Constrained Particle Swarm Optimization Research Articles

Energy management strategy for low hydrogen consumption in hybrid power systems consisting of dual fuel cells and a single lithium battery

ABSTRACT Low hydrogen consumption is an important objective for fuel cell power systems as it impacts both economic efficiency and continuous power supply. This paper addresses the optimization problem of minimizing hydrogen consumption in a hybrid power system comprising dual fuel cells and a single lithium battery. We introduce an energy management method for the multi-period synchronous optimization, incorporating a data processing module and power allocation module. The quasi-periodic loads are predicted using a modified gray-Markov chain model, while the optimization problem is solved using an adaptive constrained particle swarm optimization (ACPSO) algorithm. An initial swarm generation algorithm is introduced to enhance the effectiveness of ACPSO, and a power correction algorithm is integrated to address deviations between predicted and actual loads. The effectiveness and advantages in optimizing hydrogen consumption of the proposed strategy have been demonstrated through simulations and experiments.

Effects of dispatch algorithms and fuel variation on techno-economic performance of hybrid energy system in remote island

A stand-alone hybrid energy system is needed to mitigate the growing demand for future energy and to reduce emissions generated by conventional fuel sources. This work shows and proposes an optimum hybrid energy system configuration with less environmental pollution for a coastal region of Bangladesh with a load demand of 292.20 kWh/d and a peak load of 41.14 kW. This research presents the cost of energy (COE) and net present cost (NPC) for a PV-wind hybrid system with five alternative fuel generator technologies. For each possible configuration, the effects of three alternative dispatch algorithms, load following (LF), cycle charging (CC), and combined dispatch (CD), are examined. All three algorithms were validated using a genetic algorithm (GA), Cuckoo search algorithm (CUSA), Constrained Particle Swarm Optimization (CPSO), Harmony search algorithm (HSA), and Non-dominated Sorting Genetic Algorithm (NSGA-II). Furthermore, a sensitivity analysis is also conducted by utilizing the gasoline price, discount rate, battery cost, PV cost, and inflation rate. The results show that the PV-wind-natural gas-based system provides the minimum COE (0.196 USD/kWh) and NPC (270,483 USD) when the CD algorithm is followed. For the same optimal hybrid energy system, the COE is 0.201 USD/kWh, 0.0998 USD/kWh, 0.101 USD/kWh, 0.101 USD/kWh, and 0.0987 USD/kWh respectively for GA, CUSA, CPSO, HSA, and NSGA-II. However, COE increases by 8 % (0.212 USD/kWh) and 15 % (0.225 USD/kWh) when the CC and LF algorithms are followed. In addition, a comparison of all configurations reveals that the PV-wind-biomass configuration with the CD algorithm excludes biomass generators to lower the COE, making the system emission-free.

An adaptive operating parameters decision-making method for shield machine considering geological environment

The performance of shield machine is sensitive to geological conditions and its operation depends on driver experience. Based on D-ResNet network and improved inertia weight adaptive constrained particle swarm optimization algorithm (IWACPSO), this study proposes an adaptive operating parameters decision-making method for shield machine considering geological environment. First, a novel D-ResNet network is proposed to establish the mapping models between geological type, operating parameters and tunneling speed, cutterhead torque, respectively. Then, we design the evaluation indicators of tunneling performance considering construction efficiency and considering both construction efficiency and energy consumption, respectively, and build the mapping model between operating parameters and performance evaluation indicators. On this basis, the optimization equation with operation parameters as control variables and improving tunneling performance as the goal is established. In order to solve the decision results of optimal operating parameters under various geological types, we present an enhanced inertia weight adaptive constrained particle swarm optimization algorithm. Finally, the proposed D-ResNet and IWACPSO-based decision-making method are verified by the actual construction data of subway project in Singapore. The results show that the R2 values of the proposed D-ResNet network for predicting the tunneling speed and cutterhead torque reach 0.98 and 0.96, respectively, which are much better than AdaBoost, support vector regression, multiple linear regression and deep neural network. The IWACPSO-based decision-making method could accurately solve the optimal solution of operating parameters, and the optimization time is much lower than that of ant colony algorithm, genetic algorithm and exhaustive method. Moreover, operation parameter decision-making method considering construction efficiency increased the tunneling speed by 61.02% on average, and the operation parameter decision-making method considering construction efficiency and energy consumption increased the tunneling speed by 59.55% on average, while the tunneling specific energy decreased by 20.50% on average compared with the former.

Taguchi regression analysis and constrained particle swarm optimization for amended unconfined compressive strength (UCS) of expansive subgrade soil

Taguchi regression analysis and constrained particle swarm optimization for amended unconfined compressive strength (UCS) of expansive subgrade soil

Contact-based object inspection with mobile manipulators at near-optimal base locations

This paper presents a control and motion planning algorithm for a mobile vehicle-manipulator system such that the mobile vehicle and the manipulator mounted on it work in harmony to inspect unknown objects. Forward Dynamic Control method is used for the manipulator to accomplish a stable interaction with the environment and constrained particle swarm optimization is applied so that the vehicle can be localized at the estimated points maximizing the dexterity of the manipulator. Quartic splines are implemented to generate a smooth path for the vehicle in between the optimal locations. The proposed architecture is validated via an experimental setup consisting of a robotic arm with a force sensor at its end-effector mounted on a parallel manipulator. These experiments emulate an underwater vehicle-manipulator system, where the mobile base is subject to disturbances due to the physical interaction of the end-effector with the environment, typically a pipe. The advantage of the proposed approach is that it allows continuous and smooth movement of the base in harmony with the robotic manipulator while executing a task on a large surface (larger than the manipulator workspace can cover from a fixed position) and maintains a high level of dexterity index for the manipulator.

Image Reconstruction Algorithm for Electrical Capacitance Tomography Based on Constrained Particle Swarm Optimization

When the particle swarm is active, the robustness of the system is quite great, which is very useful for solving ill-conditioned problems like picture reconstruction. In the reconstructed image, however, the large number of pixels results in the large dimension of particles, making it harder for particles to attain the ideal solution during the optimization process. A constraint condition is applied to the position of particles to solve this problem. The image reconstruction algorithm regularized by Tikhonov is used as the particle position reference to constrain the particles to search within a certain range of the algorithm, and the penalty function is used to solve the problem to improve the particle search speed. For the inertia weight of particle swarm, this paper uses linear decreasing weight to realize its adaptive dynamic adjustment, which improves the flexibility of the algorithm. When chaos operator is added to the particle swarm location search process, if the particle is trapped in a local optimum, the chaos variable will fluctuate within a certain range, thus reducing the error rate of the optimal solution. The experimental comparison results illustrate that the modified particle swarm optimization technique for the image reconstruction of Electrical Capacitance Tomography outperforms the classic LBP and Tikhonov algorithms in terms of accuracy and efficiency.

Joint Sequence Optimization-Based OFDM Waveform Design for Integrated Radar and Communication Systems

Orthogonal frequency division multiplexing (OFDM) is often used in integrated radar and communication (IRC) systems to improve the sharing of scarce spectrum resources and develop efficient solutions for intelligent vehicles. However, the communication symbols and pilot information carried by the integrated signal and the cyclic prefix of OFDM greatly limit the performance of the radar and communication. In this paper, we propose a joint sequence optimization-based OFDM waveform design method for IRC. First, the ambiguity function of the OFDM signal block and guard interval (GI) sequence are deduced in detail, and a new composite sequence with the cascading of Walsh code and m sequence is used to optimize the correlation of communication information, resulting in better ambiguity function characteristics. Then, a multivariate optimization model is explored, and the constrained particle swarm optimization algorithm based on weight allocation (WCPSO) is proposed to obtain the optimal GI sequence. It has lower sidelobe levels and higher information transmission efficiency. In addition, an improved received signal processing method is introduced to achieve stable communication synchronization and high-resolution distance and velocity estimation of multiple targets radar detection. Finally, numerical simulation results are presented to validate the effectiveness of the proposed method.

Data preprocessing strategy in constructing convolutional neural network classifier based on constrained particle swarm optimization with fuzzy penalty function

Convolutional neural networks (CNNs) have attracted increasing attention in recent years because of their powerful abilities to extract and represent spatial/temporal information. However, for general data, its features are assumed to have weak or no correlation, and directly applying CNN to classify such data could result in poor classification performance. To address this problem, a combined technique of original data representation method of fuzzy penalty function-based constrained particle swarm optimization (FCPSO) and CNN, so-called FCPSO-CNN is designed to effectively solve the classification problems for generic dataset and applied to recognize (classify) black plastic wastes in recycling problems. In more detail, CPSO is introduced to optimize feature reordering matrix under constraints and the construction of this matrix is driven by fitness function of CNN that quantifies classification performance. The Mamdani type fuzzy inference system (FIS) is employed to realize the fuzzy penalty function (FPF) which is utilized to realize the constrained problems of CPSO as well as alleviate the issues of the original penalty function method suffering from the lack of robustness. Experimental results demonstrate that FCPSO-CNN achieves the best classification accuracy on 13 out of 17 datasets; the statistical analysis also confirms the superiority of FCPSO-CNN. An interesting point is worth to mention that some feature reordering matrices in the infeasible space come with better classification accuracy. It has been found that the proposed method results in more accurate solution than one-dimensional CNN, random reordering feature-based CNN and some well-known classifiers (e.g., Naive Bayes, Multilayer perceptron, Support vector machine).

Design of a prediction system based on the dynamical feed-forward neural network

Analysis and prediction of time series play a significant role in scientific fields of meteorology, epidemiology, and economy. Efficient and accurate prediction of signals can give an early detection of abnormal variations, provide guidance on preparing a timely response and avoid presumably adverse impacts. In this paper, a prediction system is designed based on the dynamical feed-forward neural network. The trajectory information in the reconstructed phase space, which is topologically equivalent to the dynamical evolution of the system, is applied to establish the prediction model. Moreover, an integer constrained particle swarm optimization algorithm is employed to select the optimal time delay, which is the parameter of our system. Simulation results for applications on the Lorenz system, stock market index, and influenza data indicate that our proposed method can produce efficient and reliable predictions.

Manipulation at optimum locations for maximum force transmission with mobile robots under environmental disturbances

Remote manipulation plays a key role for applications in hazardous conditions, yet designing a robust controller enabling safe interaction with unknown environment and under the influence of disturbances is a challenge. In this study, we propose effective control and optimization methods for mobile robotic manipulator systems that can increase effort transmission to a task in desired directions. The vehicle position is optimized by utilizing constrained particle swarm optimization where the objective is to enhance directional manipulability of the robotic arm within the system. A forward dynamic controller is implemented to eliminate undesired excessive motions near singular joint configurations. A reset control algorithm along with an admittance type controller are developed for stable interaction with an unknown object under environmental disturbances. The experimentally validated results show that the proposed method phase out undesired position disturbances and increase the directional manipulability for the required task enabling augmented effort transmission for the task execution.

Identification of Multiple Fire Sources in the Utility Tunnel Based on a Constrained Particle Swarm Optimization Algorithm

Identification of Multiple Fire Sources in the Utility Tunnel Based on a Constrained Particle Swarm Optimization Algorithm

Constrained self regulating particle swarm optimization

Self regulating particle swarm optimization (SRPSO) is a variant of particle swarm optimization (PSO) which has proved to be a very efficient algorithm for unconstrained optimization compared with other evolutionary algorithms (EAs) and utilized recently by the researchers for solving real-world problems. However, SRPSO has not been evaluated and analyzed for constrained optimization. In this work, SRPSO has been evaluated exhaustively for constrained optimization using the 24 constrained benchmark problems by coupling it with four efficient constraint handling techniques (CHTs). The results of constrained SRPSO algorithm have been compared with two other algorithms i.e. Differential evolution (DE) and PSO. DE and PSO have also been coupled with same four CHTs and evaluated on the 24 constrained benchmark problems. Statistical analysis on performance evaluation of three algorithms on the benchmark problems shows that constrained SRPSO algorithm performance is better than constrained PSO but it is found to be deficient when compared with constrained DE with 95% confidence level. Therefore, the objective of this work is to evaluate the SRPSO algorithm comprehensively for constrained optimization with different views to come up with suitability of constrained SRPSO algorithm when coupled with particular CHT for solving specific type of problems.

Aircraft Propeller Design through Constrained Aero-Structural Particle Swarm Optimization

An aero-structural algorithm to reduce the energy consumption of a propeller-driven aircraft is developed through a propeller design method coupled with a Particle Swarm Optimization (PSO). A wide range of propeller parameters is considered in the optimization, including the geometry of the airfoil at each propeller section. The propeller performance prediction tool employs a convergence improved Blade Element Momentum Theory fed by airfoil aerodynamic characteristics obtained from XFOIL and a validated OpenFOAM. A stall angle correction is estimated from experimental NACA 4-digits data and employed where convergence issues emerge. The aerodynamic data are corrected to account for compressibility, three-dimensional, viscous, and Reynolds number effects. The coefficients for the rotational corrections are proposed from experimental data fitting. A structural model based on Euler-Bernoulli beam theory is employed and validated against Finite Element Analysis, while the impact of centrifugal forces is discussed. A case of study is carried out where the chord and pitch distributions are compared to minimal losses distribution from vortex theory. Wind tunnel tests were performed with printed propellers to conclude the feasibility of the entire routine and the differences between XFOIL and CFD optimal propellers. Finally, the optimal CFD propeller is compared against a commercial propeller with the same diameter, pitch, and operational conditions, showing higher thrust and efficiency.

From a Pareto Front to Pareto Regions: A Novel Standpoint for Multiobjective Optimization

This work presents a novel approach for multiobjective optimization problems, extending the concept of a Pareto front to a new idea of the Pareto region. This new concept provides all the points beyond the Pareto front, leading to the same optimal condition with statistical assurance. This region is built using a Fisher–Snedecor test over an augmented Lagragian function, for which deductions are proposed here. This test is meant to provide an approximated depiction of the feasible operation region while using meta-heuristic optimization results to extract this information. To do so, a Constrained Sliding Particle Swarm Optimizer (CSPSO) was applied to solve a series of four benchmarks and a case study. The proposed test analyzed the CSPSO results, and the novel Pareto regions were estimated. Over this Pareto region, a clustering strategy was also developed and applied to define sub-regions that prioritize one of the objectives and an intermediary region that provides a balance between objectives. This is a valuable tool in the context of process optimization, aiming at assertive decision-making purposes. As this is a novel concept, the only way to compare it was to draw the entire regions of the benchmark functions and compare them with the methodology result. The benchmark results demonstrated that the proposed method could efficiently portray the Pareto regions. Then, the optimization of a Pressure Swing Adsorption unit was performed using the proposed approach to provide a practical application of the methodology developed here. It was possible to build the Pareto region and its respective sub-regions, where each process performance parameter is prioritized. The results demonstrated that this methodology could be helpful in processes optimization and operation. It provides more flexibility and more profound knowledge of the system under evaluation.

Application of neural network modeling and constrained particle swarm optimization of steam production from biomass power plant

In this paper, the steam production of the power plant from four biomass types has been investigated via the combination of the neural network (NN) model and constrained particle swarm optimization (CPSO) methods. First, the power plant modeling was accurately developed using NN simulation. Then, the model was applied to the optimization step, where the CPSO (the modified algorithm from the classical PSO) was introduced. The proposed method included real operation constraints to the algorithm, such as biomass feed availability and minimum steam production rate. In the optimization step, the COP has been defined as the cost function to check the present operation’s economic viability. To obtain the minimum production cost (COP), the CPSO suggested feeding wood bark from sources C and D with the amount of 104.1 and 463.8 tons/day, respectively. The result has been extensively validated by the real operation. The calculation framework in this study then applies to various production systems. Besides, by integrating the cost function in the optimization model with other indicators such as environmental impact indices, the output would provide an additional holistic picture of economic and environmental aspects.

Constrained particle swarm optimization for health maintenance in three-mass resonant servo control system with LuGre friction model

Constrained particle swarm optimization for health maintenance in three-mass resonant servo control system with LuGre friction model

Constraint handling technique based on Lebesgue measure for constrained multiobjective particle swarm optimization algorithm

In this paper, we study on how to achieve balance between minimizing the objectives, satisfying the constraints, and avoiding the population to stuck at locally optimal or locally feasible regions. We propose a new constraints handling technique based on diversity distance measure for constrained multiobjective optimization problems. We innovatively measure all effects of the constraints on the objectives by the interaction effects and direct effects of constraints on the objectives. The interactive effects of constraints on the objective functions are firstly expressed and quantified by using Mahalanobis distance and computing the value of the distance measure by involving a Lebesgue measure. Based on interactive and direct effects, we propose a new modified mechanism of objective space, called objective space modified mechanism based on diversity distance measure, so that the good infeasible solution could be more effectively used to find the optimal solution. The Pull and Push search is used to adaptively adjust the location of the Pareto front, which could preclude the population from being stuck at some locally optimal or locally feasible regions and could decrease time complexity. Comprehensive experiments completed for several benchmark problems demonstrate the competitiveness of the proposed algorithm, in comparison to the existing state-of-art constrained evolutionary multiobjective optimization.

Optimal three-dimensional design of functionally graded parts for additive manufacturing using Tamura–Tomota–Ozawa model

Although some conventional manufacturing technologies are capable of producing functionally graded materials, only a few additive manufacturing processes are able to build functionally graded materials with complex distribution of material composition. To exploit this unique advantage, we have developed a new methodology capable of optimization of material distribution for three-dimensional parts for any given conditions. Representation of material distribution was done through a new technique by extending the nonuniform rational basis spline surfaces to four-dimensional space. Mori–Tanaka, Levin, and Tamura–Tomota–Ozawa models were employed for the estimation of effective material properties of functionally graded structures. Subroutines were developed in a commercial finite element software to enable the analysis of parts made from functionally graded material. A constrained particle swarm optimization method was selected and implemented to optimize the material composition distribution taking into account the additive manufacturing limitations. As a case study, the material distribution optimization of a functionally graded femur bone plate under thermomechanical loading was considered. The objective was to maximize the safety factor; i.e. the ratio of local yield strength of the functionally graded plate over the von Mises stress. The results showed significant improvement compared to nonoptimal part and demonstrated the efficacy of the proposed methodology.

An Efficient Feature Weighting Method for Support Vector Regression

Support vector regression (SVR) is a powerful kernel-based method which has been successfully applied in regression problems. Regarding the feature-weighted SVR algorithms, its contribution to model output has been taken into account. However, the performance of the model is subject to the feature weights and the time consumption on training. In the paper, an efficient feature-weighted SVR is proposed. Firstly, the value constraint of each weight is obtained according to the maximal information coefficient which reveals the relationship between each input feature and output. Then, the constrained particle swarm optimization (PSO) algorithm is employed to optimize the feature weights and the hyperparameters simultaneously. Finally, the optimal weights are used to modify the kernel function. Simulation experiments were conducted on four synthetic datasets and seven real datasets by using the proposed model, classical SVR, and some state-of-the-art feature-weighted SVR models. The results show that the proposed method has the superior generalization ability within acceptable time.

Heat Exchanger Network Retrofit Using Particle Swarm Optimisation Algorithm

Heat exchanger network (HEN) retrofit is an effective way to reduce energy consumption and improve energy efficiency. A key criterion in industrial practice to evaluate the performance of a retrofit plan is the minimising total annualised cost. However, when pursuing the goal of minimising total annualised cost by graphical tools, sub-optimal solutions are inevitable because of the difficulty in finding the balance of utility cost and investment. In this study, a method based on constrained particle swarm optimisation (PSO) algorithm is developed for the annual cost minimisation when retrofitting HENs. The proposed method is applied based on an initial retrofit plan obtained by graphical tools. It can achieve the aim of total annualised cost reduction by adjusting the inlet and outlet temperatures of each heat exchanger through PSO with the consideration of different utility prices and investment. The results show that this method can find optimal temperature spans for heat exchangers to reduce the total annualised cost.