Solution to economic dispatch problem with valve-point loading effect using IPSO algorithm

An improved particle swarm optimization (IPSO) is used to solve economic dispatch problem (EDP). The IPSO has two position updating strategies. In the early stage of iteration, the individual in the population updates the position according to its own best experience with a large probability. In the later stage of iteration, the individual updates the position according to the best experience in the population with a large probability. In addition, the IPSO introduces a mutation operator after position updating, which can increase the diversity of population and prevent the premature convergence. IPSO has been used to solve EDP with valve point effect. The experimental results show that IPSO is an effective algorithm to solve EDP.

In this paper, an effective and reliable variant of particle swarm optimization with time-varying acceleration coefficients (PSO-TVAC) technique is proposed for the economic load dispatch problem considering valve-point loading effect. Main objective of the economic load dispatch (ELD) problem is to minimize the fuel cost by allocating the generation of the committed units subjected to the equality and inequality constraints. Equation of the economic dispatch objective function gets modified with the addition of a new parameter which represents the effect of valve-point loading. While exploring the use of PSO and its other variants to the economic dispatch problem, a number of research works have not considered the transmission losses properly. This paper represents the usefulness of the proposed PSO-TVAC algorithm to significantly reduce the fuel cost while taking into account the effect of transmission losses along with the non-convex characteristic due to valve loading. The results have been demonstrated for three-generator and ten-generator test systems.

This paper deals with the optimal path planning problem for mobile robot which are drove in obstacle environment. Specially, we aim at solving the deadlock problem using grid network and improved particle swarm optimization algorithm. The proposed method is different from traditional particle swarm optimization method, which generally has high-computational complexity and local deadlock. Instead, we utilize the integrated grid network and improved particle swarm optimization algorithm. By the grid network, the long path in the whole workspace with obstacles is divided into multi-segment continuous short paths. The improved particle swarm optimization is used in the short paths by the robot. Then the grids are prioritized and according to the grids priority in the short path, an integrated algorithm is proposed. By applying the proposed algorithm, the mobile robot move to the target along the optimal path without obstacle collision and deadlock. Finally, some experiments are provided to demonstrate the effectiveness of our proposed algorithm.

Application of an improved PSO algorithm to optimal tuning of PID gains for water turbine governor

In this paper, the improved particle swarm optimization (IPSO) algorithm is used to solve large scale reservoir operation optimization problem proposing unconstrained and two constrained versions of this algorithm. In the two constrained versions proposed for the IPSO algorithm, named PCIPSO and FCIPSO, each particle may be forced to satisfy problem constraints during solution building. By considering water releases or storage volumes at each operation time period as decision variable of the problem, here, two formulations are proposed for each version. In the second proposed constrained version algorithm (FCIPSO), at first, the water storage volume bounds are modified in order to recognize the infeasible components of the search space and exclude from the search process before the main search starts. This mechanism leads to smaller search space size for the problem and finally better results. The simple and hydropower operation problems of “Dez” reservoir in the southern Iran over 60, 240 and 480 monthly operations time periods are solved here using both proposed formulations of theses algorithms and the results are presented and compared with other available results. The results show the capability of the proposed algorithms and especially the second constrained version of the IPSO algorithm, FCIPSO, to optimally solve the reservoir operation optimization problem. In other words, the results of both formulations of constrained IPSO and especially FCIPSO algorithm are improved significantly in comparison with unconstrained IPSO algorithm over all operations time periods of simple and hydropower operation of the reservoir.

IRIS flower data is a class of multi variable data set, which is widely applied in data classification. This paper aims at the parameter optimization problem of least squares support vector machine (LS-SVM) in data classification, an improved particle swarm optimization(IMPSO) algorithm is introduced into the LS-SVM model for improving the learning performance and generalization ability of LS-SVM model. A new data classification method based on IMPSO algorithm and LS-SVM (IMPSO-LS-SVM) model is proposed. First, the numbers of current iteration and population are added into the control strategy of adaptive adjustment inertia weight in order to improve the performance of inertia weight of PSO algorithm. Then the IMPSO algorithm is used to search the optimal combination values of the parameters of kernel function for obtaining the IMPSO-LS-SVM. Finally, the training samples are used to comprehensively train the IMPSO-LS-SVM, and the best large-scale data classification model is constructed. The IRIS flower data is used to validate the effectiveness of the IMPSO-LS-SVM model. The result indicates that the IMPSO algorithm can effectively search the optimal combination values of the parameters, and the proposed data classification model has better generalization performance, faster training speed and higher classification precision.

Ultra-high performance concrete has excellent mechanical properties such as ultra-high strength and high durability, and has a broad application prospect in the field of bridge engineering. To make full use of the superior mechanical properties of UHPC and reduce the cost of the bridge, the particle swarm optimization algorithm is used to optimize the structural design of the UHPC beam bridge; Aiming at the problem that the traditional particle swarm optimization algorithm is easy to fall into local optimum, the nonlinear adaptive weight update method is used to improve the particle swarm optimization algorithm. Based on the above-mentioned improved particle swarm optimization algorithm, the optimal design method of ordinary reinforced UHPC beams and prestressed UHPC beams with commonly used highway spans is studied. The reliability of the optimized structural limit state is analyzed by Monte Carlo (MC) importance sampling method. The results show that compared with the traditional particle swarm optimization algorithm, the improved particle swarm optimization algorithm has higher convergence speed and convergence accuracy. The optimal height-span ratio of ordinary reinforced UHPC beams decreases with the increase of the span, and the construction cost gradually increases; The optimal height-span ratio of the prestressed beam decreases first and then increases with the increase of the span, and the construction cost gradually increases. The calculation results of load capacity reliability indexes of optimization results are all higher than the target reliability indexes of similar components stipulated in China’s “Uniform Standard for Structural Reliability Design of Highway Engineering”.

The neural network data are usually characterized by abruptness, nonlinearity and time variability, and thus, it is difficult to yield accuracy results of network traffic prediction based on a traditional radial basis function neural network that has the shortcomings of slow convergence and easily falling to local optimum. To improve the accuracy of network traffic prediction and optimize the method of parameter and structure setting for a neural network, a model of radial basis function neural network with improved particle swarm optimization algorithm is proposed by referring to the related theories of network traffic and phase space reconstruction. The improved particle swarm optimization algorithm can adjust the inertia weight and the learning factors, and make $$t$$ -distribution mutation of particles’ positions via global extremum to avoid local convergence and thereby improving its global searching capacity; with such an algorithm, the parameters of radial basis function neural network are optimized; then, in order to verify the algorithm’s effectiveness, the radial basis function neural network is trained to become an optimal prediction model, which is adopted for the prediction of two typical chaotic time series and the real network traffic. It is then compared with the traditional radial basis function neural network model and the radial basis function prediction model by improved particle swarm optimization; and the simulations result shows that the application of this model improves the accuracy of network traffic prediction, and demonstrates the algorithm’s feasibility and effectiveness for network traffic prediction.

Economic dispatch problem (EDP) is an important optimization problem in modern power system. When involved with valve-point loading effect, this problem becomes a complex non-convex and non-linear optimization problem. In this case, many researchers adopt evolutionary algorithms to solve it. An equality constraint, the power balance constraint, exists in this problem. This constraint leads some common constraint handling methods, such as feasibility rules and e constraint method, to failure. In this paper, we propose a new constraint handling method which converts the power balance constraint into two boundary inequality constraints, and apply differential evolution (DE) combined with feasibility rules and e constraint method to solve this problem. In order to prove its effectiveness, three benchmarks are tested. The numerical results show that with the help of the new transforming method, feasibility rules and e constraint method perform very well in the three benchmarks. Compared with other algorithms in literatures, the simplest DE combined with e constraint method also demonstrates competitiveness.

The economic load dispatch problem is a very important practical problem in electric power systems operation, where an efficient solution can lead to cost and emissions reductions. The use of metaheuristics, as the Firefly Algorithm, for the solution of economic load dispatch problems has been shown to be a more appropriate alternative than classical optimisation methods. In this work, we have proposed a new variant for the Firefly Algorithm, considering a non-homogeneous population. This new variant was validated using ten benchmark functions, and then applied to solve a 15-units economic load dispatch problem, considering transmission line losses, power limits, ramp limits and prohibited zones for each generator, and a 13-units non-convex system with valve-point loading effect in the cost function and power generation limits. The results show that the proposed non-homogeneous variant is able to reach better solutions than the original Firefly Algorithm, using both penalty and non-penalty power balance constraint handling methods. In general, the non-penalty approach reached better results than the penalty method. Moreover, the new variant presented relatively competitive results for the economic load dispatch problems considered, when compared to the best results found in the literature.

It is of great significance to study how to use intelligent algorithm to optimize the scheduling of microgrid, so as to improve the operation efficiency of microgrid. In this paper, particle swarm optimization(PSO) algorithm is improved by nonlinearly decreasing inertia weight and nonlinearly dynamic adjusting learning factors. Compared with PSO, the global search ability of the improved particle swarm optimization(IPSO) algorithm is improved. IPSO is used to optimize the scheduling of the microgrid grid connection model. The example results show that nonlinearly decreasing inertia weight and nonlinearly dynamic adjusting learning factors can obviously improve PSO. IPSO has faster convergence and lower comprehensive cost of microgrid scheduling. This paper provides an effective method for the day ahead economic scheduling of microgrid.

With the rapid increase of the number of Internet users and the amount of online comment data, a large number of referable information samples are provided for data mining technology. As a technical application of data mining, text sentiment classification can be widely used in public opinion management, marketing, and other fields. In this study, a combination approach to SVM (support vector machine) and IPSO (improved particle swarm optimization) is proposed to classify sentiment by using text data. First, the text data of 30,000 goods reviews and corresponding ratings are collected through the web crawler. Then, TFIDF (term frequency-inverse document frequency) and Word2vec are used to vectorize the goods review text data. Next, the proposed classification model is trained by the SVM, and the initial parameters of the SVM are optimized by the IPSO. Finally, we applied the trained SVM-IPSO model to the test set and evaluated the performance by several measures. Our experiment results indicate that the proposed model performed the best for text data sentiment classification. Additionally, the traditional machine learning model SVM becomes very effective after parameter optimization, which demonstrates that the parameters’ optimization by IPSO has successfully improved the classification accuracy. Furthermore, our proposed model SVM-IPSO significantly outperforms other benchmark models, indicating that it could be applied to improve the accuracy and efficiency for text data sentiment classification.

The sensor nodes of wireless sensor networks (WSNs) are deployed to an open and unsupervised region, and they are vulnerable to various types of attacks. Intrusion detection system can detect network attacks that nodes suffer from. This paper combines improved particle swarm optimisation (IPSO) algorithm and back-propagation neural network (BPNN), named IPSO-BPNN. We propose an intrusion detection model of WSNs based on a hierarchical structure. First, we use IPSO algorithm to optimise the initial parameters of BPNN to avoid falling into the local optimum. Then, we apply IPSO-BPNN to the intrusion detection of WSNs. Finally, we use benchmark NSL-KDD and UNSW-NB15 datasets to verify the performance of the IPSO-BPNN. The simulation results show that IPSO-BPNN has faster convergence speed, higher detection accuracy rate and lower false positive rate compared with BPNN and BPNN optimised by PSO algorithm, which can meet the WSNs intrusion detection requirements.

Resource leveling problem is among the top challenges in project management. Traditional heuristic methods and exact algorithms based upon enumeration, branch-and-bound, integer programming or dynamic programmings usually face great difficulties for large and complex projects. In this paper, an improved particle swarm optimization (IPSO) algorithm is presented to solve this problem. Firstly, a mapping is created between the feasible schedule and the position of the particle, then the IPSO begin to search the global best and the local best until the stop criteria is satisfied. A case study is presented and a comparison is made between IPSO and some traditional heuristic methods. Results show that the IPSO algorithm is more satisfying than those of the heuristic methods in terms of feasibility and efficiency. Therefore, this method has its practical application value for the resource leveling problem of project management.

In past two decades solution to the Dynamic Economic Load Dispatch (DELD) problem considering emission and transmission losses along with Valve Point Loading (VPL) effect has been investigated by researchers using several meta-heuristic techniques. Numerical techniques such as lambda iteration method, dynamic programming etc. have been unable to solve the ELD problem with VPL effect. This paper presents a systematic performance evaluation of meta-heuristic techniques named as Particle Swarm Optimization and the Genetic Algorithm for combined economic emission dispatch problem considering Valve Point Loading (VPL) to meet 24 hr. load profile on IEEE 9 bus test case with 3 generator units.