Optimization of Soft Actuator Geometry and Material Modeling Using Metaheuristic Algorithms

Cross-correlation stacking-based microseismic source location using three metaheuristic optimization algorithms

This paper is concerned with defining a new Weight Function Based model (WFB), which describes the hyper-elastic materials stress-strain behavior. Numerous hyper-elastic theoretical material models have been proposed over the past 60 years capturing the stress-strain behavior of large deformation incompressible isotropic materials. The newly proposed method has been verified against the historic Treloar’s test data for uni-axial, bi-axial and pure shear loadings of Treloar’s vulcanized rubber material, showing a promising level of confidence compared to the Ogden and the Yeoh methods. A non-linear least square optimization Matlab tool was used to determine the WFB, Yeoh and Ogden models material parameters. A comparison between the results of the three models was performed showing that the newly proposed model is more accurate for uni-axial tension as it has an error value which is less than the Ogden and Yeoh models by 1.0 to 39%. Also, the parameters calculation by more than 95%, for the bi-axial and pure shear loading cases compared to the Ogden model. Natural rubber test specimens have been tensioned using a tensile testing machine and the WFB model was applied to fit the test data results showing a very good curve fitting with an average error of 0.44%.WFB model has reduced processing time for the model.

This paper presents the application of an active energy management strategy to a hybrid system consisting of a proton exchange membrane fuel cell (PEMFC), battery, and supercapacitor. The purpose of energy management is to control the battery and supercapacitor states of charge (SOCs) as well as minimizing hydrogen consumption. Energy management should be applied to hybrid systems created in this way to increase efficiency and control working conditions. In this study, optimization of an existing model in the literature with different meta-heuristic methods was further examined and results similar to those in the literature were obtained. Ant lion optimizer (ALO), moth-flame optimization (MFO), dragonfly algorithm (DA), sine cosine algorithm (SCA), multi-verse optimizer (MVO), particle swarm optimization (PSO), and whale optimization algorithm (WOA) meta-heuristic algorithms were applied to control the flow of power between sources. The optimization methods were compared in terms of hydrogen consumption and calculation time. Simulation studies were conducted in Matlab/Simulink R2020b (academic license). The contribution of the study is that the optimization methods of ant lion algorithm, moth-flame algorithm, and sine cosine algorithm were applied to this system for the first time. It was concluded that the most effective method in terms of hydrogen consumption and computational burden was the sine cosine algorithm. In addition, the sine cosine algorithm provided better results than similar meta-heuristic algorithms in the literature in terms of hydrogen consumption. At the same time, meta-heuristic optimization algorithms and equivalent consumption minimization strategy (ECMS) and classical proportional integral (PI) control strategy were compared as a benchmark study as done in the literature, and it was concluded that meta-heuristic algorithms were more effective in terms of hydrogen consumption and computational time.

Electrochemical machining (ECM) is a preferred advanced machining process for machining Monel 400 alloys. During the machining, the toxic nickel hydroxides in the sludge are formed. Therefore, it becomes necessary to determine the optimum ECM process parameters that minimize the nickel presence (NP) emission in the sludge while maximizing the material removal rate (MRR). In this investigation, the predominant ECM process parameters, such as the applied voltage, flow rate, and electrolyte concentration, were controlled to study their effect on the performance measures (i.e., MRR and NP). A meta-heuristic algorithm, the grey wolf optimizer (GWO), was used for the multi-objective optimization of the process parameters for ECM, and its results were compared with the moth-flame optimization (MFO) and particle swarm optimization (PSO) algorithms. It was observed from the surface, main, and interaction plots of this experimentation that all the process variables influenced the objectives significantly. The TOPSIS algorithm was employed to convert multiple objectives into a single objective used in meta-heuristic algorithms. In the convergence plot for the MRR model, the PSO algorithm converged very quickly in 10 iterations, while GWO and MFO took 14 and 64 iterations, respectively. In the case of the NP model, the PSO tool took only 6 iterations to converge, whereas MFO and GWO took 48 and 88 iterations, respectively. However, both MFO and GWO obtained the same solutions of EC = 132.014 g/L, V = 2406 V, and FR = 2.8455 L/min with the best conflicting performances (i.e., MRR = 0.242 g/min and NP = 57.7202 PPM). Hence, it is confirmed that these metaheuristic algorithms of MFO and GWO are more suitable for finding the optimum process parameters for machining Monel 400 alloys with ECM. This work explores a greater scope for the ECM process with better machining performance.

On a molecular statistical basis for Ogden's model of rubber elasticity

In this work, the most common problem of the modern power system named optimal power flow (OPF) is optimized using the novel metaheuristic optimization algorithm Moth-Flame Optimizer (MFO). The MFO is inspired by the navigation strategy of moths in universe. MFO has a fast convergence rate due to the use of roulette wheel selection method. For the OPF solution, standard IEEE-30 bus test system is used. MFO is applied to solve the proposed problem. The problems considered in the OPF are fuel cost reduction, voltage profile improvement, voltage stability enhancement, active power loss minimization and reactive power loss minimization. The results obtained by MFO are compared with other techniques such as Flower Pollination Algorithm (FPA) and particle swarm optimizer (PSO). Results show that MFO gives better optimization values as compared with FPA and PSO which verifies the effectiveness of the suggested algorithm.

In this work, the most common problem of the modern power system named optimal power flow (OPF) is optimized using the novel meta-heuristic optimisation algorithm Moth-Flame Optimizer (MFO). MFO is inspired by the navigation strategy of Moths in universe. MFO has a fast convergence rate due to use of roulette wheel selection method. In order to resolve the optimal power flow problem, standard IEEE-30 bus system is used. MFO is implemented for the solution of proposed problem. The problems considered in the OPF problem are Fuel Cost Reduction, Active Power Loss Minimization, and Reactive Power Loss Minimization. The results obtained by MFO is compared with other techniques such as Flower Pollination Algorithm (FPA) and Particle Swarm Optimizer (PSO). Results shows that MFO gives better optimisation values as compared with FPA and PSO that confirms the effectiveness of the suggested algorithm.

A recent nature-inspired metaheuristic swarm intelligence algorithm called Moth Flame Optimization (MFO) is proposed for Renewable Distributed Generation (RDG) and Distribution Static Compensator (D-STATCOM) optimal siting and sizing problem. First, a power flow analysis was performed which form the base case scenario. Then, the MFO is integrated with the power flow method to implement DG and combine DG/DSTATCOM in the IEEE 33-bus radial distribution system. A multi-objective function was formulated for use in the MFO to minimize total power losses and improve the voltage profile of the system subject to the network constraints. The effectiveness of the proposed MFO is tested, its performance investigated and compared with two swarm-based algorithms, namely; Firefly Algorithm (FA) and Particle Swarm Optimization (PSO) algorithm. The results of the simulation showed that MFO can optimally site and size DG and DSTATCOM in a radial distribution system effectively, thus reducing the total power losses and improving the system's voltage profile compared to its equivalents PSO. The effectiveness of the proposed MFO is very fast in the optimization process, thus reducing the computational time and minimizing computer resources usage over FA and PSO. Also, the simulation results revealed combine DG/DSTATCOM provides better voltage profile enhancement and reduces the active power loss by 72% compared to the DG system only with 49.7% loss reduction.

Many problems in daily life exhibit nonlinear behavior. Therefore, it is important to solve nonlinear problems. These problems are complex and difficult due to their nonlinear nature. It is seen in the literature that different artificial intelligence techniques are used to solve these problems. One of the most important of these techniques is artificial neural networks. Obtaining successful results with an artificial neural network depends on its training process. In other words, it should be trained with a good training algorithm. Especially, metaheuristic algorithms are frequently used in artificial neural network training due to their advantages. In this study, for the first time, the performance of sixteen metaheuristic algorithms in artificial neural network training for the identification of nonlinear systems is analyzed. It is aimed to determine the most effective metaheuristic neural network training algorithms. The metaheuristic algorithms are examined in terms of solution quality and convergence speed. In the applications, six nonlinear systems are used. The mean-squared error (MSE) is utilized as the error metric. The best mean training error values obtained for six nonlinear systems were 3.5×10−4, 4.7×10−4, 5.6×10−5, 4.8×10−4, 5.2×10−4, and 2.4×10−3, respectively. In addition, the best mean test error values found for all systems were successful. When the results were examined, it was observed that biogeography-based optimization, moth–flame optimization, the artificial bee colony algorithm, teaching–learning-based optimization, and the multi-verse optimizer were generally more effective than other metaheuristic algorithms in the identification of nonlinear systems.

Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems

– This paper reports the performance comparison among several metaheuristics algorithms on the neural network training. In this research we use five metaheuristic algorithms which implements for diabetes data, there are Particle Swarm Optimizer (PSO), Multi-Verse Optimizer (MVO), Grey Wolf Optimizer (GWO), Bat Algorithm (BAT), and Cuckoo Search (CS). The Cuckoo Search (CS) algorithm is a recently developed meta-heuristic optimization algorithm which is suitable for solving optimization problems. The main problem to be solved is to find the most effective meta-heuristic optimization algorithm. The search was done by comparing the results of PSO (Particle Swarm Optimizer) algorithm test with the test with MVO (Multi-Verse Optimizer), GWO (Grey Wolf Optimizer), BAT (Bat Algorithm), and CS (Cuckoo Search). Then look for the most effective algorithm. The best metaheuristic algorithm that we had in this research is MVO, with best case accuracy result 78% and lowest standard deviation is 0.00675 and the worst is BAT algorithm with best case accuracy 77% and standard deviation 0.14571 .

In this paper, a novel hybrid metaheuristic optimization algorithm which is based on Particle Swarm Optimization (PSO) and recently developed Spotted Hyena Optimizer (SHO) named as Hybrid Particle Swarm and Spotted Hyena Optimizer (HPSSHO) is presented. The main concept of this algorithm is to improve the hunting strategy of Spotted Hyena Optimizer using particle swarm algorithm. The proposed algorithm is compared with four metaheuristic algorithms (i.e., SHO, PSO, DE, and GA) and benchmarked it on thirteen well-known benchmark test functions which include unimodal and multimodal. The convergence analysis of the proposed as well as other metaheuristics has also been analyzed and compared. The algorithm is tested on 25-bar real-life constraint engineering design problem to demonstrate its applicability. The experimental results reveal that the proposed algorithm performs better than other metaheuristic algorithms.

Gas lift is one of the most commonly used artificial lift method in oil producing wells. The technique requires constant Optimization of injection gas quantity in the well to maximize oil production. High pressure Lift Gas allocation is an important step in optimization process to reduce the investment on costly and scarce lift gas and maximize oil recovery. In this work, an attempt is made to optimize a group of wells with application of an innovative metaheuristic algorithm. The objective function can be either the generated profit or total produced oil as a function of injected gas. To achieve maximum recovery from gas-lifted wells, it's essential to identify the optimum injection rate for specific facility constraints, including gas availability, maximum injection depth, and compression capabilities. Normally, these parameters are unchangeable because of prior selection and installation, except when optimization techniques are implemented in the design phase. The Gas Lift Performance Curves (GLPC) are the main design element used for optimized gas injection. These curves are generated by modelling wells in a multiphase steady-state simulator. After building model, sensitivity analysis is run, and the curves are generated. In this work, the common workflow to generate GLPC is followed. Then, a new correlation for GLPC is suggested with help of a bio-inspired meta heuristic Whale Optimization Algorithm (WOA). The correlation is then used to formulate a case study for few wells located in Caspian Sea. R-score and Root Mean Square Error (RMSE) values compared. Wells and PVT models are used to create a simulation. The optimization problem is mathematically formulated using stochastic optimization techniques. The new correlation is used to fit the GLPC with WOA. A set of Pareto Optimal solutions are derived. Results of WOA is compared with other two potential algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) to obtain the global optimum of the distribution of a limited gas lift quantity. The advantages of WOA over PSO and GA is discussed, and the optimum gas allocation is obtained. The correlation outperforms all other models and also bring an injection gas saving of 10-15% and improved oil production to the extent of 5-10% with an automated WOA algorithm application. Compared to the traditionally applied numerical methods for gas allocation and optimization, AI meta-heuristic algorithms are considered as revolutionary methods, offering more solutions to gas allocation problems, which can solve non-linear multi-objective optimization problems quickly and more accurately with fast convergence. WOA is simpler, faster and outperforms PSO and GA and yield maximum production at minimum cost.

Multilevel thresholding is a popular image segmentation technique. However, computational complexity of multilevel thresholding increases very rapidly with increasing number of thresholds. Metaheuristic algorithms are applied to reduce computational complexity of multilevel thresholding. A new method of multilevel thresholding based on Moth-Flame Optimization (MFO) algorithm is proposed in this paper. The goodness of the thresholds is evaluated using Kapur's entropy or Otsu's between class variance function. The proposed method is tested on a set of benchmark test images and the performance is compared with PSO (Particle Swarm Optimization) and BFO (Bacterial Foraging Optimization) based methods. The results are analyzed objectively using the fitness function and the Peak Signal to Noise Ratio (PSNR) values. It is found that MFO based multilevel thresholding method performs better than the PSO and BFO based methods.

Hybrid generating systems in power networks have emerged as a result of the rapid growth of renewable infrastructure and widespread support for green energy. One of the most significant problems in designing and operating an electric power generation system is the efficient scheduling of all power generation facilities to meet the rising power demand. Economic load dispatch (ELD) is a generic procedure in the electrical power system, and the ELD in power system problems involves scheduling the power generating units to reduce cost and satisfy system constraints. Metaheuristic algorithms are gaining popularity for solving constrained ELD issues because of their larger global solution capacity, flexibility, and derivative-free construction. In this research, the ELD problem of integrated renewable resources is solved using a unique solution model based on hybrid optimization. Furthermore, this work considers multiobjectives such as total wind generation cost, total cost function of thermal units, and penalty cost function. The hybrid optimization model optimizes the power generation of thermal power plants within the maximum and minimum limitations. Additionally, the turbines are selected optimally by the hybrid optimization model to ensure the power generation of wind turbines based on the demands. The proposed hybrid optimization is a combination of particle swarm optimization (PSO) and cat swarm optimization (CSO), and the new algorithm is referred to as the particle oriented cat swarm optimization model (POCSO). Finally, the performance of the proposed work is compared to other conventional models. In particular, the cost function of POCSO is 6.25%, 6%, 11.7%, 36%, 27%, and 46.42% better than the cost function of whale optimization algorithm (WOA), elephant herd optimization (EHO), moth-flame optimization (MFO), dragonfly algorithm (DA), sealion optimization (SLnO), CSO, and PSO methods, respectively. Also, for IEEE-30 bus system, the best value of the proposed work is 7.46%, 5.41%, 16.30%, 14.88%, 17.60%, 13.86%, 15.21%, 17.49%, and 4.27% better than that of the PSO, CSO, SLnO, DA, MFO, EHO, WOA, multiagent glowworm swarm optimization (MAGSO), and Harris hawks optimization-based feed-forward neural network (HHO-FNN) methods, respectively.