Velocity Clamping Research Articles

Opposition‐based ideal gas molecular movement algorithm with Cauchy mutation, velocity clamping, and mirror operator

AbstractThe ideal gas molecular movement (IGMM) as a metaheuristic optimization method is a prominent option for solving optimization problems. However, in some complex cases, IGMM may possess premature convergence or get trapped in local optima. Therefore, to tackle these issues, this paper indicates a new modified IGMM algorithm named opposition‐based IGMM, which has been incorporated with opposition based learning (OBL), Cauchy mutation (CM), velocity clamping (VC), and mirror operator (MO) to enhance its performance. OBL, VC, and MO improve the convergence of IGMM, whereas CM assists IGMM to escape local optima. The effect of each strategy, OBL, CM, VC, and MO, on IGMM, is confirmed through 30 low and high‐dimensional benchmarks, including 23 well‐known mathematical problems and CEC2017 as complex test functions and three engineering problems. Analysis results represent that integration IGMM with OBL, CM, VC, and MO has the best performance among other IGMM variants and eventually improved IGMM in exploration, exploitation, accelerating convergence, and local optima avoidance.

Adaptive guided salp swarm algorithm with velocity clamping mechanism for solving optimization problems

Abstract Salp swarm algorithm (SSA) is a well-established population-based optimizer that exhibits strong exploration ability, but slow convergence and poor exploitation capability. In this paper, an endeavour is made to enhance the performance of the basic SSA. The new upgraded version of SSA named as ‘adaptive strategy-based SSA (ABSSA) algorithm’ is proposed in this paper. First, the exploratory scope and food source navigating commands of SSA are enriched using the inertia weight and boosted global best-guided mechanism. Next, a novel velocity clamping strategy is designed to efficiently stabilize the balance between the exploration and exploitation operations. In addition, an adaptive conversion parameter tactic is designed to modify the position update equation to effectively intensify the local exploitation competency and solution accuracy. The effectiveness of the proposed ABSSA algorithm is verified by a series of problems, including 23 classical benchmark functions, 29 complex optimization problems from CEC 2017, and five engineering design tasks. The experimental results show that the developed ABSSA approach performs significantly better than the standard SSA and other competitors. Moreover, ABSSA is implemented to handle path planning and obstacle avoidance (PPOA) tasks in autonomous mobile robots and compared with some swarm intelligent approach-based path planners. The experimental results indicate that the ABSSA-based PPOA method is a reliable path planning algorithm.

Velocity clamping-assisted adaptive salp swarm algorithm: balance analysis and case studies.

Salp swarm algorithm (SSA) is a recently proposed, powerful swarm-intelligence based optimizer, which is inspired by the unique foraging style of salps in oceans. However, the original SSA suffers from some limitations including immature balance between exploitation and exploration operators, slow convergence and local optimal stagnation. To alleviate these deficiencies, a modified SSA (called VC-SSA) with velocity clamping strategy, reduction factor tactic, and adaptive weight mechanism is developed. Firstly, a novel velocity clamping mechanism is designed to boost the exploitation ability and the solution accuracy. Next, a reduction factor is arranged to bolster the exploration capability and accelerate the convergence speed. Finally, a novel position update equation is designed by injecting an inertia weight to catch a better balance between local and global search. 23 classical benchmark test problems, 30 complex optimization tasks from CEC 2017, and five engineering design problems are employed to authenticate the effectiveness of the developed VC-SSA. The experimental results of VC-SSA are compared with a series of cutting-edge metaheuristics. The comparisons reveal that VC-SSA provides better performance against the canonical SSA, SSA variants, and other well-established metaheuristic paradigms. In addition, VC-SSA is utilized to handle a mobile robot path planning task. The results show that VC-SSA can provide the best results compared to the competitors and it can serve as an auxiliary tool for mobile robot path planning.

Perfectly convergent particle swarm optimisation in multidimensional space

In this paper a novel evolutionary algorithm, perfectly convergent particle swarm optimisation (PCPSO) has been proposed. This is an intelligent algorithm which does not get trapped in local minima by using personal best value along with new parameters and new velocity update equation for better exploration in the search space. Velocity clamping is used to avoid the particles from escaping the search space by preventing the initial explosion of particle velocity. The velocity clamping effectively help to control the maximum velocity of the particles by limiting the particle step size and hence the particle will have to perform further steps in the search space to travel the same distance which determines its search precision for exploration or exploitation and finally align them towards the true global minimum. Experimental results show that by using perfect convergent particle swarm optimisation (PCPSO) approach computational efficiency is increased as compared to other variants of PSO and finds fast true global minimum.

Numerical Study of Optimal Location of Non-Circular Segmented Failure Surface in Soil Slope With Weak Soil Layer

The article addresses stability analysis of complicated slopes having weak soil layer sandwiched between two strong layers. The search for critical failure surface and associated optimum/minimum factor of safety (FOS) among all potential failure surfaces can be posed as an optimization problem. Two different variants of particle swarm optimization (PSO) models, namely inertia weight-based PSO (IW-PSO) and contemporary standard PSO (CS-PSO), are used to obtain optimum global solution. Detailed comparison between the global optimum solutions obtained from two PSO variants and the effect of swarm size is studied. The performance of IW-PSO and CS-PSO are studied by observing the convergence behavior of the respective algorithms with respect to iteration count. The influence of velocity clamping on the optimized solution is investigated and its use is found beneficial as it prevents the solution from overflying the region with global best solution. The studies related to swarm diversity demonstrating the exploitation and exploration behaviors of the algorithms are also presented.

A Comparative Study on Different Parameter Factors and Velocity Clamping for Particle Swarm Optimisation

Optimisation is an interesting topic because various optimum results can be obtained using limited resources. This is the reason that researchers are interested in further investigating the possibility of optimisation by testing numerous optimisation methods. As the current research trends are preferred to solve the problems using Artificial Intelligence (AI) methods, hence more nature-inspired optimisation methods are developed. Particles Swarm Optimisation (PSO) is one of the well-known optimisation methods in Swarm Intelligence (SI). It can be used to solve many problems. However, PSO is easily trapped in local optimum and encountered premature convergence problem, which indirectly affects its performance. Besides that, its performance with different parameter factors and velocity clamping is always not considered by researchers. Therefore, the objective of this research is to compare the performance of PSO with different parameter factors and velocity clamping using average fitness values. 10 different types of PSO models are applied and tested using 10 benchmark functions. Based on the experimental results, PSO10 with constriction factor and velocity clamping is proved to be the best PSO model because it can obtain 10 out of 10 minimum average fitness values. The outcomes can assist the researchers while applying the method to their research.

Optimization of non-circular failure surface in slope based on particle swarm models

This article presents results of slope analyses employing a simple technique of generating segmented non-circular failure surfaces. The formulation of slope analysis is performed as an optimization problem with four constrained control variables characterizing the failure surface. In this study, different variants of particle swarm optimization (PSO) methods such as inertia weight equipped and contemporary standard PSO (CS-PSO) are utilized to obtain optimum control variables and associated critical failure surface. The factor of safety of the slope or the objective function of the constrained optimization problem is determined by Morgenstern–Price method. To explore the competence of proposed method with different PSO models, three benchmark problems are analysed for different maximum number of function evaluations such as 1000, 2500 and 5000. A series of sensitivity analyses are performed for various parameters such as swarm size with maximum iteration count of 100. The swarm diversity behaviour is also studied in relation with converged or promising global optimum solution. It is found that contemporary standard PSO (CS-PSO) with or without velocity clamping maintains better swarm diversity and preserves the balance between swarm “exploration” and “exploitation” efficiently. Velocity clamping and positional clamping are recommended as they prevent the solution/control variable from overflying the swarm best position.

Improved PSO Performance using LSTM based Inertia Weight Estimation

Particle Swarm Optimization (PSO) is first introduced in the year 1995. It is mostly an applied population-based meta-heuristic optimization algorithm. PSO is diversely used in the areas of sciences, engineering, technology, medicine, and humanities. Particle Swarm Optimization (PSO) is improved its performance by tuning the inertia weight, topology, velocity clamping. Researchers proposed different Inertia Weight based PSO (IWPSO). Every Inertia Weight based PSO in excelling the existing PSOs. A Long Short Term Memory (LSTM) predicting inertia weight based PSO (LSTMIWPSO) is proposed and its performance is compared with constant, random, and linearly decreasing Inertia Weight PSO. Tests are conducted on swarm sizes 50, 75, and 100 with dimensions 10, 15, and 25. The experimental results show that LSTM based IWPSO supersedes the CIWPSO, RIWPSO, and LDIWPSO.

Plant Species Classification through New Feature Extraction Model-Velocity Clamping Based Intersecting Cortical Model

Plant classification is an active research area. The purpose of our current work is to develop a suitable feature extraction model. This paper suggests a technique to extract the geometric invariants of leaf images using a new velocity clamping based particle swarm optimized intersecting Cortical Model (VCPSO-ICM). Earlier geometric moments were assessed by transforms, separate normalization was used and they were costly. Intersecting cortical model (ICM) is used to avoid the usage of separate normalization for moment invariants of leaf images. In this model, the image is directly processed, as there is no need for preprocessing images. Parameters used in the intersecting cortical model (ICM) are difficult to set for each image separately. This is solved by our model. Time sequences are extracted from each image based on new parameters. Finally, a neural network is preowned to segregate the species of leaf images. This new feature evaluation model is tested on leaf snap database and results are compared with traditional Pulse Coupled neural network (PCNN), simplified Intersecting Cortical Model (ICM).This model achieves a higher accuracy than the existing methods.

An Improved Particle Swarm Optimization Algorithm for Global Multidimensional Optimization

Abstract This paper introduces a new variant of the particle swarm optimization (PSO) algorithm, designed for global optimization of multidimensional functions. The goal of this variant, called ImPSO, is to improve the exploration and exploitation abilities of the algorithm by introducing a new operation in the iterative search process. The use of this operation is governed by a stochastic rule that ensures either the exploration of new regions of the search space or the exploitation of good intermediate solutions. The proposed method is inspired by collaborative human learning and uses as a starting point a basic PSO variant with constriction factor and velocity clamping. Simulation results that show the ability of ImPSO to locate the global optima of multidimensional functions are presented for 10 well-know benchmark functions from CEC-2013 and CEC-2005. These results are compared with the PSO variant used as starting point, three other PSO variants, one of which is based on human learning strategies, and three alternative evolutionary computing methods.

Fully Atomistic Simulations of Protein Unfolding in Low Speed Atomic Force Microscope and Force Clamp Experiments with the Help of Boxed Molecular Dynamics.

The results of boxed dynamics (BXD) fully atomistic simulations of protein unfolding by atomic force microscopy (AFM) in both force clamp (FC) and velocity clamp (VC) modes are reported. In AFM experiments the unfolding occurs on a time scale which is too long for standard atomistic molecular dynamics (MD) simulations, which are usually performed with the addition of forces which exceed those of experiment by many orders of magnitude. BXD can reach the time scale of slow unfolding and sample the very high free energy unfolding pathway, reproducing the experimental dependence of pulling force against extension and extension against time. Calculations show the presence of the pulling force "humps" previously observed in the VC AFM experiments and allow the identification of intermediate protein conformations responsible for them. Fully atomistic BXD simulations can estimate the rate of unfolding in the FC experiments up to the time scale of seconds.

Improvements on particle swarm optimization algorithm for velocity calibration in microseismic monitoring

In this paper, we apply particle swarm optimization (PSO), an artificial intelligence technique, to velocity calibration in microseismic monitoring. We ran simulations with four 1-D layered velocity models and three different initial model ranges. The results using the basic PSO algorithm were reliable and accurate for simple models, but unsuccessful for complex models. We propose the staged shrinkage strategy (SSS) for the PSO algorithm. The SSS-PSO algorithm produced robust inversion results and had a fast convergence rate. We investigated the effects of PSO’s velocity clamping factor in terms of the algorithm reliability and computational efficiency. The velocity clamping factor had little impact on the reliability and efficiency of basic PSO, whereas it had a large effect on the efficiency of SSS-PSO. Reassuringly, SSS-PSO exhibits marginal reliability fluctuations, which suggests that it can be confidently implemented.

Dealing with Boundary Constraint Violations in Particle Swarm Optimization with Aging Leader and Challengers (ALC-PSO)

Boundary violation is a common process in optimization problems. This problem can be seen in Particle Swarm Optimization(PSO) and its variants too. An aging based variant of PSO called, PSO with Aging Leader and Challengers(ALC-PSO) overcomes the stagnation problem that existed in PSO. To avoid the problem of random particles, some bound handling mechanisms need to be applied to Particle Swarm Optimization with Aging Leader and Challengers (ALC-PSO) to improve its performance. During the search process, some particles may leave the search boundaries within which the optimal solution is to be found. It becomes essential to handle such boundary constraint violations and some boundary handling strategies are required to be implemented. This paper presents some of these bound handling methods applied to ALC-PSO algorithm and comparisons are made with PSO. These methods include velocity initialization, velocity clamping and bound handling methods. The results are simulated on MATLAB R2011b for Ackley benchmark problem.

DPSO based on a min-max approach and clamping strategy for the evacuation vehicle assignment problem

Abstract The most challenging task during a flood event is to evacuate people from the affected areas to safer locations. The difficulty of this task is attributed to an uneven distribution of vehicles, a lack of timely assistance, a deficiency in decision making and poor coordination at the operational level. Although it is important to identify flood-prone areas, the assignment of vehicles to the appropriate evacuation routes is of primary importance. The latter consideration is a crucial determinant of the number of people who are saved in any emergency evacuation. This paper proposes an improved discrete particle swarm optimisation (DPSO) algorithm for solving the evacuation vehicle assignment problem (EVAP). Discrete particle positions are proposed that support the implementation of this DPSO. A min-max approach is used for the initial calculations of particle positions for the EVAP. This algorithm was computationally tested using different numbers of potentially flooded areas and compared with an average DPSO approach and with genetic algorithms. The findings of this study reveal that DPSO with a min-max approach that incorporates a new velocity clamping procedure offers good performance with respect to maximising the number of individuals who can be evacuated by vehicles from diverse locations for situations involving various numbers of potential flooded areas.

Integer Discrete Particle Swarm Optimization of Water Distribution Networks

AbstractThe integer discrete particle swarm algorithm is used as an optimization technique for the design of water distribution networks in order to minimize its total cost. Because the particle swarm is highly sensitive to its parameters and boundary conditions, the available restricted boundary conditions are applied. Also, a new boundary condition called the billiard boundary condition is introduced, which does not depend on the velocity clamping that mainly depends on human assumptions. The performance of the boundary conditions are tested under different populations, and a new initialization method by setting the initial position to one side of boundary solutions that is set to the maximum available diameter. The Newton-Raphson method is used as the hydraulic solver. The technique is applied to the optimal design of both the two-loop water distribution network, which is a well-known benchmark in the literature, and to a large-scale previously investigated two-source pipe network. The results show tha...

Probabilistic opposition-based particle swarm optimization with velocity clamping

A probabilistic opposition-based Particle Swarm Optimization algorithm with Velocity Clamping and inertia weights (OvcPSO) is designed for function optimization--to accelerate the convergence speed...

Protein Unfolding and Refolding Under Force: Methodologies for Nanomechanics

An increasing number of inter- and intramolecular interactions can nowadays be probed using single-molecule manipulation techniques. Protein unfolding and refolding is the most representative--though complex--of these interactions. Herein, we review the main modes of performing a force unfolding experiment: the velocity clamp and the new force clamp mode. We also compare some of the physical aspects behind the two most frequently used single-molecule manipulation instrumentations: optical tweezers and atomic force microscopes.

Forcing Chromatin

Forcing Chromatin

Effects of controlling the velocity of shortening on force-velocity-length and time relations in cat papillary muscle. Velocity clamping.

A new technique-velocity clamping-was used to study the effects of a constant total velocity of shortening on the interrelation between force, velocity, length, and time in cat papillary muscle. With decreasing values of clamped total velocity, the duration of the contraction increased but never exceeded the duration of an isometric contraction. Hence these findings confirm that the mode of contraction-isometric or isotonic-has a marked effect on the duration of the contraction. The active state can be prolonged when less shortening occurs and more tension is developed. This phenomenon seems mainly due to the amount of shortening rather than to the velocity of shortening of the muscle. The data were also plotted on a threedimensional graph and compared with velocity-length tracings of afterloaded contractions. The force-length trajectories of the muscle during velocity clamping did not fall on the surface described by the force-velocity-length relations as derived from afterloaded isotonic contractions. This finding could be ascribed to the masking effect of the series elastic component due to the continuous adjusting of the load.