Particle Swarm Optimization Strategy Research Articles

Efficient Multiobjective Optimization of Time-Modulated Array Using a Hybrid Particle Swarm Algorithm With Convex Programming

An efficient hybrid method for multiobjective optimization (MO) of time-modulated array (TMA) is proposed. First, by making best use of the convexity in the TMA optimization problem, a set of reference points for Pareto front (PF) are generated by convex programming. Then, with the obtained reference points function as the prior knowledge for the initial strategy of multiobjective particle swarm optimization (MOPSO), the search of the MOPSO is located around the most interested region while the diversity of the population is also guaranteed to achieve a wide spread of PF. With the proposed hybrid method, the multiobjective optimization (MO) of relative large-scale TMA (comparing with other existing TMA MO method) can be achieved in a limited computational budget while the quality of the obtained PF is much better than the traditional nonhybrid method, thanks to the appropriate use of the problem prior knowledge. Examples with 50- and 100-elements linear TMA are presented to demonstrate the robustness of the proposed method to deal with the relative large scale multiobjective TMA optimization problems.

A hybrid bacterial foraging-particle swarm optimization technique for solving optimal reactive power dispatch problem

<div data-canvas-width="397.27351844386203">This paper presents a hybrid evolutionary computation algorithm termed as hybrid bacterial foraging-particle swarm optimization (HBFPSO) algorithm, to optimal reactive power dispatch (ORPD) problem. HBFPSO algorithm merges velocity and position updating strategy of particle swarm optimization (PSO) algorithm and reproduction and elimination dispersal of bacterial foraging algorithm (BFA). The ORPD is solved for minimization of two objective functions; system real power loss and voltage stability L-index. The objective is minimized by optimally choosing the control variables; generator excitations, tap positions of on-load tap changing transformers and switched var compensators while satisfying their constraints and also the constraints of dependent variabl</div><div data-canvas-width="98.30049385204596">es; voltages of all load buses and reactive power generation of all generators. The proposed approach has been evaluated on a standard IEEE 30 bus test system and 24 bus EHV southern region equivalent Indian power system. The results offered by the proposed algorithm are compared with those offered by other evolutionary computation algorithms reported in the recent state of the art literature and the superiority of the proposed algorithm is demonstrated.</div>

A Novel Sensor Placement Strategy for an IoT-Based Power Grid Monitoring System

Dynamic thermal rating (DTR) is a technique that can effectively reduce the complexity of the decision-making processes for a smart grid. Internet-of-Things-based DTR monitoring systems can be used to achieve reliable and low-cost remote monitoring of power grids, but this method is heavily reliant on collecting accurate real-time meteorological data by sensors deployed on the power lines. However, deploying sensors on each span of the line may not be feasible due to the high cost of such sensors. Thus, this article proposes a modified binary particle swarm optimization (MBPSO) strategy to solve multiobjective combinational decision problems. The proposed method is able to determine the minimum number of sensors required to achieve nearly ideal performance. A 161-kV line located between Xizhi and Minquan, part of the Taiwan Power Company transmission system, was selected as the experimental target. The MBPSO algorithm was solved based upon hourly meteorological data provided by Taiwan’s Central Weather Bureau. The results obtained with the proposed method show that only one sensor needs to be deployed on the third span of the line to effectively monitor more than 89.6% of high conductor temperature events, and the root mean square error on the reconstructed conductor temperature distribution is less than 0.8 °C.

Modeling and optimizing an acoustic metamaterial to minimize low-frequency structure-borne sound

Conventional noise control solutions used in the transportation industry have proven to be effective in minimizing structure-borne sound at mid to high frequencies; however, a lightweight means to control low-frequency structure-borne sound remains elusive. Recent advancements in additive manufacturing technologies have enabled researchers to develop novel acoustic metamaterial concepts capable of reducing low-frequency structure-borne sound. This work presents a methodology to numerically model and optimize an acoustic metamaterial to facilitate the development of more advanced acoustic metamaterial concepts. The investigated acoustic metamaterial consists of a periodic structure embedded with resonant inclusions that are tuned to resonate out of phase with the host structure causing an attenuation in surface vibrations. First, a numerical model of the metamaterial is created using the finite element method to generate mass and stiffness matrices for a honeycomb sandwich structure. Second, the system matrices are reduced using the Craig-Bampton Method, which are then modified to include the contribution of tuned vibration absorbers as resonant inclusions. Subsequently, the particle swarm optimization strategy is employed to optimize the mass, stiffness and damping properties of the tuned vibration absorbers to minimize the RMS surface velocity over a specified frequency range. Overall, the acoustic metamaterial exhibits a strong ability to reduce the RMS surface velocity within an optimized frequency range indicating reduced structure-borne sound emission compared to conventional honeycomb structures.

Parameter Identification in Nonlinear Mechanical Systems with Noisy Partial State Measurement Using PID-Controller Penalty Functions

Dynamic models of physical systems often contain parameters that must be estimated from experimental data. In this work, we consider the identification of parameters in nonlinear mechanical systems given noisy measurements of only some states. The resulting nonlinear optimization problem can be solved efficiently with a gradient-based optimizer, but convergence to a local optimum rather than the global optimum is common. We augment the dynamic equations with a morphing parameter and a proportional–integral–derivative (PID) controller to transform the objective function into a convex function; the global optimum can then be found using a gradient-based optimizer. The morphing parameter is used to gradually remove the PID controller in a sequence of steps, ultimately returning the model to its original form. An optimization problem is solved at each step, using the solution from the previous step as the initial guess. This strategy enables use of a gradient-based optimizer while avoiding convergence to a local optimum. The efficacy of the proposed approach is demonstrated by identifying parameters in the van der Pol–Duffing oscillator, a hydraulic engine mount system, and a magnetorheological damper system. Our method outperforms genetic algorithm and particle swarm optimization strategies, and demonstrates robustness to measurement noise.

Algorithms for solving assembly sequence planning problems

Assembly sequence planning is one of the key issues in DFA and computer-aided assembly process planning research for concurrent engineering. The purpose of this paper is to solve the problem of insufficient individual intelligence in evolutionary algorithms for assembly sequence planning, and a evolutionary algorithm for assembly sequence planning is designed. In this paper, the particle swarm optimization (PSO) algorithm is used to optimize the hybrid assembly sequence planning and assembly line balance problems. According to the assembly sequence problem, the number of assembly tool changes and the number of assembly orientation changes are transformed into the operation time of the assembly line. At the same time, the transportation of heavy parts in the assembly balance problem is considered. Then, by extracting the connection relationship and information of the parts, the disassembly method is used to inversely obtain the disassembly support matrix, and then, it is used to obtain the priority relationship diagram of the assembly operation tasks that indicate the order constraints of the job tasks on the assembly line. Aiming at the shortcoming that particle swarm optimization algorithm is easy to fall into local optimum, a various population strategy is adopted to shorten the evolution stagnation time, improve the evolution efficiency of particle swarm optimization algorithm, and enhance the optimization ability of the algorithm. Combined with the three evaluation indicators of assembly geometric feasibility, assembly process continuity, and assembly tool change times, a fitness function is constructed to achieve multi-objective optimization. Finally, experiments show that the multi-agent evolutionary algorithm is incorporated into the planning process to obtain an accurate solution through the various population strategy–particle swarm optimization algorithm, which proves the feasibility of the compound algorithm and has better performance in solving assembly sequence planning problems.

A novel PSO strategy for improving dynamic change partial shading photovoltaic maximum power point tracker

ABSTRACT Photovoltaic (PV) energy systems are very important electric generation sources in modern power systems. The generated power from the PV array is a function in its terminal voltage. Tracking the maximum power needs a DC/DC converter to control the PV array terminal voltage. The boost converter is used in this paper for this purpose. Due to the multiple peaks in the power versus voltage (P-V) characteristics of PV array a smart optimization technique is required to work as a maximum power point tracker (MPPT). The particle swarm optimization (PSO) is a superior technique to track the global peak (GP) and avoid getting trapped in one of the local peaks (LPs). Despite the superiority of PSO, it suffers from some shortcomings in the application of MPPT of PV systems such as its sluggishness convergence, its inability to catch the new GP in case of acute change in shading pattern, and the possibility of getting trapped in one of the LPs. All these shortcomings are solved in this paper using a new adaptive PSO (NA-PSO) strategy. This new strategy solved these problems by starting the duty ratio at an equal distance between each other and force the particles with lower generated power to work around the one with the highest generated power. This newly proposed technique reduced the convergence time by 50% and reduced the failure rate to zero. Also, the generated energy is increased by 10.4% compared to the conventional PSO. The results collected from the NA-PSO strategy show its superiority in reducing the convergence time and failure rate and increasing the generated power, and the system efficiency, especially in the dynamic variation of the shading pattern compared to the conventional PSO.

PSO-ACSC: a large-scale evolutionary algorithm for image matting

Image matting is an essential image processing technology due to its wide range of applications. Sampling-based image matting is one of the main branches of image matting research that estimates alpha mattes by selecting the best pixel pairs. It is essentially a large-scale multi-peak optimization problem of pixel pairs. Previous study shows that particle swarm optimization (PSO) can effectively optimize the pixel pairs. However, it still suffers from premature convergence problem which often occurs in pixel pair optimization that involves a large number of local optima. To address this problem, this work presents a parameter-free strategy for PSO called adaptive convergence speed controller (ACSC). ACSC monitors and conditionally controls the particles by competitive pixel pair recombination operator (CPPRO) and pixel pair reset operator (PPRO) during the iteration. ACSC performs CPPRO to improve the competitiveness of a particle when the performance of most of the pixel pairs is worse than that of the best-so-far solution. PPRO is performed to avoid premature convergence when the alpha mattes regarding two selected particles are highly similar. Experimental results show that ACSC significantly enhances the performance of PSO for image matting and provides competitive alpha mattes comparing with state-of-the-art evolutionary algorithms.

A Novel FastSLAM Framework Based on 2D Lidar for Autonomous Mobile Robot

The autonomous navigation and environment exploration of mobile robots are carried out on the premise of the ability of environment sensing. Simultaneous localisation and mapping (SLAM) is the key algorithm in perceiving and mapping an environment in real time. FastSLAM has played an increasingly significant role in the SLAM problem. In order to enhance the performance of FastSLAM, a novel framework called IFastSLAM is proposed, based on particle swarm optimisation (PSO). In this framework, an adaptive resampling strategy is proposed that uses the genetic algorithm to increase the diversity of particles, and the principles of fractional differential theory and chaotic optimisation are combined into the algorithm to improve the conventional PSO approach. We observe that the fractional differential approach speeds up the iteration of the algorithm and chaotic optimisation prevents premature convergence. A new idea of a virtual particle is put forward as the global optimisation target for the improved PSO scheme. This approach is more accurate in terms of determining the optimisation target based on the geometric position of the particle, compared to an approach based on the maximum weight value of the particle. The proposed IFastSLAM method is compared with conventional FastSLAM, PSO-FastSLAM, and an adaptive generic FastSLAM algorithm (AGA-FastSLAM). The superiority of IFastSLAM is verified by simulations, experiments with a real-world dataset, and field experiments.

Differential Evolution Based Reliable Cooperative Spectrum Sensing in the Presence of Malicious Users

In Cognitive Radio Network, access to the vacant spectrum holes in the licensed Primary User (PU) channel is allowed to the Secondary Users. In order to use the PU spectral holes accurately and on time, spectrum sensing is extremely important to avoid interference with the PU transmission. In Cooperative Spectrum Sensing (CSS) individual users report the Fusion Center (FC) about sensing data for a global decision which leads to better sensing results as compared to non-cooperative sensing. The objective of this paper is to make the CSS performance precise and accurate in the presence of Malicious Users (MUs) reporting false sensing information to the FC. The proposed scheme reduces falsifying effects of the YES, NO and OPPOSITE categories of MUs in CSS using Differential Evolution (DE) algorithm. A dynamic threshold value is determined using optimum coefficient vector by the DE at the FC. This leads to minimum error probability in detecting PU channel when the YES, NO and OPPOSITE categories of MUs reports FC in CSS. The weighting coefficient vector is further employed at the FC to assign weights to the received sensing information of cooperative users. Therefore, cooperative users with minutely erroneous reports receive high weight as compared to MUs. The simulations performed for varying number of MUs, total cooperative users, Signal to Noise Ratios and sensing samples show minimum error in detecting PU activity for the proposed DE technique in comparison with the Kullback–Leibler divergence, particle swarm optimization, Genetic Algorithm and Maximum Gain Combination schemes.

Study on inertia weight decreasing strategy of particle swarm optimization based on inverse coseca

The standard particle swarm optimization (pso), which introduces inertia weight w, is an effective method to find the extreme value of the function. However, particle swarm optimization (pso) has some disadvantages. When dealing with optimization problems, pso lacks effective parameter control and is prone to fall into local optimization, which leads to low convergence accuracy. In, this paper, put forward a new improved particle swarm optimization (pso) algorithm, The nonlinear decreasing inertia weight by the CSC function strategy, at the same time to join the beta distribution on random Numbers, thus to balance the global search and local search ability of the algorithm. The learning factor is the changed asynchronously to improve the learning ability of the algorithm. By adopting Griewank, Rastrigrin, J.D. Schaffer three standard test functions to simulate experiment, at the same time and the basic particle swarm algorithm the inertia weight in a fixed value, the linear regressive LDIW and nonlinear regressive comparison. The experimental results show that the nonlinear decreasing strategy with dynamic adjustment of inverse cosecant function can improve the convergence speed and stability.

An adaptive integral separated proportional–integral controller based strategy for particle swarm optimization

Particle swarm optimization algorithm (PSO), which updates the particle by the linear summation of the particle’s past momentum and current search direction, has demonstrated its power in many optimization applications. However, few researches have focused on the overshoot problem of PSO caused by past momentum, which may result in an oscillation search and slow convergence speed on complex optimization problems. Based on the connection between the PSO optimization process and the PID controller based control system, we first analyze the effect of the momentum in PSO, then we find that the oscillation problem relates to the gathering of the momentum term and the current search direction. Inspired by the conditional integration in automatic control, we propose an adaptive search direction learning approach for PSO, namely ISPSO (Integral Separated PI controller-based PSO). The ISPSO separates momentum term adaptively when the current search direction is consistent with historical momentum direction, and then the IS strategy will guide particles to fly to better and steady directions by eliminating the integral gathering of historical momentum. We select seven main-stream approaches as control group, and conduct experiments on benchmark CEC2013 test suite. The experimental results of ISPSO, providing the faster steady global convergence and higher solution accuracy, show a significant improvement on PSO algorithm. Compared with the results of the seven methods, the performance of ISPSO is also promising in general, especially for unimodal and composition functions. Furthermore, our results validate the generalization and effectiveness of IS strategy by the designed experiments of PSO variants with and without IS strategy, which also indicates that the IS strategy can be applied to PSO variants with any topological structures.

Simulation and experimental validation of fast adaptive particle swarm optimization strategy for photovoltaic global peak tracker under dynamic partial shading

The P–V characteristics of PV array has one peak under uniformly distributed irradiances. Whereas, there are many peaks in the P–V curve when the irradiance is not uniformly distributed over the PV array which is called “partial shading conditions (PSCs)”. Due to its robustness in tracking the global peak (GP) of many applications, metaheuristic techniques are used as maximum power point tracker (MPPT) for the PV system under PSCs. Particle swarm optimization (PSO) has been used in this paper for this purpose. Three problems associated with the PSO have been solved in this paper using a novel fast adaptive PSO (APSO) strategy. The problem of long convergence time has been solved by updating starting values of the duty ratio of the DC-DC boost converter to be at the anticipated places of peaks. This modification reduces the convergence time and avoids the premature convergence. The problem of stored GP in the memory will prevent the PSO from capturing the current GP in case of it is lower than the stored one. This problem is solved in this paper by updating the memorized GP with the current maximum power when it is not changed for two successive iterations. The third problem of sudden change in PSCs is solved by using the updated values of duty ratio at anticipated peaks as initial values for particles. To the best of the authors’ knowledge, these problems have not been discussed or solved before in the literature. A comparison to the state-of-the-art random initialization PSO strategy shows the superiority of the proposed APSO technique in terms of tracking speed and dynamic GP tracking. The results obtained from the simulation of this strategy proved its superiority in always tracking the GP under dynamic PSCs change.

Robust optimal control problem with multiple characteristic time points in the objective for a batch nonlinear time-varying process using parallel global optimization

In this paper, we consider a nonlinear time-varying dynamical (NTVD) system with uncertain system parameters assigned to their nominal values in batch culture of glycerol bioconversion to 1,3-propanediol induced by Klebsiella pneumoniae. Some important properties of the NTVD system are discussed. Our goal is to choose a time-varying function for the NTVD system. Thus, an optimal control problem (OCP) governed by the NTVD system and subject to continuous state inequality constraints arising from engineering specifications is proposed, where the time-varying function is the control function to be chosen such that system cost (the relative error between experimental data and the simulated output of the system) and system robustness (robustness of the system with respect to uncertain system parameters) is optimized. Based on the actual fermentation process, the time-varying function is specified by a four-piecewise linear function with unknown kinetic parameters and switching instants. The resulting OCP is approximated as a sequence of nonlinear mathematical programming subproblems by the time-scaling transformation, the constraint transcription and the locally smoothing approximation techniques. A parallel global optimization algorithm, based on a novel combination of limited information particle swarm optimization and local search strategy, is then developed to solve these subproblems. Numerical results show the effectiveness and applicability of our proposed algorithm.

Greedy algorithm for image quality optimisation based on turtle-shell steganography

Information hiding, also known as data hiding, is an emerging field that combines multiple theories and technologies. In recent years, Chang et al. and Liu et al. have proposed new data hiding schemes based on Sudoku, a turtle-shell, etc. These proposed schemes have their own advantages in terms of visual quality and embedded capacity. However, the reference matrices used in these schemes are not optimal. Based on the characteristics of these schemes, Jin et al. employed particle swarm optimisation to select the reference matrix and achieved approximately optimal results in reducing the distortion of the stego-image. However, the complexity is high. In this paper, a turtle-shell matrix optimisation scheme is proposed using a greedy algorithm. The experimental results show that our proposed greedy algorithm is better than the particle swarm optimisation scheme at finding a near-optimal matrix and achieving better stego-image quality, and it outperforms the particle swarm optimisation scheme in terms of computational amount and efficiency.

Energy-Aware Data Gathering Mechanism for Mobile Sink in Wireless Sensor Networks Using Particle Swarm Optimization

In order to mitigate the hot spot problem and prolong the network lifetime, data gathering with mobile sink is an effective measure to enhance the system performance. However, the movement strategy of sink node can be regarded as traveling salesman problem, which can hardly obtain the solution with polynomial running time. To address above problem, an energy-aware data gathering mechanism for mobile sink in wireless sensor networks using particle swarm optimization is introduced. Firstly, the mathematical model is established according to the total energy consumption and delay constraints for mobile sink’s data collection. Then, the optimal rendezvous points are selected to aggregate data originated from the source nodes through multi-hop relay, and the aggregation tree will be constructed for data transmission. The spanning tree is encoded into particles, and the random method is designed to generate the data collection spanning tree with constrain of tree height limit. Furthermore, a particle swarm optimization strategy with adaptive elite mutation is designed to improve the population diversity and avoid falling into the local optimal solution prematurely. Experimental results show that the proposed method can meet the delay requirements and reduce the total energy consumption of the network.

A Hybrid Leader Selection Strategy for Many-Objective Particle Swarm Optimization

Many existing Multi-objective Particle Swarm Optimizers (MOPSOs) may encounter difficulties for a set of good approximated solutions when solving problems with more than three objectives. One possible reason is that the diluted selection pressure causes MOPSOs to fail to generate a set of good approximated Pareto solutions. In this paper, a new approach called the Hybrid Global Leader Selection Strategy (HGLSS) is proposed to deal with many-objective problems more effectively. HGLSS provides two global leader selection mechanisms: one for exploration and one for exploitation. Each particle (solution) can choose one of these two leader selection schemes to identify its global best leader. An external archive is adopted for maintaining the diversity of the found solutions and it contains the final solution reported at the end of the run. The update of the external archive is based on both Pareto dominance and density estimation. The performance of the proposed approach is compared with respect to nine state-of-the-art multi-objective metaheuristics in solving several benchmark problems. Our results indicate that the proposed algorithm generally outperforms the others in terms of Modified Inverted Generational Distance (IGD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> ) indicator.

Modified Particle Swarm Optimization With Effective Guides

Despite of its simplicity, the conventional learning strategy of canonical particle swarm optimization (PSO) is inefficient to handle complex optimization problems due to its tendency of overemphasizing the fitness information of global best position without considering the diversity information of swarm. In this article, a modified particle swarm optimization with effective guides (MPSOEG) is proposed, aiming to improve the algorithm's search performances in handling the optimization problems with different characteristics. Depending on the search performance of algorithm, two types of exemplars can be generated by an optimal guide creation (OGC) module incorporated into MPSOEG by referring to the particles with valuable directional information. Particularly, a global exemplar is generated by OCG module to guide the swarm converging towards the promising solution regions of search space, whereas a unique local exemplar can be customized for each particle to enable it escaping from local or non-optimal solution regions. In contrary to global best particle, the exemplars generated by OGC module are able to guide all MPSOEG particles more effectively by considering both fitness and diversity information of swarm, hence can achieve better balancing of algorithm's exploration and exploitation searches. Another notable contribution of MPSOEG is the simplicity of its learning framework through the elimination of both inertia weight and acceleration coefficients parameters. Comprehensive simulation studies are conducted with 25 benchmark functions and the proposed MPSOEG is reported to outperform its six peer algorithms in terms of search accuracy, search reliability and search efficiency in most tested problems.

Neuro-swarms intelligent computing using Gudermannian kernel for solving a class of second order Lane-Emden singular nonlinear model

&lt;abstract&gt; &lt;p&gt;The present work is to design a novel Neuro swarm computing standards using artificial intelligence scheme to exploit the Gudermannian neural networks (GNN)accomplished with global and local search ability of particle swarm optimization (PSO) and sequential quadratic programming scheme (SQPS), called as GNN-PSO-SQPS to solve a class of the second order Lane-Emden singular nonlinear model (SO-LES-NM). The suggested intelligent computing solver GNN-PSO-SQPS using the Gudermannian kernel are unified with the configuration of the hidden layers of GNN of differential operators for solving the SO-LES-NM. An error based fitness function (FF) applying the differential form of the differential model and corresponding boundary conditions. The FF is optimized together with the combined heuristics of PSO-SQPS. Three problems of the SO-LES-NM are solved to validate the correctness, effectiveness and competence of the designed GNN-PSO-SQPS. The performance of the GNN-PSO-SQPS through statistical operators is tested to check the constancy, convergence and precision.&lt;/p&gt; &lt;/abstract&gt;

Comparative Study of Optimization Algorithms on the Design of Broadband Antennas

Broadband antennas find many applications in modern communication systems, such as Wi-Fi, 5G, and SatCom. Multiple competing optimization methods are available for the application to antenna design, while it would be preferable to know in advance if any method is superior. Here, an application of the cross-entropy method, along with particle swarm optimization and covariance matrix adaptation evolutionary strategy, to the design of broadband antennas is presented. The first example is an aperture-coupled microstrip patch antenna that has 9.5 dBi peak directivity and 53% bandwidth after optimization. It is then used as a feed in a high-gain broadband resonant cavity antenna. Using an all-dielectric superstrate with a transverse permittivity gradient, a compact thin resonant cavity antenna with a peak directivity of 19 dBi and 40% 3-dB bandwidth was designed. A comparative analysis of the cross-entropy method, particle swarm optimization, and covariance matrix adaptation evolutionary strategy applied to these two problems was carried out to provide the basis for further optimization of antennas in radio frequency and microwave frequency bands. We found that although all three methods reached a similar solution, the cross-entropy method has a speed advantage. It improves our ability to optimize existing designs and has wider applicability beyond antenna engineering.