An efficient hybrid algorithm based on Water Cycle and Moth-Flame Optimization algorithms for solving numerical and constrained engineering optimization problems

Khalilpourazari, Soheyl; Khalilpourazary, Saman

doi:10.1007/s00500-017-2894-y

Cited by 176 publications

(84 citation statements)

References 69 publications

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“…To indicate the excellent performance of the IMFO method, it has been compared with eight well-established and competitive methods. These compared methods were the basis MFO [30], opposition-based MFO method (OMFO) [33], hybrid water cycle-moth-flame optimization method (WCMFO) [34], brain storm optimization method (BSO) [40], comprehensive learning PSO (CLPSO) method [41], artificial bee colony (ABC) method [20], sine cosine algorithm (SCA) [42], and improved JAYA (IJAYA) method [26]. All the compared methods were independently conducted 30 times on each PV model.…”

Section: Results and Analysismentioning

confidence: 99%

“…To demonstrate that IMFO can effectively escape the local optimum when solving multimodal problems, we further tested the performance of IMFO, MFO [30], OMFO [33], WCMFO [34], BSO [40], CLPSO [41], ABC [20], SCA [42], and IJAYA [26] on the CEC2017 benchmark suite [44]. Due to the space constraints, the 30 benchmark functions of CEC2017 were listed in Table A5 of the Appendix A.…”

Section: Discussion Of Imfomentioning

confidence: 99%

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Parameters Extraction of Photovoltaic Models Using an Improved Moth-Flame Optimization

Sheng

Wang

et al. 2019

Energies

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Photovoltaic (PV) models’ parameter extraction with the tested current-voltage values is vital for the optimization, control, and evaluation of the PV systems. To reliably and accurately extract their parameters, this paper presents one improved moths-flames optimization (IMFO) method. In the IMFO, a double flames generation (DFG) strategy is proposed to generate two different types of target flames for guiding the flying of moths. Furthermore, two different update strategies are developed for updating the positions of moths. To greatly balance the exploitation and exploration, we adopt a probability to rationally select one of the two update strategies for each moth at each iteration. The proposed IMFO is used to distinguish the parameter of three test PV models including single diode model (SDM), double diode model (DDM), and PV module model (PMM). The results indicate that, compared with other well-established methods, the proposed IMFO can obtain an extremely promising performance.

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Section: Results and Analysismentioning

confidence: 99%

Section: Discussion Of Imfomentioning

confidence: 99%

Parameters Extraction of Photovoltaic Models Using an Improved Moth-Flame Optimization

Sheng

Wang

et al. 2019

Energies

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“…OF � sort(OM) (8) else (9) F � sort(M t− 1 , M t ) (10) OF � sort(M t-1 , M t ) (11) end if (12) for i � 1 : n (13) for j � 1 : d (14) update t (15) calculate D with respect to the corresponding flame (16) update M(i, j) using equation (15) with respect to the corresponding flame (17) end for (18) end for (19) update the position of the current optimal agent using Lévy-flight (20) F_lévy � Lévy(F) (21) OF_lévy � FitnessFunction(F_lévy) (22) using the Metropolis criterion for OF and OF_lévy (23) update the position best flame obtained so far (24) update flame number using equation 16 Since the range of indicators is not uniform after quanti cation, the quantized data is normalized, and the data of each index is uniformly compressed into the interval [0, 1], which is bene cial to speed up the training of the model and improve the learning outcomes. e formula is as follows: Fitness value obtained so far Mathematical Problems in Engineering…”

Section: Test Settingsmentioning

confidence: 99%

“…It has the advantages of less adjustment parameters, faster convergence, and simple implementation. Hence, it is often used in engineering optimization problems [15,16]. However, MFO also has some shortcomings in the process of optimization.…”

Section: Introductionmentioning

confidence: 99%

Air Target Threat Assessment Based on Improved Moth Flame Optimization‐Gray Neural Network Model

Yue

Yang

Zuo

et al. 2019

Mathematical Problems in Engineering

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Air target threat assessment is a key issue in air defense operations. Aiming at the shortcomings of traditional threat assessment methods, such as one-sided, subjective, and low-accuracy, a new method of air target threat assessment based on gray neural network model (GNNM) optimized by improved moth flame optimization (IMFO) algorithm is proposed. The model fully combines with excellent optimization performance of IMFO with powerful learning performance of GNNM. Finally, the model is trained and evaluated using the target threat database data. The simulation results show that compared with the GNNM model and the MFO-GNNM model, the proposed model has a mean square error of only 0.0012 when conducting threat assessment, which has higher accuracy and evaluates 25 groups of targets in 10 milliseconds, which meets real-time requirements. Therefore, the model can be effectively used for air target threat assessment.

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“…The idea behind the heuristic-based method, it can solve various problems regardless of the basic algorithm framework. Metaheuristic methods can be divided into three main categories as stated by Khalilpourazari and Khalilpourazary in 2019, [10]: evolutionary algorithms, swarm intelligence, and physical-based algorithms.…”

mentioning

confidence: 99%

Neighborhood search methods with Moth Optimization algorithm as a wrapper method for feature selection problems

Alzaqebah¹,

Alrefai²,

Ahmed³

et al. 2020

IJECE

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Feature selection methods are used to select a subset of features from data, therefore only the useful information can be mined from the samples to get better accuracy and improves the computational efficiency of the learning model. Moth-flam Optimization (MFO) algorithm is a population-based approach, that simulates the behavior of real moth in nature, one drawback of the MFO algorithm is that the solutions move toward the best solution, and it easily can be stuck in local optima as we investigated in this paper, therefore, we proposed a MFO Algorithm combined with a neighborhood search method for feature selection problems, in order to avoid the MFO algorithm getting trapped in a local optima, and helps in avoiding the premature convergence, the neighborhood search method is applied after a predefined number of unimproved iterations (the number of tries fail to improve the current solution). As a result, the proposed algorithm shows good performance when compared with the original MFO algorithm and with state-of-the-art approaches.

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An efficient hybrid algorithm based on Water Cycle and Moth-Flame Optimization algorithms for solving numerical and constrained engineering optimization problems

Cited by 176 publications

References 69 publications

Parameters Extraction of Photovoltaic Models Using an Improved Moth-Flame Optimization

Parameters Extraction of Photovoltaic Models Using an Improved Moth-Flame Optimization

Air Target Threat Assessment Based on Improved Moth Flame Optimization‐Gray Neural Network Model

Neighborhood search methods with Moth Optimization algorithm as a wrapper method for feature selection problems

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