it can be used for improving classification accuracy and average precision of network intrusion detection systems.

The Internet of Things (IoT) is defined as interconnected digital and mechanical devices with intelligent and interactive data transmission features over a defined network. The ability of the IoT to collect, analyze and mine data into information and knowledge motivates the integration of IoT with grid and cloud computing. New job scheduling techniques are crucial for the effective integration and management of IoT with grid computing as they provide optimal computational solutions. The computational grid is a modern technology that enables distributed computing to take advantage of a organization’s resources in order to handle complex computational problems. However, the scheduling process is considered an NP-hard problem due to the heterogeneity of resources and management systems in the IoT grid. This paper proposed a Greedy Firefly Algorithm (GFA) for jobs scheduling in the grid environment. In the proposed greedy firefly algorithm, a greedy method is utilized as a local search mechanism to enhance the rate of convergence and efficiency of schedules produced by the standard firefly algorithm. Several experiments were conducted using the GridSim toolkit to evaluate the proposed greedy firefly algorithm’s performance. The study measured several sizes of real grid computing workload traces, starting with lightweight traces with only 500 jobs, then typical with 3000 to 7000 jobs, and finally heavy load containing 8000 to 10,000 jobs. The experiment results revealed that the greedy firefly algorithm could insignificantly reduce the makespan makespan and execution times of the IoT grid scheduling process as compared to other evaluated scheduling methods. Furthermore, the proposed greedy firefly algorithm converges on large search spacefaster , making it suitable for large-scale IoT grid environments.

Data clustering is a process of arranging similar data in different groups based on certain characteristics and properties, and each group is considered as a cluster. In the last decades, several nature-inspired optimization algorithms proved to be efficient for several computing problems. Firefly algorithm is one of the nature-inspired metaheuristic optimization algorithms regarded as an optimization tool for many optimization issues in many different areas such as clustering. To overcome the issues of velocity, the firefly algorithm can be integrated with the popular particle swarm optimization algorithm. In this paper, two modified firefly algorithms, namely the crazy firefly algorithm and variable step size firefly algorithm, are hybridized individually with a standard particle swarm optimization algorithm and applied in the domain of clustering. The results obtained by the two planned hybrid algorithms have been compared with the existing hybridized firefly particle swarm optimization algorithm utilizing ten UCI Machine Learning Repository datasets and eight Shape sets for performance evaluation. In addition to this, two clustering validity measures, Compact-separated and David–Bouldin, have been used for analyzing the efficiency of these algorithms. The experimental results show that the two proposed hybrid algorithms outperform the existing hybrid firefly particle swarm optimization algorithm.

Firefly algorithm is a nature-inspired metaheuristic algorithm inspired by the flashing behavior of fireflies. It is originally proposed for continuous problems. However, due to its effectiveness and success in solving continuous problems, different studies are conducted in modifying the algorithm to suit discrete problems. Many engineering as well as optimization problems from other disciplines involve discrete variables. Recent reviews on the application and modifications of firefly algorithm mainly focus on continuous problems. This paper is devoted to the detailed review of the modifications done on firefly algorithm in order to solve optimization problems with discrete variables. Hence, advances on the application of firefly algorithm for optimization problems with binary, integer as well as mixed variables will be discussed. Possible future works will also be highlighted.

A lot of discussion is done these days regarding the actual novelty of newcomer nature-inspired approaches. Crucial role on that matter is played by the mechanisms included in these approaches, where many of these mechanisms have been previously introduced as part of another algorithm. On the other hand, a good practice would be to use the mechanisms of a nature-inspired algorithm to enhance the performance or to overcome the drawbacks of another one. This paper investigates this issue, where four mechanisms have been isolated and studied. Furthermore, the well-known Particle Swarm Optimization and Firefly Algorithm were used to test the effect of the studied mechanisms on the exploration and exploitation of established approaches that suffer from premature convergence or mostly explore the search space, respectively.

Microscopic Image segmentation has a crucial role in detecting and diagnosing numerous critical diseases like Alzheimer’s disease, Kidney disease, Cancer, many infectious diseases, etc. Precise segmentation of hippocampus microscopic images is a prerequisite for analyzing and interpreting the brain tissues. A few metaheuristic-based multilevel image segmentation methods are found in the literature for the same. In this work, an enhanced firefly algorithm-based image segmentation method has been proposed to achieve a good quality segmentation. The proposed algorithm utilizes the classical firefly algorithm’s movement operation along with the concept of quantum superposition and quantum update operation. In this algorithm, the movements of quantum fireflies have been modeled based on two strategies: firstly, the less bright fireflies move towards the comparatively brighter ones and secondly, quantum fireflies are updated according to the global optimum by the quantum update operation. This global steered Quantum Inspired Firefly Algorithm (QIFA) has been proposed and used for the multilevel hippocampus image segmentation considering correlation and structural similarity index as objective functions. In order to validate the quality of segmentation, the F-score values with respect to the segmented images have been reported. The proposed algorithm’s performance has been compared with seven other metaheuristic algorithms. The experimental results establish that the proposed algorithm is effective in producing good quality segmentation of hippocampus images.

A rapid response optimization technique for photovoltaic maximum power point tracking (MPPT) under partial shading conditions (PSCs) is proposed in this study. To improve the solar MPPT tracking speed for rapidly-changing environmental conditions and to prevent the conventional firefly algorithm (FA) from becoming trapped at the local peaks and oscillations during the search process, a novel fusion algorithm, named the modified firefly algorithm (MFA), is proposed. The MFA integrates and modifies the processes of two algorithms, namely the firefly algorithm with neighborhood attraction (NaFA) and simplified firefly algorithm (SFA). A modified attraction process for the NaFA is used in the first iteration to avoid trapping at local maximum power points (LMPPs). In addition, in order to improve the convergence speed, the attractiveness factor of the attraction process is designed to be related to the power and position difference of the fireflies. Furthermore, the number of fireflies is designed to decrease in proportion with the iterations in the modified SFA process. Results from both the simulations and evaluations verify that the proposed algorithm offers rapid response with high accuracy and efficiency when encountering PSCs. In addition, the MFA can avoid becoming trapped at LMPPs and ease the oscillations during the search process. Consequently, the proposed method could be considered to be one of the most promising substitutes for existing approaches. In addition, the proposed method is adaptable to different types of solar panels and different system formats with specifically designed parameters.