Search results for: Improved Particle Swarm Optimization

Authors: M. Senthil Arumugam, M.V.C. Rao

Abstract:

This paper presents a novel two-phase hybrid optimization algorithm with hybrid genetic operators to solve the optimal control problem of a single stage hybrid manufacturing system. The proposed hybrid real coded genetic algorithm (HRCGA) is developed in such a way that a simple real coded GA acts as a base level search, which makes a quick decision to direct the search towards the optimal region, and a local search method is next employed to do fine tuning. The hybrid genetic operators involved in the proposed algorithm improve both the quality of the solution and convergence speed. The phase–1 uses conventional real coded genetic algorithm (RCGA), while optimisation by direct search and systematic reduction of the size of search region is employed in the phase – 2. A typical numerical example of an optimal control problem with the number of jobs varying from 10 to 50 is included to illustrate the efficacy of the proposed algorithm. Several statistical analyses are done to compare the validity of the proposed algorithm with the conventional RCGA and PSO techniques. Hypothesis t – test and analysis of variance (ANOVA) test are also carried out to validate the effectiveness of the proposed algorithm. The results clearly demonstrate that the proposed algorithm not only improves the quality but also is more efficient in converging to the optimal value faster. They can outperform the conventional real coded GA (RCGA) and the efficient particle swarm optimisation (PSO) algorithm in quality of the optimal solution and also in terms of convergence to the actual optimum value.

Keywords: Hybrid systems, optimal control, real coded genetic algorithm (RCGA), Particle swarm optimization (PSO), Hybrid real coded GA (HRCGA), and Hybrid genetic operators.

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Authors: Hyeongbok Kim, Lingling Zhao, Xiaohong Su, Junjie Wang

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The Bernoulli filter is a precise Bayesian filter for single target tracking based on the random finite set theory. The standard Bernoulli filter often underestimates the number of the targets. This study proposes a Gaussian particle flow (GPF) Bernoulli filter employing particle flow to migrate particles from prior to posterior positions to improve the performance of the standard Bernoulli filter. By employing the particle flow filter, the computational speed of the Bernoulli filters is significantly improved. In addition, the GPF Bernoulli filter provides more accurate estimation compared with that of the standard Bernoulli filter. Simulation results confirm the improved tracking performance and computational speed in two- and three-dimensional scenarios compared with other algorithms.

Keywords: Bernoulli filter, particle filter, particle flow filter, random finite sets, target tracking.

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Authors: S. Jalilzadeh, A. Kimiyaghalam, A. Ashouri

Abstract:

This paper presents a mathematical model and a methodology to analyze the losses in transmission expansion planning (TEP) under uncertainty in demand. The methodology is based on discrete particle swarm optimization (DPSO). DPSO is a useful and powerful stochastic evolutionary algorithm to solve the large-scale, discrete and nonlinear optimization problems like TEP. The effectiveness of the proposed idea is tested on an actual transmission network of the Azerbaijan regional electric company, Iran. The simulation results show that considering the losses even for transmission expansion planning of a network with low load growth is caused that operational costs decreases considerably and the network satisfies the requirement of delivering electric power more reliable to load centers.

Keywords: DPSO, TEP, Uncertainty

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Authors: N. Manavizadeh, M. Hosseini, M. Rabbani

Abstract:

In the last decades to supply the various and different demands of clients, a lot of manufacturers trend to use the mixedmodel assembly line (MMAL) in their production lines, since this policy make possible to assemble various and different models of the equivalent goods on the same line with the MTO approach. In this article, we determine the sequence of (MMAL) line, with applying the kitting approach and planning of rest time for general workers to reduce the wastages, increase the workers effectiveness and apply the sector of lean production approach. This Multi-objective sequencing problem solved in small size with GAMS22.2 and PSO meta heuristic in 10 test problems and compare their results together and conclude that their results are very similar together, next we determine the important factors in computing the cost, which improving them cost reduced. Since this problem, is NPhard in large size, we use the particle swarm optimization (PSO) meta-heuristic for solving it. In large size we define some test problems to survey it-s performance and determine the important factors in calculating the cost, that by change or improved them production in minimum cost will be possible.

Keywords: Mixed-Model Assembly Line, particle swarmoptimization, Multi-objective sequencing problem, MTO system, kitto-assembly, rest time

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Authors: Tung-Sheng Zhan, Chih-Cheng Kao, Chin-Der Yang, Jong-Ian Tsai

Abstract:

Fossil fuel-firing power plants dominate electric power generation in Taiwan, which are also the major contributor to Green House gases (GHG). CO2 is the most important greenhouse gas that cause global warming. This paper penetrates the relationship between carbon trading for GHG reduction and power generation expansion planning (GEP) problem for the electrical utility. The Particle Swarm Optimization (PSO) Algorithm is presented to deal with the generation expansion planning strategy of the utility with independent power providers (IPPs). The utility has to take both the IPPs- participation and environment impact into account when a new generation unit is considering expanded from view of supply side.

Keywords: Carbon Trading, CO2 Emission, GenerationExpansion Planning (GEP), Green House gases (GHG), ParticleSwarm Optimization (PSO).

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Authors: Yendiyarov Sergei, Zobnin Boris, Petrushenko Sergei

Abstract:

Usually, the solid-fuel flow of an iron ore sinter plant consists of different types of the solid-fuels, which differ from each other. Information about the composition of the solid-fuel flow usually comes every 8-24 hours. It can be clearly seen that this information cannot be used to control the sintering process in real time. Due to this, we propose an expert system which uses indirect measurements from the process in order to obtain the composition of the solid-fuel flow by solving an optimization task. Then this information can be used to control the sintering process. The proposed technique can be successfully used to improve sinter quality and reduce the amount of solid-fuel used by the process.

Keywords: sintering process, particle swarm optimization, optimal control, expert system, solid-fuel

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Authors: Y. Harold Robinson, M. Rajaram, Honey Raju

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Image segmentation and color identification is an important process used in various emerging fields like intelligent robotics. A method is proposed for the manipulator to grasp and place the color object into correct location. The existing methods such as PSO, has problems like accelerating the convergence speed and converging to a local minimum leading to sub optimal performance. To improve the performance, we are using watershed algorithm and for color identification, we are using EPSO. EPSO method is used to reduce the probability of being stuck in the local minimum. The proposed method offers the particles a more powerful global exploration capability. EPSO methods can determine the particles stuck in the local minimum and can also enhance learning speed as the particle movement will be faster.

Keywords: Color information, EPSO, hue, saturation, value (HSV), image segmentation, particle swarm optimization (PSO). Active Contour, GMM.

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Authors: Yizhi Gai, Yukinori Kobayashi, Yohei Hoshino, Takanori Emaru

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Motion control of flexible arms is more difficult than that of rigid arms, however utilizing its dynamics enables improved performance such as a fast motion in short operation time. This paper investigates a ball throwing robot with one rigid link and one flexible link. This robot throws a ball at a set speed with a proper control torque. A mathematical model of this ball throwing robot is derived through Hamilton’s principle. Several patterns of torque input are designed and tested through the proposed simulation models. The parameters of each torque input pattern is optimized and determined by chaos embedded vector evaluated particle swarm optimization (CEVEPSO). Then, the residual vibration of the manipulator after throwing is suppressed with input shaping technique. Finally, a real experiment is set up for the model checking.

Keywords: Motion control, flexible robotic arm, CEVEPSO, ball throwing robot.

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Authors: Morteza Kazemi Torbaghan, Seyed Mehran Kazemi, Rahele Zhiani, Fakhriye Hamed

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Truss optimization problem has been vastly studied during the past 30 years and many different methods have been proposed for this problem. Even though most of these methods assume that the design variables are continuously valued, in reality, the design variables of optimization problems such as cross-sectional areas are discretely valued. In this paper, an improved hill climbing and an improved simulated annealing algorithm have been proposed to solve the truss optimization problem with discrete values for crosssectional areas. Obtained results have been compared to other methods in the literature and the comparison represents that the proposed methods can be used more efficiently than other proposed methods

Keywords: Size Optimization of Trusses, Hill Climbing, Simulated Annealing.

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Authors: T. Vigneswari, M. A. Maluk Mohamed

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Job Scheduling plays an important role for efficient utilization of grid resources available across different domains and geographical zones. Scheduling of jobs is challenging and NPcomplete. Evolutionary / Swarm Intelligence algorithms have been extensively used to address the NP problem in grid scheduling. Artificial Bee Colony (ABC) has been proposed for optimization problems based on foraging behaviour of bees. This work proposes a modified ABC algorithm, Cluster Heterogeneous Earliest First Min- Min Artificial Bee Colony (CHMM-ABC), to optimally schedule jobs for the available resources. The proposed model utilizes a novel Heterogeneous Earliest Finish Time (HEFT) Heuristic Algorithm along with Min-Min algorithm to identify the initial food source. Simulation results show the performance improvement of the proposed algorithm over other swarm intelligence techniques.

Keywords: Grid Computing, Grid Scheduling, Heterogeneous Earliest Finish Time (HEFT), Artificial Bee colony (ABC) Algorithm, Resource Management.

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Authors: Sana Testouri, Karim Saadaoui, Mohamed Benrejeb

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This paper presents a Particle Swarm Optimization (PSO) method for determining the optimal parameters of a first-order controller for TCP/AQM system. The model TCP/AQM is described by a second-order system with time delay. First, the analytical approach, based on the D-decomposition method and Lemma of Kharitonov, is used to determine the stabilizing regions of a firstorder controller. Second, the optimal parameters of the controller are obtained by the PSO algorithm. Finally, the proposed method is implemented in the Network Simulator NS-2 and compared with the PI controller.

Keywords: AQM, first-order controller, time delay, stability, PSO.

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Authors: Seon-Ho Yoon, Jin-Young Choi, Dong-Jun Won

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This paper presents hierarchical operation strategies which are minimizing operation error between day ahead operation plan and real time operation. Operating power systems between centralized and decentralized approaches can be represented as hierarchical control scheme, featured as primary control, secondary control and tertiary control. Primary control is known as local control, featuring fast response. Secondary control is referred to as microgrid Energy Management System (EMS). Tertiary control is responsible of coordinating the operations of multi-microgrids. In this paper, we formulated 3 stage microgrid operation strategies which are similar to hierarchical control scheme. First stage is to set a day ahead scheduled output power of Battery Energy Storage System (BESS) which is only controllable source in microgrid and it is optimized to minimize cost of exchanged power with main grid using Particle Swarm Optimization (PSO) method. Second stage is to control the active and reactive power of BESS to be operated in day ahead scheduled plan in case that State of Charge (SOC) error occurs between real time and scheduled plan. The third is rescheduling the system when the predicted error is over the limited value. The first stage can be compared with the secondary control in that it adjusts the active power. The second stage is comparable to the primary control in that it controls the error in local manner. The third stage is compared with the secondary control in that it manages power balancing. The proposed strategies will be applied to one of the buildings in Electronics and Telecommunication Research Institute (ETRI). The building microgrid is composed of Photovoltaic (PV) generation, BESS and load and it will be interconnected with the main grid. Main purpose of that is minimizing operation cost and to be operated in scheduled plan. Simulation results support validation of proposed strategies.

Keywords: Battery energy storage system, energy management system, microgrid, particle swarm optimization.

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Authors: Afzeri, R. Muhida, Darmawan, A. N. Berahim

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Sharing the manufacturing facility through remote operation and monitoring of a machining process is challenge for effective use the production facility. Several automation tools in term of hardware and software are necessary for successfully remote operation of a machine. This paper presents a prototype of workpiece holding attachment for remote operation of milling process by self configuration the workpiece setup. The prototype is designed with mechanism to reorient the work surface into machining spindle direction with high positioning accuracy. Variety of parts geometry is hold by attachment to perform single setup machining. Pin type with array pattern additionally clamps the workpiece surface from two opposite directions for increasing the machining rigidity. Optimum pins configuration for conforming the workpiece geometry with minimum deformation is determined through hybrid algorithms, Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). Prototype with intelligent optimization technique enables to hold several variety of workpiece geometry which is suitable for machining low of repetitive production in remote operation.

Keywords: Optimization, Remote machining, GeneticAlgorithms, Machining Fixture.

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Authors: K. Abbaspoursani, M. Allahyari, M. Rahmani

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Thermal conductivity is an important characteristic of a nanofluid in laminar flow heat transfer. This paper presents an improved model for the prediction of the effective thermal conductivity of nanofluids based on dimensionless groups. The model expresses the thermal conductivity of a nanofluid as a function of the thermal conductivity of the solid and liquid, their volume fractions and particle size. The proposed model includes a parameter which accounts for the interfacial shell, brownian motion, and aggregation of particle. The validation of the model is verified by applying the results obtained by the experiments of Tio2-water and Al2o3-water nanofluids.

Keywords: Critical particle size, nanofluid, model, and thermal conductivity.

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Authors: Tarek Aboueldah, Hanan Farag

Abstract:

Parallel Job Shop Scheduling Problem (JSSP) is a multi-objective and multi constrains NP-optimization problem. Traditional Artificial Intelligence techniques have been widely used; however, they could be trapped into the local minimum without reaching the optimum solution. Thus, we propose a hybrid Artificial Intelligence (AI) model with Discrete Breeding Swarm (DBS) added to traditional AI to avoid this trapping. This model is applied in the cost minimization of the Car Sequencing and Operator Allocation (CSOA) problem. The practical experiment shows that our model outperforms other techniques in cost minimization.

Keywords: Parallel Job Shop Scheduling Problem, Artificial Intelligence, Discrete Breeding Swarm, Car Sequencing and Operator Allocation, cost minimization.

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Authors: Jai Raj, Jito Vanualailai, Bibhya Sharma, Shonal Singh

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This paper proposes a solution to the motion planning and control problem of car-like mobile robots which is required to move safely to a designated target in a priori known workspace cluttered with swarm of boids exhibiting collective emergent behaviors. A generalized algorithm for target convergence and swarm avoidance is proposed that will work for any number of swarms. The control laws proposed in this paper also ensures practical stability of the system. The effectiveness of the proposed control laws are demonstrated via computer simulations of an emergent behavior.

Keywords: Swarm, practical stability, motion planning, emergent.

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Authors: Blesson Varghese, Gerard T. McKee

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The work reported in this paper is motivated by the fact that there is a need to apply autonomic computing concepts to parallel computing systems. Advancing on prior work based on intelligent cores [36], a swarm-array computing approach, this paper focuses on 'Intelligent agents' another swarm-array computing approach in which the task to be executed on a parallel computing core is considered as a swarm of autonomous agents. A task is carried to a computing core by carrier agents and is seamlessly transferred between cores in the event of a predicted failure, thereby achieving self-ware objectives of autonomic computing. The feasibility of the proposed swarm-array computing approach is validated on a multi-agent simulator.

Keywords: Autonomic computing, intelligent agents, swarm-array computing.

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Authors: Saeed Sedighizadeh, Abbas Mansoori, Mohammad Reza Pirestani, Davoud Sedighizadeh

Abstract:

The usual method of river flow diversion involves construction of tunnels and cofferdams. Given the fact that the cost of diversion works could be as high as 10-20% of the total dam construction cost, due attention should be paid to optimum design of the diversion works. The cost of diversion works depends, on factors, such as: the tunnel dimensions and the intended tunneling support measures during and after excavation; quality and characterizes of the rock through which the tunnel should be excavated; the dimensions of the upstream (and downstream) cofferdams; and the magnitude of river flood the system is designed to divert. In this paper by use of the cost of unit prices for tunnel excavation, tunnel lining, tunnel support (rock bolt + shotcrete) and cofferdam fill the cost function was determined. The function is then minimized by the aid of PSO Algorithm (particle swarm optimization). It is found that the optimum diameter and the total diversion cost are directly related to the river flood discharge (Q). It has also shown that in addition to optimum diameter design discharge (Q), river length, tunnel length, is mainly a function of the ratios (not the absolute values) of the unit prices and does not depend on the overall price levels in the respective country. The results of optimization use in some of the case study lead us to significant changes in the cost.

Keywords: Diversion Tunnel, Optimization, PSO Algorithm

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Authors: Bibhya Sharma, Jito Vanualailai, Jai Raj

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This paper proposes a new obstacle and collision avoidance control laws for a three-dimensional swarm of boids. The swarm exhibit collective emergent behaviors whilst avoiding the obstacles in the workspace. While flocking, animals group up in order to do various tasks and even a greater chance of evading predators. A generalized algorithms for attraction to the centroid, inter-individual swarm avoidance and obstacle avoidance is designed in this paper. We present a set of new continuous time-invariant velocity control laws is presented which is formulated via the Lyapunov-based control scheme. The control laws proposed in this paper also ensures practical stability of the system. The effectiveness of the proposed control laws is demonstrated via computer simulations

 

Keywords: Lyapunov-based Control Scheme, Motion planning, Practical stability, Swarm.

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Authors: P.-W. Tsai, J.-W. Chen, C.-W. Chen, C.-Y. Chen

Abstract:

In recent years, many researchers are involved in the field of fuzzy theory. However, there are still a lot of issues to be resolved. Especially on topics related to controller design such as the field of robot, artificial intelligence, and nonlinear systems etc. Besides fuzzy theory, algorithms in swarm intelligence are also a popular field for the researchers. In this paper, a concept of utilizing one of the swarm intelligence method, which is called Bacterial-GA Foraging, to find the stabilized common P matrix for the fuzzy controller system is proposed. An example is given in in the paper, as well.

Keywords: Half-circle fuzzy numbers, predictions, swarm intelligence, Lyapunov method.

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Authors: A. Safari, H. Shayeghi, H. A. Shayanfar

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On the basis of the linearized Phillips-Herffron model of a single-machine power system, a novel method for designing unified power flow controller (UPFC) based output feedback controller is presented. The design problem of output feedback controller for UPFC is formulated as an optimization problem according to with the time domain-based objective function which is solved by iteration particle swarm optimization (IPSO) that has a strong ability to find the most optimistic results. To ensure the robustness of the proposed damping controller, the design process takes into account a wide range of operating conditions and system configurations. The simulation results prove the effectiveness and robustness of the proposed method in terms of a high performance power system. The simulation study shows that the designed controller by Iteration PSO performs better than Classical PSO in finding the solution.

Keywords: UPFC, IPSO, output feedback Controller.

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Authors: S. Jamshidi, E. Feizi Ashtiani, M. Ardestani

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Waste Load Allocation (WLA) strategies usually intend to find economic policies for water resource management. Water quality trading (WQT) is an approach that uses discharge permit market to reduce total environmental protection costs. This primarily requires assigning discharge limits known as total maximum daily loads (TMDLs). These are determined by monitoring organizations with respect to the receiving water quality and remediation capabilities. The purpose of this study is to compare two approaches of TMDL assignment for WQT policy in small catchment area of Haraz River, in north of Iran. At first, TMDLs are assigned uniformly for the whole point sources to keep the concentrations of BOD and dissolved oxygen (DO) at the standard level at checkpoint (terminus point). This was simply simulated and controlled by Qual2kw software. In the second scenario, TMDLs are assigned using multi objective particle swarm optimization (MOPSO) method in which the environmental violation at river basin and total treatment costs are minimized simultaneously. In both scenarios, the equity index and the WLA based on trading discharge permits (TDP) are calculated. The comparative results showed that using economically optimized TMDLs (2nd scenario) has slightly more cost savings rather than uniform TMDL approach (1st scenario). The former annually costs about 1 M$ while the latter is 1.15 M$. WQT can decrease these annual costs to 0.9 and 1.1 M$, respectively. In other word, these approaches may save 35 and 45% economically in comparison with command and control policy. It means that using multi objective decision support systems (DSS) may find more economical WLA, however its outcome is not necessarily significant in comparison with uniform TMDLs. This may be due to the similar impact factors of dischargers in small catchments. Conversely, using uniform TMDLs for WQT brings more equity that makes stakeholders not feel that much envious of difference between TMDL and WQT allocation. In addition, for this case, determination of TMDLs uniformly would be much easier for monitoring. Consequently, uniform TMDL for TDP market is recommended as a sustainable approach. However, economical TMDLs can be used for larger watersheds.

Keywords: Waste load allocation (WLA), Water quality trading (WQT), Total maximum daily loads (TMDLs), Haraz River, Multi objective particle swarm optimization (MOPSO), Equity.

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Authors: Ramdan B. A. Koad, Ahmed. F. Zobaa

Abstract:

Since the output characteristics of photovoltaic (PV) system depends on the ambient temperature, solar radiation and load impedance, its maximum power point (MPP) is not constant. Under each condition PV module has a point at which it can produce its MPP. Therefore, a maximum power point tracking (MPPT) method is needed to uphold the PV panel operating at its MPP. This paper presents comparative study between the conventional MPPT methods used in (PV) system: Perturb and Observe (P&O), Incremental Conductance (IncCond), andParticle Swarm Optimization (PSO) algorithmfor (MPPT) of (PV) system. To evaluate the study, the proposed PSO MPPT is implemented on a DC-DC cuk converter and has been compared with P&O and INcond methods in terms of their tracking speed, accuracy and performance by using the Matlab tool Simulink. The simulation result shows that the proposed algorithm is simple, and is superior to the P&O and IncCond methods.

Keywords: Incremental Conductance (IncCond) Method, Perturb and Observe (P&O) Method, Photovoltaic Systems (PV) and Practical Swarm Optimization Algorithm (PSO).

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Authors: S. Park, J. Hwang, K. Rou, E. Kim

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In this paper, we consider a new particle filter inspired by biological evolution. In the standard particle filter, a resampling scheme is used to decrease the degeneracy phenomenon and improve estimation performance. Unfortunately, however, it could cause the undesired the particle deprivation problem, as well. In order to overcome this problem of the particle filter, we propose a novel filtering method called the genetic filter. In the proposed filter, we embed the genetic algorithm into the particle filter and overcome the problems of the standard particle filter. The validity of the proposed method is demonstrated by computer simulation.

Keywords: Particle filter, genetic algorithm, evolutionary algorithm.

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Authors: Zhu Xiong-feng, Hou Zhong-xi, Guo Zheng, Liu Zhao-Wei

Abstract:

A method of dynamic mesh based airfoil optimization is proposed according to the drawbacks of surrogate model based airfoil optimization. Programs are designed to achieve the dynamic mesh. Boundary condition is add by integrating commercial software Pointwise, meanwhile the CFD calculation is carried out by commercial software Fluent. The data exchange and communication between the software and programs referred above have been accomplished, and the whole optimization process is performed in iSIGHT platform. A simplified airfoil optimization study case is brought out to show that aerodynamic performances of airfoil have been significantly improved, even save massive repeat operations and increase the robustness and credibility of the optimization result. The case above proclaims that dynamic mesh based airfoil optimization is an effective and high efficient method.

Keywords: unmanned air vehicles, dynamic mesh, airfoil optimization, CFD, genetic algorithm

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Authors: Reza Sirjani, Nobosse Tafem Bolan

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Many optimization techniques available in the literature have been developed in order to solve the problem of voltage stability enhancement in power systems. However, there are a number of drawbacks in the use of previous techniques aimed at determining the optimal location and size of reactive compensators in a network. In this paper, an Improved Cuckoo Search algorithm is applied as an appropriate optimization algorithm to determine the optimum location and size of a Static Var Compensator (SVC) in a transmission network. The main objectives are voltage stability improvement and total cost minimization. The results of the presented technique are then compared with other available optimization techniques.

Keywords: Cuckoo search algorithm, optimization, power system, var compensators, voltage stability.

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Authors: Vahid Mohammadi Saffarzadeh, Pourya Jafarzadeh, Masoud Mazloom

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This paper presents a hybrid approach for solving nqueen problem by combination of PSO and SA. PSO is a population based heuristic method that sometimes traps in local maximum. To solve this problem we can use SA. Although SA suffer from many iterations and long time convergence for solving some problems, By good adjusting initial parameters such as temperature and the length of temperature stages SA guarantees convergence. In this article we use discrete PSO (due to nature of n-queen problem) to achieve a good local maximum. Then we use SA to escape from local maximum. The experimental results show that our hybrid method in comparison of SA method converges to result faster, especially for high dimensions n-queen problems.

Keywords: PSO, SA, N-queen, CSP

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Authors: Abdussamad Ismail, Dong-Sheng Jeng

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A self-evolution algorithm for optimizing neural networks using a combination of PSO and JPSO is proposed. The algorithm optimizes both the network topology and parameters simultaneously with the aim of achieving desired accuracy with less complicated networks. The performance of the proposed approach is compared with conventional back-propagation networks using several synthetic functions, with better results in the case of the former. The proposed algorithm is also implemented on slope stability problem to estimate the critical factor of safety. Based on the results obtained, the proposed self evolving network produced a better estimate of critical safety factor in comparison to conventional BPN network.

Keywords: Neural networks, Topology evolution, Particle swarm optimization.

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Authors: Sana Bouzaida, Anis Sakly, Faouzi M'Sahli

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In this paper, a TSK-type Neuro-fuzzy Inference System that combines the features of fuzzy sets and neural networks has been applied for the identification of MIMO systems. The procedure of adapting parameters in TSK model employs a Shuffled Frog Leaping Algorithm (SFLA) which is inspired from the memetic evolution of a group of frogs when seeking for food. To demonstrate the accuracy and effectiveness of the proposed controller, two nonlinear systems have been considered as the MIMO plant, and results have been compared with other learning methods based on Particle Swarm Optimization algorithm (PSO) and Genetic Algorithm (GA).

Keywords: Identification, Shuffled frog Leaping Algorithm (SFLA), TSK-type neuro-fuzzy model.

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Authors: Bibhya Sharma, Jito Vanualailai, Jai Raj

Abstract:

In this paper, we propose a solution to the motion planning and control problem for a swarm of three-dimensional boids. The swarm exhibit collective emergent behaviors within the vicinity of the workspace. The capability of biological systems to autonomously maneuver, track and pursue evasive targets in a cluttered environment is vastly superior to any engineered system. It is considered an emergent behavior arising from simple rules that are followed by individuals and may not involve any central coordination. A generalized, yet scalable algorithm for attraction to the centroid and inter-individual swarm avoidance is proposed. We present a set of new continuous time-invariant velocity control laws, formulated via the Lyapunov-based control scheme for target attraction and collision avoidance. The controllers provide a collision-free trajectory. The control laws proposed in this paper also ensures practical stability of the system. The effectiveness of the control laws is demonstrated via computer simulations.

Keywords: Swarm, Practical stability, Motion planning.

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