Improved moth flame optimization algorithm to optimize cost-oriented two-sided assembly line balancing

An improved moth-flame optimization (IMFO) algorithm is proposed to increase the location accuracy of a vision measurement system. This algorithm can optimize the initial pose parameters by improving a series of random solutions to the required precision. A measurement experiment system of space manipulator is designed to precision test. The IMFO algorithm is evaluated on 23 benchmark functions and measurement experiments for pose, and the results are verified by a comparative study with self-adaptive differential evolution (SaDE), moth-flame optimization (MFO), and proactive particle swarm optimization (PPSO). The statistical results of the benchmark functions show that the IMFO algorithm can provide very promising and competitive results. Additionally, the experimental results of pose measurement show that the accuracy of the IMFO algorithm is approximately twice higher than that of other three algorithms. All in all, the experiments indicate that the IMFO algorithm has a good optimization ability to complete the visual identification accurately.

The Moth flame optimization algorithm (MFO) is a nature-inspired optimization algorithm which imitates moths darts into the fire. Similarly, to other meta-heuristic algorithm, MFO still has the disadvantage of getting into the local best and the convergence rate cannot be satisfying. In this paper a Multi-operator Moth Flame Optimization algorithm (MOMFO) is proposed to improve the MFO algorithm, which combines Elite Search strategy, Adaptive control strategy and Chaos search strategy to enhance MFO.A two-stage strategy is used to balance the global search and local search ability, which makes the algorithm have better search performance and accuracy. The proposed MOMFO is benchmarked on eight well-known test functions and real-world problem, and the results show that the algorithm is effective in solving complex nonlinearity problems.

In this article, the optimal allocation of compensation devices in power systems is determined using Improved Moth-flame Optimization (IMFO) algorithm. The main objective of this study is to maximize power system loadability, reduce total power system losses and enhance the voltage profile through finding the optimal locations and sizes of Static VAR Compensator (SVC) and Thyristor Controlled Compensator (TCSC). The methodology of this article is based on Continuation Power Flow (CPF) which is merged with IMFO in Optimum Power Flow (OPF) problem restricted by equality and inequality constraints. The proposed algorithm is implemented to regulate the required control variables. Both of single and multi-objective functions are applied on 57-IEEE bus test system to prove the performance of the proposed algorithm. The acquired simulation results by IMFO are compared with Moth Flame Optimization technique (MFO) and Multi Verse Optimizer (MVO) to emphasize the robustness of the proposed algorithm.

In this research we study the optimization problem of path planning in an oceanic ecosystem with a Static current in the presences of various obstacles with the autonomy of Autonomous underwater vehicles (AUV). We introduce an improved Moth flame optimization algorithm called refraction principle and opposite-based-learning Moth flame optimization (ROBL-MFO) in a Static current environment. This algorithm addresses the differences between the traditional Moth flame optimization (MFO) algorithm. First, it adjusts the moth position update formula we deal with the term of the historically optimal flame average, and improves the convergence velocity of the algorithm. Secondly, it uses a random inverse learning strategy to narrow down the algorithm search. Finally, the principle of refraction provides the algorithm with the ability to jump from the local optima and help the algorithm avoid premature convergence. The numerical results show that the ROBL-MFO improves the classical MFO, in terms of optimization criteria.KeywordsAutonomous underwater vehiclesPath planningStatic currentRefraction principle and opposite-based learning - Moth flame optimization

Time series prediction of wind speed has been widely used in wind power generation. The volatility and instability of wind speed have a large negative impact on wind turbines and power systems, which can lead to grid collapse in severe cases. Therefore, accurate wind speed prediction is crucial for wind power generation. In this paper, considering the influence of different parameters on algorithm training and prediction, an improved moth flame optimization algorithm is constructed to optimize the LSTM wind energy prediction system to obtain better performance. The system consists of three modules: data preprocessing, optimization, and prediction. The data preprocessing module uses fuzzy information granulation to blur the input data. On this basis, the combination of swarm intelligent optimization algorithm and prediction model can effectively predict wind speed time series. Taking the California wind farm as an example, the MAPE of the experiment in the short-term forecast is 3.15%, the MAPE of the medium-term forecast is 4.38%, and the MAPE of the long-term forecast is 18.28%. The experimental results show that the proposed model has obvious advantages over the previous model.

This paper deals with a new joint replenishment problem, in which a number of non-instantaneous deteriorating items are replenished from several suppliers under different quantity discounts schemes. Involving both joint replenishment decisions and supplier selection decisions makes the problem to be NP-hard. In particular, the consideration of non-instantaneous deterioration makes it more challenging to handle. We first construct a mathematical model integrated with a supplier selection system and a joint replenishment program for non-instantaneous deteriorating items to formulate the problem. Then we develop a novel swarm intelligence optimization algorithm, the Improved Moth-flame Optimization (IMFO) algorithm, to solve the proposed model. The results of several numerical experiments analyses reveal that the IMFO algorithm is an effective algorithm for solving the proposed model in terms of solution quality and searching stableness. Finally, we conduct extensive experiments to further investigate the performance of the proposed model.

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.

As the move towards Grid Integrated-Photovoltaic (GI-PV) system is proposed to improve the power quality development. A novel Adaptive Neuro-Fuzzy Inference System (ANFIS) based on improved Moth Flame Optimization (MFO) algorithm is described for grid integrated approach. The solar integration of Maximum Power Point (MPP) fed into modified Switched Boost Inverter (SBI) is presented, this GI-PV connected circuit has become prominent research in a recent scenario for energy demand. Proposed MFOA-ANFIS controller has generated the duty cycle pulses to each converter circuit. The benefit of grid-tied SBI is direct control outer-loop employed to obtain MFO-ANFIS techniques. To maintain a constant voltage DC-link is employed for inner-loop, this presence of constant DC-power to grid loads with support of MFO-ANFIS assists Proportional Integral Differential (PID) method. The results acquired by the simulation expressed that the proposed controller is addressed to maintain active and reactive power exchange, regulate DC bus-link voltages, grid voltage, and grid current. The effectiveness of the practical implication research is achieved by the output as represented as minimum grid harmonics, load current, and compensator current as verified in MATLAB/Simulink platform.

An IMFO-LSTM_BIGRU combined network for long-term multiple battery states prediction for electric vehicles

Renewable Energy Resources (RES) can supply a clean and a smart solution to satisfy the rapidly growing load demand. The Photovoltaic (PV) and wind turbines are considered as resources of Distributed Generation (DG). The integration of PV - DG units into distribution systems has a significant effect on reducing the system active power losses. In this paper, a new technique based on the Moth Flame Optimization (MFO) algorithm, is used to optimally allocate the PV -DG units in Radial Distribution Systems (RDSs) and determine the optimal size of each unit. The objective of the technique is to minimize the total active power losses of the system, while satisfying several operating constraints. The proposed optimization algorithm is tested using three different IEEE standard RDSs of various sizes, which are 33, 69 and 118-bus systems. In order to demonstrate the applicability and the performance of the MFO algorithm, numerical simulations are obtained when applying the MFO algorithm, and compared with different existing optimization techniques. This comparison shows the validity of the proposed MFO algorithm.

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 recently proposed swarm inspired optimization algorithm based on the navigation approach of Moths in space entitle as Moth-Flame Optimization (MFO) algorithm, is used for solve equality and inequality constrained optimization, real challenging layout problems. The navigating strategy of moths in universe entitles transverse orientation, a well active mechanism for travel so far distance in the straight direction. In fact, artificial lights trick moths, so they follow a deadly spiral path. MFO algorithm gives the competitive results with both continuous and discrete control variables. Real Challenging Constrained Optimization is a way of optimising an objective function in presence of constraints on some control variables. MFO have an ability to solve both constraints that may be either hard constrained or soft constrained. A statical representation of MFO algorithm expresses the best objective function value with reference to accuracy and standard deviation over recently proposed and most popular optimization algorithms. Fourteen constrained benchmark function of real engineering problems have been calculated and gained solutions were compared with the solution obtained by various recognized algorithms. The results obtained through MFO algorithm represent better solutions in the field of engineering design problems among many recently developed algorithms.

Inverse transient radiation analysis in one-dimensional participating slab using improved Ant Colony Optimization algorithms

Wind and photovoltaic power have become vital components in the transformation of energy structures. However, their inherent features of intermittency, fluctuations, and randomness severely constrain large-scale grid integration and efficient utilization. To address this challenge, we conduct a dayahead peak-shaving joint optimal dispatch study for the Hydro-Wind-PV Complementary System. By comprehensively considering grid-side stability requirements and the complex operational constraints of the complementary system, a day-ahead joint optimization dispatch model is established to minimize the variance of residual load. Accounting for uncertainties in forecasts, typical dispatch scenarios are constructed as model inputs based on hydrological conditions and weather patterns. The Improved Moth-Flame Optimization Algorithm is ultimately applied to achieve efficient problem-solving. Experimental results demonstrate that the proposed methodology fully leverages the peak-shaving capacity of the Hybrid Pumped Storage Hydropower Plant, effectively reduces the peak-valley difference in grid residual load, and provides decision support for scheduling plan formulation in the complementary system within new-type energy systems.

This paper introduces the comprehensive study of automatic generation control (AGC) system with proportional integral derivative controller with filter (PIDF). A nature-inspired optimization algorithm that is moth-flame optimization (MFO) algorithm is employed for controller gains concurrent optimization. First, a two-area non-reheat interconnected thermal power system is investigated and obtained superior performance with MFO-tuned PI controller as equated to recent competitive optimization techniques, but major improvement is investigated with PIDF controller. Then, the work is continued to a three-area interconnected hybrid system with proper generation rate constraint (GRC). PIDF controller can be implemented as secondary controller in the areas, whose performance is equated with conventional controllers. Result analysis divulges that MFO-tuned PIDF controller performs effective than all other controllers considered in this article. Robustness of the proposed technique is evaluated using sensitivity analysis.