Particle Swarm Optimization Population Research Articles

A Hybrid Taguchi Particle Swarm Optimization Algorithm for Reactive Power Optimization of Deep-Water Semi-Submersible Platforms with New Energy Sources

In order to realize the sustainable development of energy, the combination of new energy power generation technology and the traditional offshore platform has excellent research prospects. The access to new energy sources can provide a powerful supplement to the power grid of the offshore platform, but will also create new challenges for the planning, operation, and control of the power grid of the platform; hence, it is very important to optimize the reactive power of the offshore platform with new study, a mathematical model was first built for the reactive power optimization of offshore platform power systems with new energy sources, and the Taguchi method was then used to optimize the parameters and population of particle swarm optimization, thereby addressing a defect in particle swarm optimization, namely, that it can easily fall into local optimal solutions. Finally, the algorithm proposed in this paper was applied to solve the reactive power optimization problem of the offshore platform power system with new energy sources. The experimental results show that the proposed algorithm has stronger optimization ability, reduces the system active power loss to the greatest extent, and improves the voltage quality. These results provide theoretical support for the practical application and optimization of the deep-water semi-submersible production platform integrated with new energy sources.

Applying to aerodynamic optimization an enhanced particle swarm optimization algorithm based on parallel exchange

The particle swarm optimization (PSO) algorithm is easy to implement and can obtain high-quality solutions to optimization problems. It is widely applied to nonlinear and difficult problems such as aerodynamic optimization. However, to solve multi-modal problems, it easily falls into locally optimal values, showing that its robustness is poor. In order to improve the robustness of the PSO algorithm, an enhanced particle swarm optimization algorithm based on parallel exchange (EPSOBPE) is proposed. The algorithm enhances the optimization capability and its robustness through the parallel evolution of the cuckoo search algorithm (CSA), PSO population, hierarchical exchange operation and reinforcement learning strategy. Therefore, the algorithm has both the global search capability of the CSA and the local capability of the PSO algorithm, thus making the EPSOBPE very robust. Functional test results show that the EPSOBPE has stronger robustness and adaptability to different problems than other intelligent optimization algorithms. Moreover, the EPSOBPE is applied to the aerodynamic optimization design of the RAE2822 airfoil and the M6 wing. Compared with other algorithms, the EPSOBPE is more robust, and its optimization capability is better.

Autonomous Surface Vehicle energy-efficient and reward-based path planning using Particle Swarm Optimization and Visibility Graphs

Autonomous Surface Vehicles require path planning to navigate among complex shorelines and spatio-temporal environmental forces such as water currents. Path planning is performed to generate an energy-efficient route to a goal destination based on water current forecasts. Deterministic algorithms such as Dijkstra’s Shortest-Path First are able to generate an optimal solution, but scale exponentially with the size of the search space. In coastal navigation, the complex shorelines and forces require high resolution search spaces such that classical planning algorithms will require substantial time. Metaheuristic algorithms such as Particle Swarm Optimization (PSO) sacrifice guaranteed optimality to substantially reduce computation. However, there is a risk of premature convergence to local optima. After showing PSO struggling to reliably produce near-optimal solutions, we use Visibility Graphs (VGs) to initialize the PSO solution population. We demonstrate that this substantially improves PSO’s ability to reliably achieve solutions that are competitive with Dijkstra. We also introduce the concept of opportunistic reward-based planning, and apply PSO to this planning problem. If a vessel is navigating to a goal destination, we propose taking advantage of nearby sampling opportunities to increase the scientific return of the mission. PSO is used to optimize routes that balance path efficiency and reward. Using a complex assignment of reward values across the search space, there are numerous opportunities for PSO to get stuck in local optima. Again, we use VGs to initialize the PSO population which we demonstrate increases solution consistency while improving both the reward and efficiency. We propose using VGs to generate an initial PSO population and demonstrate that the combination efficiently plans routes for two complex marine path planning problems.

IoD swarms collision avoidance via improved particle swarm optimization

Abstract Drones flights have been investigated widely. In the presence of high density and complex missions, collision avoidance among swarm of drones and with environment obstacles becomes a challenging task and indispensable. This paper aims to enhance the optimality and rapidity of three dimensional IoD path generation by improving the particle swarm optimization (PSO) algorithm. The improvements include using chaos map logic to initialize the population of PSO. Also, adaptive mutation is utilized to balance local and global search. Then, the inactive particles are replaced by new fresh particles to push the solution toward global optimal. Furthermore, Monte Carlo simulation is carried out and the results are compared with slandered PSO and with recent work CIPSO. The results exhibit significant improvement in convergence speed as well as optimal solution which prove the ability of proposed method to generate safety path for IoD formation without collision with terrain obstacle and among drones.

Research on Function Optimization Based on Improved Genetic Particle Swarm Optimization

Aiming at the shortcomings of single algorithm such as blind operation, directionless, long calculation time, low accuracy in Genetic Algorithm and poor diversity of population of Particle Swarm Optimization, which is prone to premature and fall into local optimum. In this study, the serial algorithm fusion idea is adopted, and the population evolved from the Genetic Algorithm is taken as the initial population to be executed by the Particle Swarm Algorithm, namely GAPSO algorithm. In view of the drawbacks of the respective algorithms in the execution of GAPSO algorithm, this paper proposes an Improved Genetic Particle Swarm Optimization algorithm, namely IGAPSO algorithm. In this algorithm, the improved GA only optimizes the initial particle once and then gives it to the improved PSO for optimization. Its optimization method and ability are greatly improved, resulting in a rapid increase in convergence speed. In addition, the improved PSO introduces adaptive inertia weight and learning factor, so that the particles can adaptively adjust global search and local search, and adaptively balance the influence of self-experience and social experience, which greatly avoids falling into local optimization problem and makes the solution more accurate. Through the verification of test functions, GAPSO has obviously improved the calculation accuracy, convergence speed and global stability compared with a single algorithm, while IGAPSO has also improved the convergence accuracy and speed compared with GAPSO, especially in multi-peak functions.

A Novel Hybrid Location Algorithm Based on Chaotic Particle Swarm Optimization for Mobile Position Estimation

Location estimation is significant in mobile and ubiquitous computing systems. Considering the influence of measurement error caused by time difference of arrival (TDOA)/angle of arrival (AOA) hybrid location algorithm and the nonlinear optimization problem encountered in the location estimation, in this paper, a particle swarm optimization (PSO) algorithm based on the chaos theory is proposed for the hybrid location of mobile location estimation. Taking the TDOA/AOA hybrid location algorithm for mobile location estimation as the object, the proposed algorithm greatly improves the location performance and accuracy of mobile location estimation. First, the estimation function of the mobile station is obtained by the maximum likelihood method, and then the initial population of PSO is generated by using the estimation function of the mobile station as a fitness function. The chaotic optimized particle swarm optimization algorithm (CPSO) is used to solve the optimal solution of the optimal position of the population and obtain the optimal mobile location position estimation, which makes the TDOA/AOA location algorithm have better location performance. The simulation results have demonstrated that the performance of the proposed method compared with the traditional Chan algorithm, the Taylor algorithm, and the TDOA/AOA hybrid location algorithm, the proposed algorithm can reduce the impact of error on the location accuracy, achieve a balance of global and local search capabilities, and have a faster convergence speed and more accurate positioning accuracy.

A New Hybrid Method for Parameter Optimization of SVR

The performance of Support Vector Regression (SVR) depends heavily on its parameters, but some optimization methods based on Grid Search (GS) or evolutionary algorithms still have several issues that must be addressed. This paper proposes a new hybrid method (PSO-SS) that combines Particle Swarm Optimization (PSO) and Scatter Search (SS) to optimize the parameters of the SVR. In PSO-SS, to improve the search capability of PSO and reduce the likelihood of the PSO becoming trapped in the local optimum, the initial PSO population is generated by the diversification generation method and the improvement method of SS, and the velocity updating formula of PSO is improved by adding diversity information. On the StatLib and UCI datasets, our experiments show that the PSO-SS method is an effective parameter optimization method compared with other methods. In addition, an SVR model with its parameters optimized by PSO-SS (PSO-SS-SVR) is used to predict the grain size of aluminum alloys. The experimental results show that the PSO-SS-SVR method outperforms Back Propagation Neural Network (BPNN), PSO-SVR and the empirical model.

Managing multi-line power congestion by using Hybrid Nelder–Mead – Fuzzy Adaptive Particle Swarm Optimization (HNM-FAPSO)

This paper presents a Hybrid Nelder–Mead – Fuzzy Adaptive Particle Swarm Optimization (HNM-FAPSO) for a Multi-Line Congestion Management (MLCM) problem. MLCM is a nonlinear optimization problem in deregulated power system. The generators which are sensitive to the congested lines are selected based on the novel Apparent Power Sensitivity Factor (APSF) and are rescheduled by using the HNM-FAPSO algorithm. The objective of hybridizing the Nelder–Mead (NM), Fuzzy adaptive Particle Swarm Optimization (FAPSO) is to blend their unique advantages as well as their efficacy. The NM method is a very efficient local search procedure and is used to initialize the population for Particle Swarm Optimization (PSO) which is used to search global best value. But PSO has a problem of premature convergence due to linear variation of its internal parameter i.e. inertia weight. To overcome this, a fuzzy inference system is used to dynamically update the inertia weight. The feasibility and performance of the HNM-FAPSO algorithm is demonstrated on the standard IEEE 30-bus system and IEEE 118-bus system. A comparison of simulation results and statistical analysis reveal optimization efficacy of the HNM-FAPSO algorithm over other algorithms like FAPSO, NM, Simple Bacterial Foraging (SBF), adaptive bacterial foraging algorithm with Nelder–Mead (ABFNM), Genetic Algorithm (GA) and PSO available in literature. The main outcome of the proposed algorithm in solving MLCM is reduction in congestion cost, rescheduled real power, power loss and computational time.

Comparison across growth kinetic models of alkaline protease production in batch and fed-batch fermentation using hybrid genetic algorithm and particle swarm optimization

The aim of this study was to estimate the kinetic parameters of alkaline protease production with consideration of different growth kinetic models in order to establish the most adequate one to describe the bioprocess dynamics in both batch and fed-batch modes. As a result, a particular method for parameter estimation is developed in this paper. In this method, a hybrid of two metaheuristic techniques, genetic algorithm (GA) and particle swarm optimization (PSO), which takes advantage of both techniques, is applied. In the suggested hybrid algorithm, GA provides the initial population for PSO and then PSO performs the improvement task. The method needs low-intensive computation and proved to be superior to the traditional methods. As a result of applying this method, it was found that the Contois model, in spite of its simplicity, provides a satisfactory agreement with the experimental data, whereas the adoption of more detailed models leads to negligible improvements of the fit. Finally, for comparison of performance of the hybrid algorithm, we used GA only and PSO only. It was shown that the proposed hybrid between meta-heuristics GA and PSO is more effective in terms of running time (two-fold faster) and solution quality, since it benefits from synergy. Also it was concluded that PSO performed slightly better than GA.

Application Mapping onto Butterfly-Fat-Tree based Network-on-Chip using Discrete Particle Swarm Optimization

paper addresses the problem of application mapping onto Butterfly-Fat-Tree (BFT) based Network-on-Chip design. It proposes a new mapping technique based on discrete Particle Swarm Optimization (PSO) to map the cores of the core graph to the routers. The basic PSO has been augmented by running multiple PSO and deterministically generating a part of the initial population for PSO. The mapping results have been compared with well-known techniques reported in the literature for a number of benchmark applications. The reported strategy produces results superior to those obtained via existing approaches within a reasonable CPU time. Keywordsmapping, Network-on-Chip, System-on-Chip, Butterfly-Fat-Tree, Discrete Particle Swarm Optimization

Application Mapping Onto Mesh-Based Network-on-Chip Using Discrete Particle Swarm Optimization

This paper presents a discrete particle swarm optimization (PSO)-based strategy to map applications on both 2-D and 3-D mesh-connected Networks-on-Chip. The basic PSO formulation has been augmented by: 1) running multiple PSOs and 2) deterministically generating a part of the initial population for PSO. The mapping results, in terms of the overall communication metric, have been compared with well-known techniques reported in the literature and also with exact methods built around integer linear programming (ILP). Our PSO-based results are superior to those from reported techniques. For smaller benchmarks, the results obtained are same as those corresponding to the ILP formulation, establishing the quality of the solution strategy.

Improved particle swarm optimization for constrained optimization functions

To overcome the weakness of over-concentration when the population of Particle Swarm Optimization(PSO) is initialized and the search precision of basic PSO is not high,an Improved PSO(IPSO) for constrained optimization problems was proposed.A technique of Good Point Set(GPS) was introduced to distribute the initialized particles evenly and the population with diversity would not fall into the local extremum.Co-evolutionary method was utilized to maintain communication between the two populations;thereby the search accuracy of PSO was increased.The simulation results indicate that,the proposed algorithm obtains the theoretical optimal solutions on the test of five benchmark functions used in the paper and the statistical variances of four of them are 0.The proposed algorithm improves the calculation accuracy and robustness and it can be widely used in the constrained optimization problems.

Reconfiguration of Distribution Systems for Real Power Loss Minimization Using Adaptive Particle Swarm Optimization

This article presents a new method for reconfiguration of radial distribution systems for minimization of real power loss using adaptive particle swarm optimization without involving any additional cost for the installation of tap changing transformers, capacitors, and concerned switching equipment. The initial population for particle swarm optimization is created using a heuristic approach, and the particles are adapted with the help of graph theory to generate feasible individuals. This not only avoids tedious mesh check but also reduces the computational burden. The effectiveness of the proposed method is demonstrated on two different test systems. The application results show that the proposed method is efficient and promising for the minimization of real power loss of radial distribution systems.

The Stop and Go Particle Swarm: A Swarm with a Dynamically Adapting Population Size

In this study, we propose the stop and go particle swarm optimization (PSO) algorithm, a new method to dynamically adapt the PSO population size. Stop and go PSO (SG-PSO) takes advantage of the fact that in practical problems there is a limit to the required accuracy of the optimization result. In SG-PSO, particles are stopped when they have approximately reached the required accuracy. Stopped particles do not consume valuable function evaluations. Still, the information contained in the stopped particles' state is not lost, but rather as the swarm evolves, the particles may become active again, behaving as a memory for the swarm. In addition, as an extension to the SG-PSO algorithm we propose the mixed SG-PSO (MSG-PSO) algorithm. In the MSG-PSO algorithm each particle is given a required accuracy, and through the accuracy settings global search and local search can be balanced. Both SG-PSO and MSG-PSO algorithms are straightforward modifications to the standard PSO algorithm. The SG-PSO algorithm shows strong improvements over the standard PSO algorithm on multimodal benchmark functions from the PSO literature while approximately equivalent results are observed on unimodal benchmark functions. The MSG-PSO algorithm outperforms the standard PSO algorithm on both unimodal and multimodal benchmark functions.