Particle Swarm Optimization PSO)

"This paper proposes an improved maximum power
point tracking (MPPT) method for the photovoltaic (PV) system
using a modified particle swarm optimization (PSO) algorithm.
The main advantage of the method is the reduction of the steadystate
oscillation (to practically zero) once the maximum power
point (MPP) is located. Furthermore, the proposedmethod has the
ability to track the MPP for the extreme environmental condition,
e.g., large fluctuations of insolation and partial shading condition.
The algorithm is simple and can be computed very rapidly; thus,
its implementation using a low-cost microcontroller is possible. To
evaluate the effectiveness of the proposed method, MATLAB simulations
are carried out under very challenging conditions, namely
step changes in irradiance, step changes in load, and partial shading
of the PV array. Its performance is compared with the conventional
Hill Climbing (HC) method. Finally, an experimental rig
that comprises of a buck–boost converter fed by a custom-designed
solar array simulator is set up to emulate the simulation. The software
development is carried out in the Dspace 1104 environment
using a TMS320F240 digital signal processor. The superiority of
the proposed method over the HC in terms of tracking speed and
steady-state oscillations is highlighted by simulation and experimental
results."
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6138329&tag=1

This paper proposes a penalty based differential evolution (P-DE) for extracting the parameters of solar photovoltaic (PV) modules at different environmental conditions. The two diode model of a solar cell is used as the basis for the extraction problem. The analyses carried out using synthetic current-voltage (I-V) data set showed that the proposed P-DE outperforms other Evolutionary Algorithm methods, namely the simulated annealing (SA), genetic algorithm (GA), and particle swarm optimization (PSO). P-DE consistently converges to the global optimum values very rapidly. The performances are evaluated using the well known quality test and student T-tests. Furthermore, the P-DE extraction method is practically validated by six solar modules of different types (multi-crystalline, mono-crystalline, and thin-film). The results were found to be in close agreement with the experimental I-V data set, especially at very low irradiance values. The latter can be very useful in predicting the performance of the solar system under partial shading conditions. The main application of the proposed work is the possibility of developing a highly accurate simulator for solar PV system designer.

Renewable energy is becoming more popular due to environmental concerns about the previous energy source. Accurate solar photovoltaic system model parameters substantially impact the efficiency of solar energy conversion to electricity. In this matter, swarm and evolutionary optimization algorithms have been widely utilized in dealing with practical problems due to their more straightforward concepts, efficacy, flexibility, and easy-to-implement procedural frameworks. However, the nonlinearity and complexity of the photovoltaic parameter identification caused swarm and evolutionary optimizers to exhibit Immaturity in the obtained solutions. To deal with such concerns on immature convergence and imbalanced searching trends, in this paper, we proposed the PSOCS algorithm based on the core components of particle swarm optimization (PSO) and the strategy of random reselection of parasitic nests that appeared in the cuckoo search. The parameters of the single-diode model and the double-diode model are identified based on several experiments. Based on the comprehensive comparisons, results indicate that the developed PSOCS algorithm has higher convergence accuracy and better stability than the original PSO, the original cuckoo search, and other studied algorithms. The findings indicate that we suggest the PSOCS algorithm as an enhanced and efficient approach for dealing with parameter extraction of solar photovoltaic modules. We think this simple variant of PSO can be employed as a tool for the optimal designing of photovoltaic systems.

Accurate and fast islanding detection of distributed generation is highly important for its successful operation
in distribution networks. Up to now, various islanding detection technique based on communication,
passive, active and hybrid methods have been proposed. However, each technique suffers from
certain demerits that cause inaccuracies in islanding detection. Computational intelligence based techniques,
due to their robustness and flexibility in dealing with complex nonlinear systems, is an option
that might solve this problem. This paper aims to provide a comprehensive review of computational
intelligence based techniques applied for islanding detection of distributed generation. Moreover, the
paper compares the accuracies of computational intelligence based techniques over existing techniques
to provide a handful of information for industries and utility researchers to determine the best method
for their respective system.

The estimation of prediction intervals (PIs) is a major issue limiting the use of Artificial Neural Networks (ANN) solutions for operational streamflow forecasting. Recently, a Lower Upper Bound Estimation (LUBE) method has been proposed that outperforms traditional techniques for ANN-based PI estimation. This method construct ANNs with two output neurons that directly approximate the lower and upper bounds of the PIs. The training is performed by minimizing a coverage width-based criterion (CWC), which is a compound, highly nonlinear and discontinuous function. In this work, we test the suitability of the LUBE approach in producing PIs at different confidence levels (CL) for the 6 hours ahead streamflow discharges of the Susquehanna and Nehalem Rivers, US. Due to the success of Particle Swarm Optimization (PSO) in LUBE applications, variants of this algorithm have been employed for CWC minimization. The results obtained are found to vary substantially depending on the chosen PSO paradigm. While the returned PIs are poor when single-objective swarm optimization is employed, substantial improvements are recorded when a multi-objective framework is considered for ANN development. In particular, the Multi-Objective Fully Informed Particle Swarm (MOFIPS) optimization algorithm is found to return valid PIs for both rivers and for the three CL considered of 90%, 95% and 99%. With average PI widths ranging from a minimum of 7% to a maximum of 15% of the range of the streamflow data in the test datasets, MOFIPS-based LUBE represents a viable option for straightforward design of more reliable interval-based streamflow forecasting models.

 
Keywords: MOFIPS; PSO; prediction interval; rainfall-runoff modeling; LUBE; neural networks; streamflow prediction;

Warehouses constitute a key component of supply chain networks. An improvement to the operational efficiency and the productivity of warehouses is crucial for supply chain practitioners and industrial managers. Overall warehouse efficiency largely depends on synergic performance. The managers preemptively estimate the overall warehouse performance (OWP), which requires an accurate prediction of a warehouse's key performance indicators (KPIs). This research aims to predict the KPIs of a ready-made garment (RMG) warehouse in Bangladesh with a low forecasting error in order to precisely measure OWP. Incorporating advice from experts, conducting a literature review, and accepting the limitations of data availability, this study identifies 13 KPIs. The traditional grey method (GM)-the GM (1, 1) model-is established to estimate the grey data with limited historical information but not absolute. To reduce the limitations of GM (1, 1), this paper introduces a novel particle swarm optimization (PSO)-based grey model-PSOGM (1, 1)-to predict the warehouse's KPIs with less forecasting error. This study also uses the genetic algorithm (GA)-based grey model-GAGM (1, 1)-the discrete grey model-DGM (1, 1)-to assess the performance of the proposed model in terms of the mean absolute percentage error and other assessment metrics. The proposed model outperforms the existing grey models in projecting OWP through the forecasting of KPIs over a 5-month period. To find out the optimal parameters of the PSO and GA algorithms before combining them with the grey model, this study adopts the Taguchi design method. Finally, this study aims to help warehouse professionals make quick OWP estimations in advance to take control measures regarding warehouse productivity and efficiency.

In this work, we suggest that the poorer results obtained with Particle Swarm Optimization (PSO) in some previous studies should be attributed to the cross-validation scheme commonly employed to improve generalization of PSO-trained Neural Network River Forecasting (NNRF) models. Cross-validation entails splitting the training dataset into two, and accepting particle position updates only if fitness improvements are concurrently measured on both subsets. The NNRF calibration process thus becomes a multi-objective (MO) optimization problem which is still addressed as a single-objective one. In our opinion, PSO cross-validated training should be carried out under a MO optimization framework instead. Therefore, in this work we introduce a novel MO variant of the swarm optimization algorithm to train NNRF models for the prediction of future streamflow discharges in the Shenandoah River watershed, Virginia (US). The case study comprises over 9,000 observations of both streamflow and rainfall observations, spanning a period of almost 25 years. The newly introduced Multi-objective Fully Informed Particle Swarm (MOFIPS) optimization algorithm is found to provide better performing models with respect to those developed using the standard PSO, as well as advanced gradient-based optimization techniques. These findings encourage the use of a MO approach to NNRF cross-validated training with swarm optimization.

Keywords:
FIPS, multi-objective, particle swarm optimization, PSO, neural network river forecasting, NNRF

The goal of this study was to investigate a novel approach of predicting the ultimate capacity of axially loaded circular concrete-filled steel tube (CCFST) columns. A hybrid intelligent system, namely GAP-BART, was developed based on the Bayesian additive regression tree (BART) combining with three nature-inspired optimization algorithms such as Genetic Algorithm (GA), Artificial Bee Colony (ABC), and Particle Swarm Optimization (PSO), and then applied. Three sub-hybrid models of the system were built, abbreviated as G-BART, A-BART, and P-BART, respectively, using 504 experimental data collected from published research. The compiled database covered five input variables, including the diameter of the circular cross-section-section (D), the wall thickness of the steel tube (t), the length of the column (L), the compressive strength of the concrete (f ′ c), and the yield strength of the steel tube (f y). The coefficient of determination (R 2) values of (0.9971, 0.9982, and 0.9986) and (0.9891, 0.9923 and 0.9931) were achieved for training and testing of G-BART, A-BART, and P-BART models, respectively. The P-BART model performed the lowest RMSE and MAE values for the training and testing set of (66.85 kN and 49.60 kN) and (141.24 kN and 102.04 kN), respectively. These results indicated that although the proposed models were able to estimate ultimate axial capacity with high accuracy, the P-BART model had the best performance among them. For benchmarking, the obtained results were validated against several mathematical approaches as well as other AI techniques (MARS, ANN). The findings of the comparative analysis clearly showed superior ability to predict the CFST ultimate axial capacity relative to the benchmark models. The relative importance of each predictor was investigated to find the most significant input variables. The results confirmed that the hybrid GAP-BART system can serve as a reliable and accurate tool for the design of CCFST columns and to predict their performance.

Accurate simulation of evaporation plays an important role in the efficient management of water the direct method where Class A pan-evaporimeter is used, and an indirect method that includes empirical equations. However, despite its widespread usage, Class A pan-evaporimeter method can be affected by human and instrumentation errors. Empirical equations, on the other hand, are generally linked to the different climatic factors that should provide initial or boundary conditions in the mathematical equations that affect the rate of evaporation. Considering these challenging, heuristic soft computing approaches that do not need key information about the physics of evaporation. In this study, a Quantum-behaved Particle Swarm Optimization algorithm, embedded into a multi-layer perceptron technique, is developed to estimate the evaporation rates over a daily forecast horizon. The measured evaporation data from 2012–2014 for Talesh meteorological station located in Northern Iran are employed. The predictive accuracy of the MLP-QPSO model is evaluated with existing methods: i.e. a hybrid MLP-PSO and a standalone MLP model. The results are evaluated in respect to statistical performance criterion: the mean absolute error, root mean square error (RMSE), Willmott’s Index and the Nash–Sutcliffe coefficient. In conjunction with these metrics, Taylor diagrams are also utilized to assess the level of agreement between the forecasted and observed evaporation data. Evidently, the hybrid MLP-QPSO model is confirmed to be an optimal forecasting tool applied for estimating daily pan evaporation, outperforming both the hybrid MLP-PSO and the standalone model.In light of these results, the present study justifies the potential utility of the hybrid MLP-QPSO model to be applied for estimating daily evaporation rates in North of Iran.

A spillway is a structure used to regulate the discharge flowing from hydraulic structures such as a dam. It also helps to dissipate the excess energy of water through the still basins. Therefore, it has a significant effect on the safety of the dam. One of the most serious problems that may be happening below the spillway is bed scouring, which leads to soil erosion and spillway failure. This will happen due to the high flow velocity on the spillway. In this study, an alternative to the conventional methods was employed to predict scour depth (SD) downstream of the ski-jump spillway. A novel optimization algorithm, namely, Harris hawks optimization (HHO), was proposed to enhance the performance of an artificial neural network (ANN) to predict the SD. The performance of the new hybrid ANN-HHO model was compared with two hybrid models, namely, the particle swarm optimization with ANN (ANN-PSO) model and the genetic algorithm with ANN (ANN-GA) model to illustrate the efficiency of ANN-HHO. Additionally, the results of the three hybrid models were compared with the traditional ANN and the empirical Wu model (WM) through performance metrics, viz., mean absolute error (MAE), root mean square error (RMSE), coefficient of correlation (CC), Willmott index (WI), mean absolute percentage error (MAPE), and through graphical interpretation (line, scatter, and box plots, and Taylor diagram). Results of the analysis revealed that the ANN-HHO model (MAE = 0.1760 m, RMSE = 0.2538 m) outperformed ANN-PSO (MAE = 0.2094 m, RMSE = 0.2891 m), ANN-GA (MAE = 0.2178 m, RMSE = 0.2981 m), ANN (MAE = 0.2494 m, RMSE = 0.3152 m) and WM (MAE = 0.1868 m, RMSE = 0.2701 m) models in the testing period. Besides, graphical inspection displays better accuracy of the ANN-HHO model than ANN-PSO, ANN-GA, ANN, and WM models for prediction of SD around the ski-jump spillway.

This work proposes an enhanced particle swarm optimization scheme that improves upon the performance of the standard particle swarm optimization algorithm. The proposed algorithm is based on chaos search to solve the problems of stagnation, which is the problem of being trapped in a local optimum and with the risk of premature convergence. Type 1" constriction is incorporated to help strengthen the stability and quality of convergence, and adaptive learning coefficients are utilized to intensify the exploitation and exploration search characteristics of the algorithm. Several well known benchmark functions are operated to verify the effectiveness of the proposed method. The test performance of the proposed method is compared with those of other popular population-based algorithms in the literature. Simulation results clearly demonstrate that the proposed method exhibits faster convergence, escapes local minima, and avoids premature convergence and stagnation in a high-dimensional problem space. The validity of the proposed PSO algorithm is demonstrated using a fuzzy logic-based maximum power point tracking control model for a standalone solar photovoltaic system.

For multi-objective optimization problems, particle swarm optimization (PSO) algorithm generally needs a large number of fitness evaluations to obtain the Pareto optimal solutions. However, it will become substantially time-consuming when handling computationally expensive fitness functions. In order to save the computational cost, a surrogate-assisted PSO with Pareto active learning is proposed. In real physical space (the objective functions are computationally expensive), PSO is used as an optimizer, and its optimization results are used to construct the surrogate models. In virtual space, objective functions are replaced by the cheaper surrogate models, PSO is viewed as a sampler to produce the candidate solutions. To enhance the quality of candidate solutions, a hybrid mutation sampling method based on the simulated evolution is proposed, which combines the advantage of fast convergence of PSO and implements mutation to increase diversity. Furthermore, ε-Pareto active learning (ε-PAL) method is employed to pre-select candidate solutions to guide PSO in the real physical space. However, little work has considered the method of determining parameter ε. Therefore, a greedy search method is presented to determine the value of ε where the number of active sampling is employed as the evaluation criteria of classification cost. Experimental studies involving application on a number of benchmark test problems and parameter determination for multi-input multi-output least squares support vector machines (MLSSVM) are given, in which the results demonstrate promising performance of the proposed algorithm compared with other representative multi-objective particle swarm optimization (MOPSO) algorithms.

A study was conducted for fuel characterization of a stable nano-oxide added water emulsified biodiesel blend (NWEB) prepared with optimal emulsifying parameters. NWEB prepared with Hydrophilic-lipophilic balance (HLB) value 5.0 indicated the least separation from emulsion layer as compared to other HLB values 4.3 and 6.0. Fourier transformed infrared spectroscopy was conducted in Mid-IR range to ensure the quality of biodiesel and emulsified fuel. The stability data of NWEB prepared with varying the emulsifying parameters were used in radial basis function neural network (RBFNN) for simulation of actual and predicted separation from emulsion. The well trained RBFNN model was coupled with particle swarm optimization (PSO) technique to find the optimal emulsifying parameters to produce a stable NWEB. The predicted optimal emulsifying parameters were found as 69.7 ppm nano-oxide concentration, 10% water, 1% surfactant and 2500 rpm of stirrer for a predicted separation of 0.90% after 30 h of emulsification against actual separation of 0.84%. The fuel properties of NWEB and base fuels were determined following the standard procedures. These properties were found within the limits of American, European and Indian Standards for diesel, biodiesel and biodiesel blends and thus indicating the suitability of these fuels in diesel engine.

Inflatable dams are flexible hydraulic structures that are constructed on rivers and are inflated by fluids such as air or water. This research investigates the effects of influential dimensionless factors on estimating one of the critical hydraulic characteristics of inflatable dams, namely the discharge capacity. Various parameters such as the proportion of total upstream head to dam height (H 1 /D h), the ratio of overflowing head to dam height (h/D h), the ratio of discharge per unit width to its maximum value (q/q max), the ratio of the internal pressure of the tube to its maximum value (p/p max) and the ratio of the longitudinal coordinate placement of each element to x max are used. A hybrid model based on the Particle Swarm Optimization (PSO) and the Genetic Algorithm (GA), PSO-GA, is proposed to improve the accuracy of the estimation by combining the advantages of both algorithms. Moreover, the performance of the model is compared with available hybrid models, including the Artificial Neural Networks (ANNs) optimized by Stochastic Gradient Descent (SGD) model (ANN-SGD) and the ANN-PSO and ANN-GA models. Finally, the performance of the algorithms is evaluated using statistical indicators such as the coefficient of determination (R 2), root mean square error (RMSE), mean absolute percentage error (MAPE) and the scatter index (SI). The results show that the internal pressure plays a vital role with respect to forecasting the discharge coefficient, and omitting it degrades the accuracy by 2.12%. In comparison with other models, the proposed PSO-GA hybrid model provides the most accurate results (R 2 = 0.999, MAPE = 0.04). Finally, comparing the results of the proposed PSO-GA with the benchmarked ANN-GA, ANN-PSO and ANN-SGD methods proves the superiority of the hybrid PSO-GA method.

This paper studies the particle swarm optimization (PSO) with switched topology (SW-TOPO) and its application to the multi-optima problems (M-OPT). Particles converge at multiple optima simultaneously, since SW-TOPO disconnect the transmission of information and separate the topology of the particles. We introduce the switching path length as a basic measure to evaluate the switched topology. Also, applying the proposed PSO to typical benchmark functions of the M-OPT, the algorithm efficiency is investigated.

The aim of this paper is to employ fractional order proportional integral derivative (FO-PID) controller and integer order PID controller to control the position of the levitated object in a magnetic levitation system (MLS), which is inherently nonlinear and unstable system. The proposal is to deploy discrete optimal pole-zero approximation method for realization of digital fractional order controller. An approach of phase shaping by slope cancellation of asymptotic phase plots for zeros and poles within given bandwidth is explored. The controller parameters are tuned using dynamic particle swarm optimization (dPSO) technique. Effectiveness of the proposed control scheme is verified by simulation and experimental results. The performance of realized digital FO-PID controller has been compared with that of the integer order PID controllers. It is observed that effort required in fractional order control is smaller as compared with its integer counterpart for obtaining the same system performance.

This paper presents a novel algorithm named the motion-encoded particle swarm optimization (MPSO) for finding a moving target with unmanned aerial vehicles (UAVs). From the Bayesian theory, the search problem can be converted to the optimization of a cost function that represents the probability of detecting the target. Here, the proposed MPSO is developed to solve that problem by encoding the search trajectory as a series of UAV motion paths evolving over the generation of particles in a PSO algorithm. This motion-encoded approach allows for preserving important properties of the swarm including the cognitive and social coherence, and thus resulting in better solutions. Results from extensive simulations with existing methods show that the proposed MPSO improves the detection performance by 24% and time performance by 4.71 times compared to the original PSO, and moreover, also outperforms other state-of-the-art metaheuristic optimization algorithms including the artificial bee colony (ABC), ant colony optimization (ACO), genetic algorithm (GA), differential evolution (DE), and tree-seed algorithm (TSA) in most search scenarios. Experiments have been conducted with real UAVs in searching for a dynamic target in different scenarios to demonstrate MPSO merits in a practical application.

Since the efficiency of photovoltaic (PV) power is closely related to the weather, many PV enterprises install weather instruments to monitor the working state of the PV power system. With the development of the soft measurement technology, the instrumental method seems obsolete and involves high cost. This paper proposes a novel method for predicting the types of weather based on the PV power data and partial meteorological data. By this method, the weather types are deduced by data analysis, instead of weather instrument. A better fault detection is obtained by using the support vector machines (SVM) and comparing the predicted and the actual weather. The model of the weather prediction is established by a direct SVM for training multiclass predictors. Although SVM is suitable for classification, the classified results depend on the type of the kernel, the parameters of the kernel, and the soft margin coefficient, which are difficult to choose. In this paper, these parameters are optimized by particle swarm optimization (PSO) algorithm in anticipation of good prediction results can be achieved. Prediction results show that this method is feasible and effective.

The volatile wind power generation brings a full spectrum of problems to power system operation and management, ranging from transient system frequency fluctuation to steady state supply and demand balancing issue. In this paper, a novel wind integrated power system day-ahead economic dispatch model, with the consideration of generation and reserve cost is modelled and investigated. The proposed problem is first formulated as a chance constrained stochastic nonlinear programming (CCSNLP), and then transformed into a deterministic nonlinear programming (NLP). To tackle this NLP problem, a three-stage framework consists of particle swarm optimization (PSO), sequential quadratic programming (SQP) and Monte Carlo simulation (MCS) is proposed. The PSO is employed to heuristically search the line power flow limits, which are used by the SQP as constraints to solve the NLP problem. Then the solution from SQP is verified on benchmark system by using MCS. Finally, the verified results are feedback to the PSO as fitness value to update the particles. Simulation study on IEEE 30-bus system with wind power penetration is carried out, and the results demonstrate that the proposed dispatch model could be effectively solved by the proposed three-stage approach.

Minimization of load shedding during contingency conditions is solved as an optimization problem. As a new topic, instead of local load shedding, total load shedding of a large power system is considered. Power generation rescheduling is considered to minimize the load shedding, as well. Different importance factors for buses are also considered. The linear programming method (LP) is used to solve this problem in a short period of time without considering some power system constraints. Particle swarm optimization (PSO) is also used
to solve the problem by considering all power system constraints, but with a longer solving time. Finally, a new method, the sequential use of LP and PSO, is proposed, which is faster than PSO and considers all constraints. The IEEE 14 bus test system is used to compare the performance of the mentioned methods and a comparison of the proposed algorithm and genetic algorithm is accomplished.

Flexible manipulators are very commonly used in industries. In this paper a single-link flexible joint robot is modeled firstly by using Euler–Lagrange energy equation. An optimized Linear Quadratic Regulator is employed to control the manipulator. After that, a Linear Quadratic Regulator (LQR) controller is used for optimal control of the manipulator. For optimizing the LQR, the regulator term weighting of the LQR is achieved by using the newly introduced grey wolf optimizer technique. With the optimized LQR controller based on the proposed performance index, it is tried to have a system with the minimum overshoot and settling time. By considering the proposed performance index and comparing with the PSO-based controller as a popular algorithm, the superiority of the proposed LQR controller in improving the stability and performance of the manipulator is shown. The simulations are performed in MATLAB environment and the results confirm the efficiency of the proposed controller.

We consider the Multi-Area Economic Dispatch problem (MAEDP) in deregulated power system environment for practical multi-area cases with tie line constraints. Our objective is to generate allocation to the power generators in such a manner that the total fuel cost is minimized while all operating constraints are satisfied. This problem is NP-hard. In this paper, we propose Hybrid Particle Swarm Optimization (HGAPSO) to solve MAEDP. The experimental results are reported to show the efficiency of proposed algorithms compared to Particle Swarm Optimization with Time-Varying Acceleration Coefficients (PSO-TVAC) and RCGA.

This paper aims to show better performance of Fractional Order Integral State Feedback (FOISF) controller over Integral State Feedback (ISF) controller with experimental validation in level control of a two tank (non interactive) system. A nonlinear as well as a linear model of the system is obtained using physical laws and experimental open loop response. Considering the linear model an ISF controller has been designed using pole placement technique. FOISF controller is designed considering the nonlinear model using Particle Swarm Optimization (PSO). Proposed controllers are validated in an experimental set up manufactured by Feedback Instruments (Model No 33-041S). Experimental results reveal that FOISF controller provides better performances than that of ISF controller.

This paper proposed a technique to identify Proton Exchange Membrane Fuel Cell (PEMFC)'s parameters using an optimization approach. This paper present the application of particle swarm optimization (PSO) based algorithm known as local optima avoidance particle swarm optimization (LOAPSO) in extracting parameters from PEMFC. The proposed technique is used to identify the PEMFC parameters in term of the voltage-current characteristics. An experimental data from publish literature are used to test and verify the consistency of accurately identifying various parameters.

An optimal artificial neural network (ANN) has been developed to predict the Nusselt number of non-Newtonian nanofluids. The resulting ANN is a multi-layer perceptron with two hidden layers consisting of six and nine neurons, respectively. The tangent sigmoid transfer function is the best for both hidden layers and the linear transfer function is the best transfer function for the output layer. The network was trained by a particle swarm optimization (PSO) algorithm. Nanofluid concentration, Reynolds number, and Prandtl number are input for the ANN and the nanofluid Nusselt number is its output. There exists an excellent agreement between the ANN predicted values and experimental data. The average and maximum differences between experimental data and those predicted by ANN are about 0.8 and 5.6 %, respectively. It was also found that ANN predicts the Nusselt number of nanofluids more accurately than the previously proposed correlation.

Nature is a great source of inspiration for solving complex problems in networks. It helps to find the optimal solution.
Metaheuristic algorithm is one of the nature-inspired algorithm which helps in solving routing problem in networks. The dynamic features, changing of topology frequently and limited bandwidth make the routing, challenging in MANET. Implementation of appropriate routing algorithms leads to the efficient transmission of data in mobile ad hoc networks. The algorithms that are inspired by the principles of naturally-distributed/collective behavior of social colonies have shown excellence in dealing with complex optimization problems. Thus some of the bio-inspired metaheuristic algorithms help to increase the efficiency of routing in ad hoc networks. This survey work presents the overview of bio-inspired metaheuristic algorithms which support the efficiency of routing in mobile ad hoc networks.

In the IT industry, precisely estimate the effort of each software project the development cost and schedule are count for much to the software company. So precisely estimation of man power seems to be getting more important. In the past time, the IT companies estimate the work effort of man power by human experts, using statistics method. However, the outcomes are always unsatisfying the management level. Recently it becomes an interesting topic if computing intelligence techniques can do better in this field. This research uses some computing intelligence techniques, such as Pearson product-moment correlation coefficient method and one-way ANOVA method to select key factors, and K-Means clustering algorithm to do project clustering, to estimate the software project effort. The experimental result show that using computing intelligence techniques to estimate the software project effort can get more precise and more effective estimation than using traditional human experts did.

—PSO is a way of optimizing a function without knowing the function by analyzing social behavior of particles in a swarm. In PSO every particle knows it's neighbor's best and will be move toward it; if the neighbor's best is better than its own best and this effects the velocity vector of a particle. Evaluation and comparison results shows that our algorithm achieves considerably higher average. In this paper, we combine parameters with the encoding space of a particle. By doing this personal best of each particle and finally the global best of whole swarm will improve remarkably.

Initial Solutions (IS) are decisive in meta-heuristics based optimization problems since they impact the performance of the optimization process. This research work proposes and compares some random and deterministic algorithms to create initial solutions based on existing expansion planning criteria to solve the AC Transmission Expansion Planning (TEP) problem. The TEP is formulated as a full non-convex optimization problem using the AC network representation. A local version of the Particle Swarm Optimization (LPSO) technique is employed to solve the TEP problem. The Garver 6-bus and IEEE 24-bus test systems are used to evaluate the IS algorithms performance. It is shown that these algorithms have great potential to improve the robustness and computational effort of meta-heuristics. Index Terms-AC model, electric power systems, transmission expansion planning, initial solutions, least-effort criterion.

Travelling salesman problem (TSP) is a most popular combinatorial routing problem, belongs to the class of NP-hard problems. Many approacheshave been proposed for TSP.Among them, swarm intelligence (SI) algorithms can effectively achieve optimal tours with the minimum lengths and attempt to avoid trapping in local minima points. The transcendence of each SI is depended on the nature of the problem. In our studies, there has been yet no any article, which had compared the performance of SI algorithms for TSP perfectly. In this paper,four common SI algorithms are used to solve TSP, in order to compare the performance of SI algorithms for the TSP problem. These algorithms include genetic algorithm, particle swarm optimization, ant colony optimization, and artificial bee colony. For each SI, the various parameters and operators were tested, and the best values were selected for it. Experiments oversome benchmarks fromTSPLIBshow that artificial bee colony algorithm is the best one among the fourSI-basedmethods to solverouting problems like TSP.

This paper presents the complete modeling and simulation of Wave Energy Conversion System (WECS) driven doubly-fed induction generator with a closed-loop vector control system. Two Pulse Width Modulated voltage source (PWM) converters for both rotor-and stator-side converters have been connected back to back between the rotor terminals and utility grid via common dc link. The closed-loop vector control system is normally controlled by a set of PID controllers which have an important influence on the system dynamic performance. This paper presents a Multi-objective optimal PID controller design of a doubly-fed induction generator (DFIG) wave energy system connected to the electrical grid using Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). PSO and GA are used to optimize the controller parameters of both the rotor and grid-side converters to improve the transient operation of the DFIG wave energy system under a fault condition as compared with the conventional methods to design PID controllers.