A High-Dimensional Model Representation Guided PSO Methodology With Application on Compressor Airfoil Shape Optimization

In this paper, a hybrid algorithm based on Particle Swarm Optimization (PSO) and Cuckoo Search (CS) is detailed with the application of Optimal Reactive Power Dispatch (ORPD). The Levy flight and the elimination mechanism are introduced to the traditional PSO algorithm. Benchmark tests show that the hybrid algorithm has better optimization efficiency than original PSO and CS algorithms. In the application of the ORPD, the fitness function is the total real loss of the distribution network. Inequality constrains are handled by the limitation of searching space. The simulation results on the IEEE-14 bus standard system and a real distribution network illustrate the feasibility and effectiveness of the algorithm. The total real power losses decrease 7.1% and 8% in two applications. The mathematical tests and engineering applications demonstrate that the hybrid algorithm has better searching ability than the standard PSO algorithm, and it is suitable for the solution of ORPD problem.

The particle swarm optimization (PSO) algorithm presents a new way for finding optimal solutions of complex optimization problems. In this paper a modified particle swarm optimization algorithm is presented. We modify the PSO algorithm in some aspects. Firstly, a contractive factor is introduced to the position update equation, and the particles are limited in search region. A new strategy for updating velocity is then adopted, in which the velocity is weakened linearly. Thirdly, using an idea of intersecting two modified PSO algorithms. Finally, adding an item of integral control in the modified algorithm can improve its global search ability. Based on these strategies, we proposed a new PSO algorithm named crossed PSO algorithm. Simulation results show that the crossed PSO is superior to the original PSO algorithm and can get overall promising performance over a wide range of problems.

The hybrid energy system design problem needs efficacy tools to reach optimal results in remote areas. The metaheuristic optimization methods are the best choice to address complex problems. This article presents an improved approach based on an energy management strategy for optimal sizing and configuration of standalone photovoltaic scheme components. Improved particle swarm optimization for optimization and configuration of photovoltaic panel and battery system is applied using MATLAB and hourly solar radiation, ambient temperature data, and load demand. The objective of the optimization problem is to define the optimal sizing of the system components to meet the load demand to compose a cost‐effective and reliable scheme. The optimized system results from the improved approach are compared by original particle swarm optimization and simulated annealing algorithms. So, the optimum design of the hybrid scheme is analyzed and compared based on different reliability values. The results show that the improved approach finds the optimal results easily with lower cost, faster convergence, and better reliability indexes in different reliability indexes in comparison with the other approaches. In this regard, for different reliability values (1%, 3%, and 5%), the improved particle swarm optimization shows approximately 22.9% cost saving in comparison with the simulated annealing, and improved particle swarm optimization shows approximately 0.35% cost saving in comparison with the particle swarm optimization. Also, show the reliability of the standalone hybrid photovoltaic system.

Influenced by the unbalanced state of particle swarm in the process of fuel combustion in thermal power plants, the fuel cost in the thermal power generation stage is relatively high. Therefore, a new particle swarm optimization model for fuel combination in thermal power plants is proposed. Combined with the combustion properties of different fuels, from the point of view of particle swarm optimization, in the process of carrying out specific particle swarm optimization simulation, the original particle swarm optimization algorithm is improved adaptively. A new particle swarm optimization algorithm is constructed by coupling it with a multiphase turbulence model. The fuel combustion performance of the power plant is analyzed by using its coupling. The optimization model of fuel combination in the thermal power plant is constructed by maximizing the energy release of fuel as the core, and the state of the fluid-particle field is taken as the constraint condition. In the test results, the design of the fuel combination optimization model can fully improve the energy release degree of fuel and reduce fuel consumption under the same power generation demand, which has a positive effect on power generation cost control.

The particle swarm optimization (PSO) algorithm is an intelligent optimization algorithm and has been successfully applied to many fields. However, it has the deficiencies of the poor search capability and the low calculation efficiency. To solve the problems, the genetic algorithm is introduced to improve the PSO algorithm in the paper. Then, three simulated examples were used to assess the ability of the improved PSO algorithm. The results showed that the improved PSO algorithm could jump out of the local minimum and achieve the better solution. Furthermore, the paper reveals that the improved PSO algorithm seems to be an improvement on the original PSO algorithm.

PSOG: Enhanced particle swarm optimization by a unit vector of first and second order gradient directions

Premature convergence, the major problem that confronts evolutionary algorithms, is also encountered with the Particle Swarm Optimization (PSO) algorithm. In the previous work [11], [12], [13], the Quantum-behaved Particle Swarm (QPSO) is proposed. This novel algorithm is a global-convergence-guaranteed and has a better search ability than the original PSO. But like other evolutionary optimization technique, premature in the QPSO is also inevitable. In this paper, we propose a method of controlling the diversity to enable particles to escape the sub-optima more easily. Before describing the new method, we first introduce the origin and development of the PSO and QPSO. The Diversity-Controlled QPSO, along with the PSO and QPSO is tested on several benchmark functions for performance comparison. The experiment results testify that the DCQPSO outperforms the PSO and QPSO.KeywordsParticle Swarm OptimizationParticle SwarmEvolutionary ComputationParticle Swarm Optimization AlgorithmPremature ConvergenceThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Evolutionary optimization algorithms, including particle swarm optimization (PSO), have been successfully applied in oil industry for production planning and control. Such optimization studies are quite challenging due to large number of decision variables, production scenarios, and subsurface uncertainties. In this work, multi-stage, multi-swarm PSO (MS2PSO) algorithm is proposed to fix certain issues with canonical PSO algorithm such as premature convergence, excessive influence of global best solution, and oscillation. Multiple experiments are conducted using Olympus benchmark to compare the efficacy of algorithms. Results from canonical PSO are first compared with two PSO variations in which hyperparameters are tuned to prioritize exploration in early phase and exploitation in late phase. Firstly, linearly decreasing inertia weight (LDIW-PSO) is used to have greater weight of current particle position during initial iterations, and vice versa. Then, time-varying acceleration coefficients (TVAC-PSO) are used to have greater weight of personal best and lesser weight of global best during the initial iterations, and vice versa. Next, a two-stage multi-swarm PSO (2SPSO) is used where multiple-swarms of the first stage collapse into a single swarm in the second stage. Finally, MS2PSO with multiple stages and multiple swarms is used in which swarms recursively collapse after each stage. Multiple swarm strategy ensures that diversity is retained within the population and multiple modes are explored. Staging ensures that local optima found during initial stage does not lead to premature convergence. Optimization test case comprises of 90 control variables of which 72 are well control related and 18 are well placement related. Swarm intelligence refers to global patterns emerging from simple interactions among population. Algorithmic rules at micro level lead to social interaction at meso level, which then further leads to collective behavior at macro level. It is observed that different algorithm designs have their own benefits and drawbacks. Decreasing inertia weight in LDIW-PSO enables exploration in early stages and convergence around global best in the later stages. TVAC-PSO on the other hand restricts social learning and aids exploration in the early iterations. Social learning component is increased as run progresses, and population moves towards global best. 2SPSO aids in exploring multi-modal objective space, thus preventing premature convergence to a local optima. Swarms collapse into one group in the second stage, and run finally converges towards global best. Multiple swarms and stages in MS2PSO ensure that diversity in population is maintained throughout the run which enables continuous learning, and thus mitigates premature convergence. Both 2SPSO and MS2PSO are found to be helpful for problems with high dimensions and multiple modes where greater degree of exploration is desired. Commercial cloud computing and parallel programming were used to handle high computational workload and reduce run-time from weeks to days. Coefficients of canonical PSO are tuned in LDIW-PSO and TVAC-PSO, which helps in mitigating issues like premature convergence and oscillation. Two-stage PSO (2SPSO) where multiple swarms of first stage collapse into one in second stage, and multi-stage multi-swarm PSO (MS2PSO) where swarms recursively collapse into one are proposed. These algorithms modify the social behavior at meso scale based on number of swarms, number of stages and iterations in each stage.

Parallel Global Best-Worst Particle Swarm Optimization Algorithm for solving optimization problems

Oil is one of the most precious source of energy for the world and has an important role in the global economy. Therefore, the long-term prediction of the crude oil price is an important issue in economy and industry especially in recent years. The purpose of this paper is introducing a new Particle Swarm Optimization (PSO) algorithm to forecast the oil prices. Indeed, the PSO is a population-based optimization method inspired by the flocking behavior of birds. Its original version suffers from tripping in local minima. Here, the PSO is enhanced utilizing a convergence operator, an adaptive inertia weight and linear acceleration coefficients. The numerical results of mathematical test functions, obtained by the proposed algorithm and other variants of the PSO elucidate that this new approach operates competently in terms of the convergence speed, global optimality and solution accuracy. Furthermore, the effective variables on the long-term crude oil price are regarded and utilized as input data to the algorithm. The objective function of the optimization process considered in this research study is the summation of the square of the difference between the actual and the predicted oil prices. Finally, the long-term crude oil prices are accurately forecasted by the proposed strategy which proves its reliability and competence.

Neural Network system for SoC is one topic in the world. Recently, SoC began to combine AI technique by Python. When developers consider Intelligent system on SoC, the co-design software and hardware are significant. In this case, small devices and embedded system are target system. For IoT, those techniques will grow up in the world. Those cases, avoiding premature convergence while keeping performance is a challenge in training the neural network, especially in a case of large NNs or a large number of training data. In this case, one problem is a huge memory and processing power. For tiny memory and processing system as SoC, we propose an improved particle swarm optimization (PSO) algorithm called the PSOseed2 algorithm for training NN. The PSOseed2 algorithm solves the premature convergence of the standard PSO (SPSO) algorithm by slightly modifying the velocity update function without adding many computational tasks to the SPSO algorithm. We evaluated this algorithm on field programmable gate array (FPGA)-based NN and software-based NN and trained these NNs with different PSO algorithms that are SPSO, PSOseed, PSOseed2, and dissipative PSO. Experimental results with different datasets confirmed that the NNs trained by the proposed PSOseed2 algorithm had better recognition rates and lower global learning errors than the NN trained by other PSO algorithms. In this talk, I would like to talk those relational research in the world and our results and discussion.

We consider a population based Particle Swarm Optimization (PSO) algorithm and a few modifications to increase quality of optimization. Several strategies are investigated to exchange data between processors in parallel algorithm. Experimental investigation is performed on Multiple Gravity Assist problem. The results are compared with original PSO.

Collaborative beamforming in wireless sensor networks using a novel particle swarm optimization algorithm variant

In our previous research applied to the job shop scheduling problem (JSSP) for machine tool production, the multi-objective optimisation model based on the particle swarm optimization (PSO) research had an imbalance performance between the convergence rate and the convergence precision. In this article, a new hybrid algorithm using PSO and generic algorithm (GA) is proposed to solve this particular problem. In this new algorithm, named hybrid PSO-GA algorithm (HPGA), the PSO algorithm is redefined and modified by introducing genetic operators, i.e. the crossover operator and the mutation operator, to update the particles in the population. The HPGA is then applied in heavy machinery company with minimising machines’ makespan and minimising jobs’ tardiness as the two optimal objectives. The comparisons with actual application report have illustrated that the proposed HPGA can obtain higher quality of schedule solution for machine tool production. Furthermore, with solution quality and convergence rate as the two estimation measurements metrics, some comparisons are performed in order to illustrate that the HPGA has superiority over the PSO, GA and simulated annealing algorithm (SA). Results have indicated that, with the combination of the merits of PSO and GA, the proposed HPGA approach can achieve not only better solution quality but also faster convergence rate than the PSO, GA and SA, within a reasonable computation time for high dimensions JSSP.

This paper proposes a motion trajectory control method of underground intelligent scraper based on particle swarm optimization. The simulation model of whole system was established in simulink according to autonomous navigation bivariate PID control algorithm and scraper motion control model. The process of particle swarm optimization was designed based on MATLAB platform. The parameter optimization model of PID controller based on particle swarm optimization (PSO) algorithm is established. The parameters of PID controller are optimized, and the global position optimal solution is obtained. It obtained correlation curve of motion trajectory deviation through the experiment of motion trajectory of particle swarm optimization. The particle swarm optimization algorithm is proved to be very effective in controlling the trajectory of the intelligent scraper.