Moth Flame Optimization Research Articles

Implementation of Marin Predators Algorithm for optimizing the position of multiple Optical Network Units in Fiber Wireless Access Networks

• The problem of the optimal placement of multiple ONUs in FiWi networks is solved using- Marin predators algorithm (MPA) • A cost function is defined in terms of the average distance among ONUs and users. • The optimized locations are used in a developed simulation model to analyze the performance of FiWi network in terms of throughput and average propagation delay of FiWi access network. • The results obtained through the MPA are benchmarked with respect to the Harris Hawks Optimization (HHO), Whale Optimization algorithm (WOA), Moth-Flame optimization (MFO) and Greedy algorithms. Fiber-Wireless (FiWi) access network is a hybrid network that offers huge bandwidth and ubiquity to the users and is expected to play a dominant role in the emerging next generation communication networks. In this manuscript, a novel Marine Predators algorithm (MPA) is used to optimally place multiple Optical Network Units (ONUs) in FiWi access network. The optimized locations are used in a developed simulation model to assess the performance of FiWi network in terms of throughput and average propagation delay considering peer-to-peer communication. Also, the optimizer’s performance was evaluated in terms of fitness value, exploration, exploitation, computational complexity and nature of convergence curve. The MPA's efficiency is benchmarked with respect to a heuristic, Greedy algorithm, as well as existing efficient metaheuristics – Moth-Flame Optimization (MFO), Whale Optimization algorithm (WOA) and Harris-Hawks Optimization (HHO). It was found that MPA outperforms Greedy, MFO, WOA and HHO algorithms in each of the cases.

Three-Layer Multi-UAVs Path Planning Based on ROBL-MFO

This paper proposes a new three-layer path planning method, where we fused two existing path planning methods (global path and local path) into a single problem for multi- unmanned aerial vehicles (UAVs) path planning for UAV. The global-path network layer contains the latest information and algorithms for global planning according to specific applications. The trajectory planning layer represents the kinematics and different motion characteristics, the planning-execution layer implements the local planning algorithm for obstacle avoidance. In the last layer, we propose a new swarm intelligence algorithm called the refraction principle and opposite-based-learning moth flame optimization (ROBL-MFO). In contrast to the classical MFO, the proposed algorithm addresses the shortcoming of the classical MFO algorithm. First, it adapts the moth position update formula to the notion of historical optimal flame average and improves the convergence speed of the algorithm. Second, it utilizes a random inverse learning strategy to narrow down the search space. Finally, the principle of refraction gives the algorithm the ability to jump out of local optima and helps the algorithm avoid premature convergence. The experimental results show that the performance of the proposed algorithm is versatile, robust, and stable.

An Evolutionary Algorithmic Approach for Improving the Success Rate of Selective Assembly through a Novel EAUB Method

This work addresses an evolutionary algorithmic approach to reduce the surplus pieces in selective assembly to increase success rates. A novel equal area amidst unequal bin numbers (EAUB) method is proposed for classifying the parts of the ball bearing assembly by considering the various tolerance ranges of parts. The L16 orthogonal array is used for identifying the effectiveness of the proposed EAUB method through varying the number of bins of the parts of an assembly. Because of qualities such as minimal setting parameters, ease of understanding and implementation, and rapid convergence, the moth–flame optimization (MFO) algorithm is put forward in this work for identifying the optimal combination of bins of the parts of an assembly toward maximizing the percentage of the success rate of making assemblies. Computational results showed a 5.78% improvement in the success rate through the proposed approach compared with the past literature. The usage of the MFO algorithm is justified by comparing the computational results with the harmony search algorithm.

Inserting of heuristic techniques into the stability regions for multiarea load frequency control systems with time delays

The design and optimization of robust controller parameters are required to improve the controller performances and to keep the stability of load frequency control (LFC) system. In addition, reducing the number of iterations and computational time is very important for swiftly tuning of the controller parameters and the system to reach stability rapidly. For this purpose, this study presents the inserting of heuristic optimization techniques into stability regions method identified in proportional-integral (PI) controllers space for multiarea LFC systems with communication time delays (CTDs). This method consists of two steps: determination of stability region for the system and application of heuristics. Stability region for the system is found via stability boundary locus (SBL) and moth-flame optimization (MFO), particle swarm optimization (PSO), sine cosine algorithm (SCA), slime mould algorithm (SMA) and whale optimization algorithm (WOA) are inserted and applied to this region. In addition, a cost function having time domain specifications is developed for improving the performances of LFC and it is compared with the well-known integral error functions. Also, the robust stability region, which tolerates any system parameter and any time delay variation, is identified and the significance of this region is given for robustness analysis. It is observed from the analyses that better system outputs have been obtained with developed cost function. Steady state errors are minimized and transient state performances are improved with the proposed method. Moreover, desired system performances have been achieved with lower computational time and iteration number (approximately more than about 89% reduced according to classical approach) without deteriorating the stable structure of the system by the proposed method.

Secure Cluster based Routing Using Improved Moth Flame Optimization for Wireless Sensor Networks

Secure Cluster based Routing Using Improved Moth Flame Optimization for Wireless Sensor Networks

COVID-19 classification using chest X-ray images: A framework of CNN-LSTM and improved max value moth flame optimization.

Coronavirus disease 2019 (COVID-19) is a highly contagious disease that has claimed the lives of millions of people worldwide in the last 2 years. Because of the disease's rapid spread, it is critical to diagnose it at an early stage in order to reduce the rate of spread. The images of the lungs are used to diagnose this infection. In the last 2 years, many studies have been introduced to help with the diagnosis of COVID-19 from chest X-Ray images. Because all researchers are looking for a quick method to diagnose this virus, deep learning-based computer controlled techniques are more suitable as a second opinion for radiologists. In this article, we look at the issue of multisource fusion and redundant features. We proposed a CNN-LSTM and improved max value features optimization framework for COVID-19 classification to address these issues. The original images are acquired and the contrast is increased using a combination of filtering algorithms in the proposed architecture. The dataset is then augmented to increase its size, which is then used to train two deep learning networks called Modified EfficientNet B0 and CNN-LSTM. Both networks are built from scratch and extract information from the deep layers. Following the extraction of features, the serial based maximum value fusion technique is proposed to combine the best information of both deep models. However, a few redundant information is also noted; therefore, an improved max value based moth flame optimization algorithm is proposed. Through this algorithm, the best features are selected and finally classified through machine learning classifiers. The experimental process was conducted on three publically available datasets and achieved improved accuracy than the existing techniques. Moreover, the classifiers based comparison is also conducted and the cubic support vector machine gives better accuracy.

Moth Flame Optimization: Theory, Modifications, Hybridizations, and Applications.

The Moth flame optimization (MFO) algorithm belongs to the swarm intelligence family and is applied to solve complex real-world optimization problems in numerous domains. MFO and its variants are easy to understand and simple to operate. However, these algorithms have successfully solved optimization problems in different areas such as power and energy systems, engineering design, economic dispatch, image processing, and medical applications. A comprehensive review of MFO variants is presented in this context, including the classic version, binary types, modified versions, hybrid versions, multi-objective versions, and application part of the MFO algorithm in various sectors. Finally, the evaluation of the MFO algorithm is presented to measure its performance compared to other algorithms. The main focus of this literature is to present a survey and review the MFO and its applications. Also, the concluding remark section discusses some possible future research directions of the MFO algorithm and its variants.

Automated software effort estimation for agile development system by heuristically improved hybrid learning

SummaryIn the process of modern software development, an important role is played by software effort estimation. The failure or success of the projects is mostly based on the schedule outcomes and effort estimation accuracy. The effort estimation problem still remains as a challenging issue because of the limitations of various standard measures for forecasting the efforts in the plan‐driven software development. This article emphasizes on novel software effort estimation framework using heuristically improved hybrid learning model. This article proposes heuristically improved hybrid learning (HI‐HL) with deep belief network and artificial neural network for software effort estimation. The weight optimization strategy of DBN and ANN to attain highly accurate estimation. Here, the weight optimization is performed by integrating two standard optimization algorithms such as forest optimization algorithm and moth‐flame optimization, so called as solution index‐based forest moth flame optimization with the purpose of solving the fitness function concerning the “Magnitude of Relative Error and Mean Absolute Error (MAE).” From the table results, for dataset 1, the SMAPE of SI‐FMFO‐HI‐HL is correspondingly secured 60.75%, 34.26%, 56.77%, and 61.44% improved than ELM, LSTM, DNN, and fuzzy. The simulation findings indicated that the recommended estimation model outperformed the baseline approaches on the three publically available datasets.

On the Utilization of an Ensemble of Meta-Heuristics for Simulating Energy Consumption in Buildings

Predicting energy consumption has been a substantial topic because of its ability to lessen energy wastage and establish an acceptable overall operational efficiency. Thus, this research aims at creating a meta-heuristic-based method for autonomous simulation of heating and cooling loads of buildings. The developed method is envisioned on two tiers, whereas the first tier encompasses the use of a set of meta-heuristic algorithms to amplify the exploration and exploitation of Elman neural network through both parametric and structural learning. In this regard, 10 meta-heuristic were utilized, namely differential evolution, particle swarm optimization, invasive weed optimization, teaching-learning optimization, ant colony optimization, grey wolf optimization, grasshopper optimization, moth-flame optimization, antlion optimization, and arithmetic optimization. The second tier is designated for evaluating the meta-heuristic-based models through performance evaluation and statistical comparisons. An integrative ranking of the models is achieved using average ranking algorithm.

A novel plant leaf disease detection by adaptive fuzzy C-Means clustering with deep neural network

ABSTRACT The contribution of a plant is most significant for both human life and nature. The plant diseases affect whole plants, including leaves, stems, fruit, root, and flower. However, conventional approaches enclosed human involvement in classifying and identifying diseases. This process takes more time to complete a task. The main intention of this paper is to effectively develop a deep structured architecture for the detection of plant leaf diseases by introducing intelligent techniques, which have several processing steps. As a major contribution, Adaptive Fuzzy C-Means Clustering (FCM) is adopted for the abnormality segmentation. Moreover, the Improved Deep Neural Network (I-DNN) has achieved the greatest strength in enhancing the performance of plant leaf disease recognition. Here, Newly Updated Moth-Flame Optimization (NU-MFO) is utilised for enhancing the classification efficiency through a valuable objective function. The recommended method achieves higher accuracy rate in the recognition of diseases when compared to the baseline approaches. The precision of the NU-MFO-I-DNN at 85% learning rate is 0.01%, 0.26%, 0.07%, and 0.28% higher than MFO-I-DNN, GWO-I-DNN, SSO-I-DNN, and PSO-I-DNN, respectively.

Indirect Analysis of Concrete Slump Using Different Metaheuristic-Empowered Neural Processors

Estimating the mechanical parameters of concrete is significant towards achieving an efficient mixture design. This research deals with concrete slump analysis using novel integrated models. To this end, four wise metaheuristic techniques of biogeography-based optimization (BBO), salp swarm algorithm (SSA), moth-flame optimization (MFO), and wind driven optimization (WDO) are employed to optimize a popular member of the neural computing family, namely multilayer perceptron (MLP). Four predictive ensembles are constructed to analyze the relationship between concrete slump and seven concrete ingredients including cement, water, slag, fly ash, fine aggregate, superplasticizer, and coarse aggregate. After discovering the optimal complexities by sensitivity analysis, the results demonstrated that the combination of metaheuristic algorithms and neural methods can properly handle the early prediction of concrete slump. Moreover, referring to the calculated ranking scores (RSs), the BBO-MLP (RS = 21) came up as the most accurate model, followed by the MFO-MLP (RS = 17), SSA-MLP (RS = 12), and WDO-MLP (RS = 10). Lastly, the suggested models can be promising substitutes to traditional approaches in approximating the concrete slump.

Multi-strategy modified INFO algorithm: Performance analysis and application to functional electrical stimulation system

A functional electrical stimulation (FES) system holds a significant importance for the paralyzed individuals as it can help them to perform the tasks they are unable to do. It is crucial to develop an efficient control mechanism for the FES system as it acts on the human musculoskeletal system which presents noise and uncertainties. Therefore, this paper proposes a novel control method for efficient operation of the FES system. In this regard, a proportional-integral-derivative controller with filter (PID-F) mechanism is proposed for the first time in literature for efficient operation of the FES system. Besides, a novel multi-strategy based weighted mean of vectors algorithm (m-INFO) is also developed using a modified opposition-based learning and Lévy flight mechanism together with Nelder-Mead simplex search method. Unimodal, multimodal, low-dimensional and CEC2019 benchmark functions are used to demonstrate the excellent performance of the proposed m-INFO algorithm against several other metaheuristic optimizers. The proposed m-INFO algorithm is then used as an efficient tool to design the PID-F controlled FES system. To achieve optimal tuning, a simple yet effective objective function is also proposed. The excellent ability of the developed m-INFO algorithm-based PID-F controller for FES system is demonstrated through comparative statistical, transient response and frequency response analyses using original weighted mean of vectors, marine predators and moth-flame optimization algorithms based PID-F controlled FES systems.

An improved moth flame optimization algorithm based on modified dynamic opposite learning strategy

An improved moth flame optimization algorithm based on modified dynamic opposite learning strategy

Optimal Operation Strategy of PV-Charging-Hydrogenation Composite Energy Station Considering Demand Response

Traditional charging stations have a single function, which usually does not consider the construction of energy storage facilities, and it is difficult to promote the consumption of new energy. With the gradual increase in the number of new energy vehicles (NEVs), to give full play to the complementary advantages of source-load resources and provide safe, efficient, and economical energy supply services, this paper proposes the optimal operation strategy of a PV-charging-hydrogenation composite energy station (CES) that considers demand response (DR). Firstly, the operation mode of the CES is analyzed, and the CES model, including a photovoltaic power generation system, fuel cell, hydrogen production, hydrogen storage, hydrogenation, and charging, is established. The purpose is to provide energy supply services for electric vehicles (EVs) and hydrogen fuel cell vehicles (HFCVs) at the same time. Secondly, according to the travel law of EVs and HFCVs, the distribution of charging demand and hydrogenation demand at different periods of the day is simulated by the Monte Carlo method. On this basis, the following two demand response models are established: charging load demand response based on the price elasticity matrix and interruptible load demand response based on incentives. Finally, a multi-objective optimal operation model considering DR is proposed to minimize the comprehensive operating cost and load fluctuation of CES, and the maximum–minimum method and analytic hierarchy process (AHP) are used to transform this into a linearly weighted single-objective function, which is solved via an improved moth–flame optimization algorithm (IMFO). Through the simulation examples, operation results in four different scenarios are obtained. Compared with a situation not considering DR, the operation strategy proposed in this paper can reduce the comprehensive operation cost of CES by CNY 1051.5 and reduce the load fluctuation by 17.8%, which verifies the effectiveness of the proposed model. In addition, the impact of solar radiation and energy recharge demand changes on operations was also studied, and the resulting data show that CES operations were more sensitive to energy recharge demand changes.

A new intrusion detection system based on Moth–Flame Optimizer algorithm

This study relies on using a Moth–Flame Optimization (MFO) method as a search algorithm and a Decision Tree (DT) as an evaluation algorithm to generate an efficient feature subset for intrusion detection systems (IDS). The target is to find a feature subset using the minimum number of traffic network features that later obtains the maximum performance by the machine algorithms used in the classification task. This depends on enhancing the MFO by adopting new operators besides the embedded spiral operator to balance the exploration and exploitation alleviating the local minima problem. The main contribution of this work is the adoption of the cosine similarity measure to binarize the continuous MFO into a binary problem. Cosine similarity overcomes the limitations of the commonly used sigmoid function that depends on using a threshold value for conversion. However, cosine similarity computes the similarity ratio between the current solution and the optimal solution. The augmented MFO wrapper framework is applied as an IDS to detect anomalous traffic in the network. The proposed method is compared against several well-known state-of-the-art algorithms on three network datasets (KDDCUPP9, NSL-KDD, and UNSW-NB15), using IDSACC, IDSTPR, IDSFPR, IDSF-score, and convergence evaluation measures to assess the performance of the proposed method. The experimental results show the superiority of the proposed cosine similarity method compared to other algorithms with an accuracy of 97.8%, F-score of 99%, TPR of 99.6%, and FPR of 8.1% using only five selected features from the KDDCUPP99 dataset. It achieved the accuracy of 89.7%, TPR of 89.1%, FPR of 2.9%, when four selected features from the NSL-KDD dataset are used. And finally, it achieved an accuracy of 92.4%, TPR of 92.3%, FPR of 3%, and F-score 94.2% when the UNSW-NB15 dataset is used.

Quantum-Inspired Moth-Flame Optimizer With Enhanced Local Search Strategy for Cluster Analysis.

Clustering is an unsupervised learning technique widely used in the field of data mining and analysis. Clustering encompasses many specific methods, among which the K-means algorithm maintains the predominance of popularity with respect to its simplicity and efficiency. However, its efficiency is significantly influenced by the initial solution and it is susceptible to being stuck in a local optimum. To eliminate these deficiencies of K-means, this paper proposes a quantum-inspired moth-flame optimizer with an enhanced local search strategy (QLSMFO). Firstly, quantum double-chain encoding and quantum revolving gates are introduced in the initial phase of the algorithm, which can enrich the population diversity and efficiently improve the exploration ability. Second, an improved local search strategy on the basis of the Shuffled Frog Leaping Algorithm (SFLA) is implemented to boost the exploitation capability of the standard MFO. Finally, the poor solutions are updated using Levy flight to obtain a faster convergence rate. Ten well-known UCI benchmark test datasets dedicated to clustering are selected for testing the efficiency of QLSMFO algorithms and compared with the K-means and ten currently popular swarm intelligence algorithms. Meanwhile, the Wilcoxon rank-sum test and Friedman test are utilized to evaluate the effect of QLSMFO. The simulation experimental results demonstrate that QLSMFO significantly outperforms other algorithms with respect to precision, convergence speed, and stability.

On English Courses and Teaching Strategies in Compound Application-Oriented Talents Training Based on the Education Ideas of CDIO in the Construction of Smart Cities

To improve the level of English education in China and promote the cultivation of compound application-oriented talents in colleges and universities, this research integrates the concept of conceive design implement operate (CDIO) into college English education. First, the CDIO educational concept is introduced in detail. Second, the moth-flame optimization (MFO) algorithm and the support vector machine (SVM) model by the MFO algorithm are presented, and they are applied to teaching evaluation, and then the teaching plan is proposed by the CDIO concept. The teaching evaluation model is applied to the English teaching of students in class A with ordinary learning levels in a university in Hengyang City. The implementation method of the teaching plan is explained in detail by taking “Unit 2 Charlie Chaplin” in the textbook “New Horizons College English” as an example. Finally, the accuracy of the MFO-SVM model for teaching evaluation is verified by simulation analysis, and the corresponding teaching strategies are summarized through teaching evaluation and customization of teaching plans. The experimental results reveal that the MFO-SVM model has an accuracy of 96.43% in teaching evaluation, while the extreme learning machine (ELM), SVM, and back propagation neural network (BPNN) models have an accuracy of 92.27%, 90.45%, and 86.36%, respectively in the evaluation results of the teaching quality. The summarized teaching strategies are mainly divided into four parts, namely, autonomous learning, project-based teaching, dynamic generation and information feedback, and production teaching. This research has a certain reference for the current reform of English education in China and the cultivation of compound application-oriented talents.

Spiral Gaussian mutation sine cosine algorithm: Framework and comprehensive performance optimization

Sine Cosine Algorithm (SCA), as a recently viral population-based meta-heuristic, which is in the extensive application for a variety of optimization cases. Regardless of the concerns on its novelty, SCA updates the population based on a simple updating rule with a basic structure and few parameters. However, it is considered that SCA remains the weakness of low diversity, slow convergence speed, stagnation in local optimum, and low accuracy of solutions. In that case, an attempt is made to design a new SCA version to overcome these shortcomings, namely FGSCA, combined with two strategies, including spiral motion and Gaussian mutation. Spiral motion is inspired by Moth-flame Optimization (MFO), which is employed to strengthen the capacity of exploitation based on the original SCA. The Gaussian mutation is adopted to increase the population's diversity generated by SCA and strengthen local exploration capability. The principle is to update the population generated by SCA, respectively, and then choose the best from these two new populations by greedy selection. Combining the two strategies effectively strengthens the original SCA's performance and maintains a proper exploitation and exploration balance. To examine the performance of FGSCA, it is used for making comparisons with eight well-known meta-heuristic algorithms (MAs), six reported SCA variants, and ten improved MAs on 23 well-known benchmark test problems and 30 standard IEEE CEC2014 benchmark test problems. Results indicate that the overall performance of FGSCA is superior to twenty-four comparative MAs on 53 benchmark test problems. Besides, FGSCA is employed to resolve three practical engineering problems. Results exhibit that FGSCA harvests the best results among twenty-four comparative MAs. Thus, FGSCA is expected to be a mighty and efficient tool for dealing with various complex optimization problems.

Selecting Mobile Services in Cloud and Edge Environment by Moth-Flame Optimization Algorithm

Mobile edge computing is playing an increasingly important role in the rise of mobile internet technology. Services deployed on edge servers nearby mobile users would provide computing capabilities with low latency and high scalability. Usually, a single service is challenging to meet a complex user request, which asks for composing services. With the increasing number of services in the cloud and edge computing environment and the user mobility, selecting appropriate services to meet the complex mobile user's requests becomes a crucial problem. This paper proposes a modified moth-flame optimization algorithm using overall QoS for service selection. Specifically, the overall QoS of services is calculated by combining the subjective and objective QoS with the ordinal relationship and coefficient of variation, and the moth-flame optimization algorithm is improved by adding the differential evolution algorithm. The experimental results show that the proposed approach outperforms some other services selection approaches.

Optimization of TMD Parameters in Frequency Domain Including SSI Effect by Means of Recent Metaheuristic Algorithms

This study presents the application of three recent metaheuristic optimization algorithms to design the tuned mass damper (TMD) for a high-rise building structure, taking into account the effect of soil–structure interaction (SSI) in the frequency domain. The algorithms used in this study were grey wolf optimizer (GWO), moth–flame optimization (MFO), and whale optimization algorithm (WOA), and the obtained results were compared with the well-known particle swarm optimization (PSO) and genetic algorithm (GA). The TMD optimization problem was formulated with two objective functions: the H2 and the H∞ norms of the top floor displacement. The frequency, the mass, and the damping ratio of the TMD were optimized within defined bounds for a 40-story benchmark structure. Unlike in previous research, the optimization in this study was performed for different mass ratios. First, the efficiency of the obtained optimum parameters was plotted in the frequency domain and then tested in the time domain by presenting the top floor displacement of the same benchmark structure. The time domain analysis was carried out using the El Centro and Petrolia earthquakes as external disturbances. It was found that the optimal TMD parameters are affected by the soil type. The GWO algorithm reached the optimal solutions faster and more efficiently than the other algorithms. GA limitations were pointed out.