Modified Particle Swarm Optimization Method Research Articles

A Longitudinal control for an autonomous vehicle using modified particle swarm optimization method

A Longitudinal control for an autonomous vehicle using modified particle swarm optimization method

Proposed Hybrid Technique in Cryptanalysis of Cryptosystem Based on PSO and SA

Modern ciphers are one of the more difficult to break cipher systems because these ciphers high security, high speed, non - propagation error and difficulty in breaking it. One of the most important weaknesses of stream cipher is a matching or correlation between the output key-stream and the output of shift registers. This work considers new investigation methods for cryptanalysis stream cipher using ciphertext only attack depending on Particle Swarm Optimization (PSO) for the automatic extraction for the key. It also introduces a cryptanalysis system based on PSO with suggestion for enhancement of the performance of PSO, by using Simulated Annealing (SA). Additionally, it presents a comparison for the cryptanalysis system results that were obtained by the proposed technique, which is called Modified PSO (MPSO) with classical PSO and GA. These algorithms can be used for reducing the number of attempts or trials of key space, which are needed to reach to the optimal solution (the exact initial setting of Linear Feedback Shift Register (LFSR)),and increase the speed of the search process to find the best solution. Based on the obtained results, these algorithms were shown to be effective at finding the optimal solution and the MPSO method operates better compared with PSO in the term of time and accuracy. Well known stream cipher systems were attacked by the two soft computing methods as the cases of study, which are Geffe , Brȕer and Shrinking stream cipher systems.

Optimization method for spatial route adjustment of multi-bends pipes considering assembly demands

PurposeThis paper aims to propose an optimization method to automatically adjust the spatial route of multibend pipes to meet the assembly demands in constrained space.Design/methodology/approachThe compact geometric parameters that uniquely determine the pipe route are analyzed. Besides, the relationship between these parameters and the end pose is revealed based on the exponential product formula. Mathematical representations for the engineering constraints, including the end pose restriction, collision interference, manufacture ability and geometric limitations, are further established. On this basis, the adjustment of the spatial route is formulated as a multiconstraint optimization problem. A modified particle swarm optimization method based on the combination of gradient projection and swarm intelligence is designed to find the near-optimal pipe that meets the required assembly demands.FindingsThe experimental results show that the proposed method can effectively find the feasible pipe route that satisfies the engineering constraints and the end pose requirement is highly guaranteed.Originality/valueThe proposed method can automate the geometric adjustment of multi-bend pipes to meet the actual assembly demands, which significantly reduces manual efforts and guarantees high accuracy. The results demonstrate the possibility of further applications in the pipe assembly or design process, especially in ships, aerospace products or pressure vessels.

Optimal control problem arising in mathematical modeling of cerebral vascular pathology embolization

Arteriovenous malformation (AVM) of the brain is a congenital vascular abnormality, in which the arterial and venous blood pools are intertwined and directly connected. This dangerous disease causes a high risk of intracranial hemorrhage and disrupts brain functioning. The preferred method of AVM treating is embolization, which is the endovascular filling of abnormal AVM vessels with a special embolic agent. Despite the fact that this method is widely used in neurosurgery, in some cases its use is accompanied by perioperative AVM vessels rupture. In this regard, the aim of this work is to study the optimal scenarios for multi-stage AVM embolization from the effectiveness and safety of the procedure point of view. Mathematically, the joint movement of blood and embolic agent in the AVM body is described on the basis of a one-dimensional two-phase filtration model, which takes into account the redistribution of blood to surrounding healthy vessels. For the numerical solution of the resulting integro-differential system of equations, a monotonic modification of the CABARET scheme is used. To find optimal embolization scenarios, the optimal control problem with phase constraints arising from medicine is formulated. A modified particle swarm optimization method is used to solve this problem numerically. This technique is used to obtain optimal embolization scenarios on the basis of real patients clinical data collected during neurosurgical operations.

Motion control of the inverted pendulum: development of the installation, identification of the system and synthesis of the optimal motion controller

In the work, an approach has been developed that allows of synthesizing optimal controllers of dynamic systems. It consists in the identification of a physical model of a dynamical system (in the case under consideration of the "inverted pendulum" type). This provides the ground for the controller synthesis problem statement. This approach does not require a mathematical model of the system in the form of a system of differential equations, which is its advantage. However, in order to use the advantage, it is necessary to estimate the quality of the system identification. Such calculations showed the validity of the developed approach. The synthesis of the optimal controller was carried out on the basis of the well-known methodology, which presupposes the reduction of the original problem to the problem of unconstrained optimization of a function with a complex topology. For this, a modified particle swarm optimization method has been used. Experimental validation of the control results has shown in practice the complete achievement of the control goal – stabilization of the system with the presence of minor residual oscillations of the phase coordinates of the system.

MODIFIED PSO ALGORITHM WITH NON-LINEARLY DECREASING INERTIA WEIGHT

This paper proposes a modified particle swarm optimization method with non linearly decreasing inertia weight (MPSO-NDIW) and time varying acceleration coefficients. In this MPSO-NDIW method, proper control of the global exploration and local exploitation is done in finding the optimum solution efficiently. In the early stage, full range of search space is allowed for search by the PSO algorithm and in the later stages of search, fine tuning of solution is done so that the algorithm converges to the global optima efficiently .In velocity vector equation inertia weight and acceleration coefficients are non-linearly varied with iterations. To compare the performance of the MPSO-NDIW method with the other improved PSO, four well known benchmark test functions are used. The results reveal that MPSO-NDIW is a efficient technique and has better performance. KeyWords - Acceleration coefficients, particle swarm, asymmetric initialization, velocity vector, position vector.

Duhem Model-Based Hysteresis Identification in Piezo-Actuated Nano-Stage Using Modified Particle Swarm Optimization.

This paper presents modeling and parameter identification of the Duhem model to describe the hysteresis in the Piezoelectric actuated nano-stage. First, the parameter identification problem of the Duhem model is modeled into an optimization problem. A modified particle swarm optimization (MPSO) technique, which escapes the problem of local optima in a traditional PSO algorithm, is proposed to identify the parameters of the Duhem model. In particular, a randomness operator is introduced in the optimization process which acts separately on each dimension of the search space, thus improving convergence and model identification properties of PSO. The effectiveness of the proposed MPSO method was demonstrated using different benchmark functions. The proposed MPSO-based identification scheme was used to identify the Duhem model parameters; then, the results were validated using experimental data. The results show that the proposed MPSO method is more effective in optimizing the complex benchmark functions as well as the real-world model identification problems compared to conventional PSO and genetic algorithm (GA).

The Optimal Allocation of Distributed Generators Considering Fault Current and Levelized Cost of Energy Using the Particle Swarm Optimization Method

The power requirements of grids have risen as artificial intelligence and electric vehicle technologies have been used. Thus, the installation of distributed generators (DGs) has become an essential factor to streamline power grids. The objective of this study is to optimize the capacity and location of DGs. For this purpose, an objective function was defined, which takes into account the fault current and the levelized cost of energy, and a modified particle swarm optimization method was applied. Then, we analyzed a case of a single line-to-ground fault with a test feeder (i.e., the IEEE 30 bus system) with no DGs connected, as well as a case where the DGs are optimally connected. The effect of the optimally allocated DGs on the system was analyzed. We discuss an optimal layout method that takes the economic efficiency of the DG installation into account.

Blockchain-based Secure Data Transmission for UAV Swarm using Modified Particle Swarm Optimization Path Planning Algorithm

With the rapid development of unmanned aerial vehicles assisted applications enabled with a communication system, they are open to various malicious attacks. As a new form of flying things, they can access the network for better communication via the aerial base station. Most of the Unmanned aerial vehicles assisted flying objects' optimal path selection schemes does not consider the path deviation. In path deviation attacks, secure data transmission are not addressed in existing works. The secure communication process between Unmanned aerial vehicles and base station are exploited through security-based attacks. Moreover, path loss issue leads to multicast packet loss and unsecured broadcast. The existing network architecture setup does not fulfill the secure data communication and privacy issues. In this paper, Blockchain is utilized to investigate the secure communication between Unmanned aerial vehicles to Wireless Unmanned aerial vehicles Base stations. Since the destination information is dynamic under an uncertain environment, it will cause a delay in data communication. Unmanned aerial vehicles are more vulnerable to security attacks. The proposed blockchain-based architecture supports secure data communication in Unmanned aerial vehicles uncertain environments. To improve network security, this paper designs a modified particle swarm optimization method for better path selection. Through these experimental results, a blockchain-based data communication scheme is outer performed concerning network security.

The Modified Particle Swarm Optimization for a Special Case of the Assignment Problem: A Case Study in Chicken Transportation

This research aims at solving the special case of multistate assignment problem. The problem includes many special characteristics which are not normally included in the assignment problem. There are many types and conditions of vehicles included in the planning and different road conditions of traveling, which would have different effects on fuel consumption, which is the objective function of the study. The proposed problem is determined as a large and complicated problem making the optimization software unable to find an optimal solution within the proper time. Therefore, the researchers had developed a method for determining the optimal solution by using particle swarm optimization (PSO) in which the methods were developed for solving the proposed problem. This method is called the modified particle swarm optimization (modified PSO). The proposed method was tested with three groups of tested instances, i.e., small, medium, and large groups. The computational result shows that, in small-sized and medium-sized problems, the proposed method performed not significantly different from the optimization software, and in the large-sized problems, the modified PSO method gave 3.61% lower cost than the cost generated from best solution generated from optimization software within 72 hours and it gave 11.03% better solution than that of the best existing heuristics published so far (differential evolution algorithm).

Multi-UUV Cooperative Dynamic Maneuver Decision-Making Algorithm Using Intuitionistic Fuzzy Game Theory

In this paper, a multi-unmanned underwater vehicle (UUV) cooperative dynamic maneuver decision-making algorithm is proposed based on the combination of game theory and intuitionistic fuzzy sets. Underwater environments with weak connectivity, underwater noise, and dynamic uncertainties are fully considered through intuitionistic fuzzy sets, which solves one of the main problems in making decisions underwater. Subsequently, the intuitionistic fuzzy multiattribute evaluation of a UUV maneuver strategy is conducted, and the intuitionistic fuzzy payment matrix of the cooperative dynamic maneuver game is obtained. Thereafter, the Nash equilibrium condition is proposed to satisfy the intuitionistic fuzzy total order, and the Nash equilibrium maneuver decision-making model under a dynamic underwater environment is established. Meanwhile, the modified particle swarm optimization method is presented to solve the established problem and find the optimal strategy. Finally, an example is used to verify the superiority of the proposed cooperative dynamic maneuver decision-making algorithm.

An enhanced spatial statistical method for continuous monitoring of winter road surface conditions

To facilitate more efficient winter maintenance decision support, road weather information systems (RWIS) have been widely used by highway agencies. However, the cost of RWIS stations is high, and they have limited monitoring coverage. To address this challenge, this paper presents an innovative framework that applies regression kriging to integrate stationary and mobile RWIS data to improve the accuracy of road surface temperature (RST) estimation. Furthermore, an optimal RWIS network expansion strategy is introduced by incorporating a modified particle swarm optimization method with the objective of minimizing spatially averaged kriging estimation errors. A sensitivity analysis is also conducted to investigate the influence of station densities on model performance. The case study from Alberta, Canada, demonstrates the feasibility and applicability of the proposed method. The findings provide insights for continuous monitoring and visualization of both road weather and surface conditions and for optimizing RWIS network planning.

A Modified Consensus Model in Group Decision Making With an Allocation of Information Granularity

In order to deal with a complex decision making problem, a group of experts are commonly invited to express their opinions and reach a final decision. For the purpose of building consensus among the members of the group, it is requisite to include iterative mechanisms of brain storming. The particle swarm optimization (PSO) method can be used to model the interactive process of forming decisions. In this paper, we propose a modified consensus model of group decision making augmented by an allocation of information granularity. Under a level of information granularity, it is found that the consistency indexes of randomly created multiplicative reciprocal matrices in the analytic hierarchy process may be bigger than unity. To alleviate this limitation, a modified objective function is proposed, and it is optimized by using the modified PSO method. The information granularity is allocated by considering the reciprocity of preference relations. Some comparative studies are carried out to illustrate the proposed consensus model through numerical examples. The observations reveal that a more consistent decision can be achieved by the proposed approach.

Improved Rao-Blackwellised particle filter based on randomly weighted particle swarm optimization

In this paper, a new Rao-Blackwellised particle filter simultaneous localization and mapping method based on randomly weighted particle swarm optimization is proposed in order to solve some problems in the Rao-Blackwellised particle filter, including the depletion of particles and the loss of the diversity in the process of resampling. The particle swarm optimization strategy is introduced in the modified algorithm, and the inertia weight is randomly set. Modified particle swarm optimization method is utilized to optimize the particle set in order to avoid particle degenerating and to keep the diversity. The proposed algorithm is simulated in the Qt platform. To verify the proposed method, the experiment is performed using turtlebot robots under the robot operating system. Results show that the proposed method is superior than extended Kalman filter based on simultaneous localization and mapping method.

Smart Global Maximum Power Point Tracking Controller of Photovoltaic Module Arrays

This study first explored the effect of shading on the output characteristics of modules in a photovoltaic module array. Next, a modified particle swarm optimization (PSO) method was employed to track the maximum power point of the multiple-peak characteristic curve of the array. Through the optimization method, the weighting value and cognition learning factor decreased with an increasing number of iterations, whereas the social learning factor increased, thereby enhancing the tracking capability of a maximum power point tracker. In addition, the weighting value was slightly modified on the basis of the changes in the slope and power of the characteristic curve to increase the tracking speed and stability of the tracker. Finally, a PIC18F8720 microcontroller was coordinated with peripheral hardware circuits to realize the proposed PSO method, which was then adopted to track the maximum power point of the power–voltage (P–V) output characteristic curve of the photovoltaic module array under shading. Subsequently, tests were conducted to verify that the modified PSO method exhibited favorable tracking speed and accuracy.

Measurement of absolute displacement based on dual-path submicron-aperture fiber point-diffraction interferometer

The measurement of three-dimensional absolute displacement within large range can be achieved with submicron-aperture fiber point-diffraction interferometer (SFPDI), in which the numerical iterative algorithm for displacement reconstruction and point-diffraction wavefront determine the achievable measurement accuracy, reliability and efficiency of the system. To avoid the poor measurement accuracy in the lateral direction parallel to the fringe direction in the SFPDI with only one fiber pair, a dual-path SFPDI (DP-SFPDI) based on modified fast searching particle swarm optimization (PSO) algorithm is proposed to realize the rapid and accurate measurement of three-dimensional absolute displacement. Based on the DP-SFPDI with two submicron-aperture fiber pairs and modified PSO method, the measurement reliability, efficiency and accuracy in all the three dimensions of the system are significantly improved, making it more feasible for practical application. The effect of point-diffraction wavefront error on the measurement is analyzed. Both the numerical simulation and comparison experiments have been carried out to demonstrate the accuracy and feasibility of the proposed DP-SFPDI system, high measurement accuracy, convergence rate and efficiency have been realized with the proposed method.

Time-Optimal Path Planning With Power Schedules for a Solar-Powered Ground Robot

This paper examines an integrated path planning and power management problem for a solar-powered unmanned ground vehicle (UGV). The proposed method seeks to minimize the travel time of the UGV through an area of known energy density by designing a smooth, heuristically optimized path and allocating the vehicle's power among its electrical components, while the UGV harvests ambient energy along the designed path to satisfy with the mission's strict energy constraints. A scalar field is first established to evaluate the solar radiation density at discrete locations. A modified particle swarm optimization method is applied to search for a minimal time path wherein the energy gathered is equal to or greater than the energy expended. The proposed modeling and optimization strategy is verified through computer simulation and experimental demonstration.

Interrogation of a spectral profile division multiplexed FBG sensor network using a modified particle swarm optimization method

This paper applies the concept of spectral profile division multiplexing to track each Bragg wavelength shift in a serially multiplexed fiber Bragg grating (FBG) network. Each sensor in the network is uniquely characterized by its own reflected spectrum shape, thus spectral overlapping is allowed in the wavelength domain. In contrast to the previous literature, spectral distortion caused by multiple reflections and spectral shadowing between FBG sensors, that occur in serial topology sensor networks, are considered in the identification algorithm. To detect the Bragg wavelength shift of each FBG, a nonlinear optimization function based on the output spectrum is constructed and a modified dynamic multi-swarm particle swarm optimizer is employed. The multiplexing approach is experimentally demonstrated on data from multiplexed sensor networks with up to four sensors. The wavelength prediction results show that the method can efficiently interrogate the multiplexed network in these overlapped situations. Specifically, the maximum error in a fully overlapped situation in the specific four sensor network demonstrated here was only 110 pm. A more general analysis of the prediction error and guidelines to optimize the sensor network are the subject of future work.

Optimization of cylindrical shells stiffened by rings under external pressure including their post-buckling behaviour

Optimization of a simply supported cylindrical shell stiffened by inner rings is considered in this paper. The shell is loaded by external pressure. The first critical buckling load is maximized. A material volume and a slope of a post-critical path are assumed as optimization constraints. The inner rings are made from the material obtained from the shell by decreasing its thickness. This way the volume of the material remain constant. The structure is modelled and solved by the finite element method (FEM). The linear and nonlinear stability analyses are done in the ANSYS software. An effect of geometric imperfections on a shape of equilibrium path is discussed. The optimization was performed numerically using the modified particle swarm optimization method (MPSO). The results are compared with a reference cylindrical shell with no rings. The single ring placed in the middle of the shell is good enough for stabilization of a post-buckling path for a shorter shell, regardless of its thickness. It is necessary to use three rings optimally distributed along the shell length, for a longer shell. The additional optimization profit was obtained by varying internal diameters of the rings. The proposed concept of shell stiffening by inner rings eliminates a major disadvantage of smooth cylindrical shells, namely an unstable post-buckling path.

A hybrid approach based on IGDT–MPSO method for optimal bidding strategy of price-taker generation station in day-ahead electricity market

This paper considers a price-taker generation station producer that participates in a day-ahead market. The producer behaves as a price-taker participant in the day-ahead electricity market. In electricity market, the price-taker producer could develop bidding strategies to maximize own profits. While making optimal bidding strategy, the market price uncertainty needs to be considered as they have direct impact on the expected profit and bidding curves. In this paper, a hybrid approach based on information gap decision theory (IGDT) and modified particle swarm optimization (MPSO) is used to develop the optimal bidding strategy. Information gap decision theory is used to model the optimal bidding strategy problem. It assesses the robustness/opportunity of optimal bidding strategy in the face of the market price uncertainty while price-taker producer considers whether a decision risk-averse or risk-taking. The optimization problems to delivering IGDT approach are solved using MPSO. It is shown that risk-averse or risk-taking decisions might affect the expected profit and bidding curve to day-ahead electricity market. The IGDT–MPSO method is illustrated through a case study and compared to IGDT–MINLP method.