International Journal of Swarm Intelligence (9 papers in press)
by Marie-Laure Bouchet
Abstract: Love sak
Keywords: sake; japan;matsuri.
This article is being considered for Open Access
Improving The Cooperation of Fuzzy Simplified Memory A* Search and Particle Swarm Optimization for path planning
by Mehdi Neshat
Abstract: Problem solving is a very important subject in the world of AI. In fact, a problem can be considered one or more goals along with a set of available interactions for reaching those goals. One of the best ways of solving AI problems is to use search methods. The SMA* (Simplified Memory Bounded A*) is one of the best methods of informed search. In this research, a hybrid method was proposed to increase the performance of SMA* search. The combining fuzzy logic with this search method and improving it with PSO algorithm brought satisfactory results. The use of fuzzy logic leads to increase the search flexibility especially when a robot dealing with lots of barriers and path changes. Furthermore, combining PSO saves the search from being trapped into local optimums and provides for search some correct and accurate suggestions. In the proposed algorithm, the results indicate that the cost of search and branching factor are decreased in comparison with other methods.rnrn
Keywords: : Informed Search; Fuzzy Logic; Particle Swarm Optimization;Simplified Memory Bounded A*; Robot Navigation.
Special Issue on: Computational Intelligence
Fractional Order Ant Colony Control with Genetic Algorithm based Initialization
by Ambreesh Kumar, Varun Upadhaya, Ayush Singh, Paras Pandey, Rajneesh Sharma
Abstract: Initializing parameters of the Ant Colony Optimization (ACO) presents a formidable research lacuna in the design of ACO based control. So far, initial ACO parameters have been set using a trial and error procedure and sometimes with some heuristics. However, these approaches lead to slow convergence and sometimes even divergence of the designed fractional order controller. Our proposed approach advocates a genetic algorithm (GA) based initialization of the ACO parameters for efficient and effective design of the ACO based fractional order controllers. We minimize a multi objective function via a nested GA technique for designing the GA based ACO fractional order PID controller. This brings in an element of certainty in the performance of the designed ACO controller. The controller has been validated on seven fractional order systems with comparative evaluation against: a) ACO control and, b) GA based fractional order control. Our GA based ACO controller is able to garner better transient response parameters (rise time, peak overshoot) along with an excellent steady state performance as signified by (settling time, ITAE). A flip side though, is higher computational complexity (easily tackled by high performance machines) of our approach which is mitigated to a certain extent by its superior performance.
Keywords: Genetic Algorithm; Ant Colony Control; Fractional Order system; GA based ACO.
Optimizing Fracture in Automotive Tail Cap by Firefly Algorithm
by Ganesh Kakandikar, Omkar Kulkarni, Sujata Patekar, Trupti Bhoskar
Abstract: Deep drawing is a manufacturing process in which sheet metal is progressively formed into a three-dimensional shape through the mechanical action of a die forming the metal around a punch. The deep drawing process contains many components and steps. Pots, pans for cooking, containers, sinks, automobile parts such as panels and gas tanks are among a few of the items manufactured by sheet metal deep drawing. The Predominant failure modes in sheet metal parts (deep drawing process) are fracture. The prediction and prevention of fracture are extremely important in the design of tooling and process parameters in deep drawing process. Fracture or necking occurs in a drawn part, which is under excessive tensile stresses. Fractures are the important defects in deep drawing operation, which can be prevented using blank holding force. Fracture limit depends on various tooling, process and material parameters. Firefly algorithm is one of the evolutionary optimization algorithms, and is inspired by fireflies behaviour in nature. Each firefly movement is based on absorption of the other one.
Keywords: Firefly algorithm; deep Drawing Process; Optimization; Fracture.
Special Issue on: Soft Computing Techniques for Engineering Applications
Trajectory planning of an Autonomous Mobile Robot
by Suvranshu Pattanayak, Bibhuti Bhusan Choudhury
Abstract: The latest moves in trajectory planning for autonomous mobile robots are directed towards a popular investigation work. This paper introduces modified particle swarm optimization technique called as APSO (Adaptive particle swarm optimization) and PSO for trajectory length optimization. For estimating the trajectory length of the robot, nine numbers of obstacles is selected between start and goal point in a static environment. Lastly a comparison is established between these two approaches, to identify the approach that affords shortest trajectory length in a least computation time and shortest possible travel time. Simulation result shows that APSO contributes towards curtail trajectory length at a lesser computational and travel time as compared to PSO.
Keywords: Autonomous Mobile Robot; PSO; APSO.
Improved pole-placement for adaptive pitch control
by SHRABANI SAHU, Sasmita Behera
Abstract: This paper presents an improved technique to regulate the pitch angle of a wind turbine benchmark model (WTBM) implemented in MATLAB SIMULINK environment. As the model is nonlinear in nature, to accomplish the desired power production level in the constant power region, an adaptive controller is implemented. It takes care of the pitch control with online estimates of the plant parameters that are susceptible to change due to disturbances. Here, the controller design is based on the pole-placement methodology for a self-tuning controller (STC). Location of the desired pair of poles is defined by the damping factor and natural frequency . The selection of these parameters is performed by utilizing particle swarm optimization (PSO), constriction factor based PSO (CFBPSO), genetic algorithm (GA), modified grey wolf optimization (MGWO) and improved sine cosine algorithm (ISCA) and the results are put side by side for a consistent set of algorithm parameters. A Monte Carlo Simulation has been carried out for comparison of the algorithms. The achieved results show the improvement in performance by employing ISCA for pole-placement of an adaptive STC controller.
Keywords: Pitch control; adaptive control; pole placement; PSO; GA; MGWO; ISCA.
Enhanced electromagnetic swarm yields better optimization
by Srikanth Kavirayani
Abstract: Swarm Intelligence has been one of the leading techniques used by researchers worldwide for optimization. In this paper, the fine tuning of the update equations for the swarm are done based on linkage of particle motion with a electromagnetic field and also under the influence of strategic delays. The motion of a particle in a search space is confined to free space in general, however if restricted the solution under the envelope of a magnetic field, the algorithm better converges within a electromagnetic field. The studies are then applied to the triple inverted pendulum case study which showed that stability was achieved with ease when compared to classical methods of control.
Keywords: Particle swarm; magnetic fields; Time delay; Efficiency ; Triple Inverted Pendulum.
Implementation of Grasshopper optimization algorithm for closed loop speed control a BLDC motor drive
by Devendra Potnuru, Ayyarao S.L.V. Tummala
Abstract: This paper presents a recently proposed Grasshopper Algorithm for speed control of BLDC motor drive in closed loop. The main objective of this paper is to obtain optimal PID gains of speed controller at different operating conditions. The efficient PID tuning is based on minimization of integral square error which is the objective function of this optimization problem. The PID controller is used for speed control of the BLDC motor drive. The drive has been simulated in MATLAB/Simulink environment and is tested at different reference speeds.
Keywords: BLDC; Grasshopper Optimization; PID tuning; Speed controller.
Black Hole Optimized Cascade Proportional Derivative-Proportional Integral Derivative Controller for Frequency Regulation in Hybrid Distributed Power System
by G.T. Chandrasekhar, Ramana Pilla
Abstract: This manuscript presents a novel Black Hole Optimized (BHO) Proportional Derivative- Proportional Integral Derivative controller (PD-PID) is provided for the optimal solution of the frequency regulation of hybrid power system. At first, a two area power system is considered in which area-1 having thermal, distributed units and in area-2 includes thermal, hydel and nuclear units. Appropriate nonlinearities such boiler dynamics, governor dead band (GDB) and generation rate constraint (GRC) are considered. In the next step, PD-PID controller is considered as a secondary controller and its preeminence is shown by comparing with Proportional Integral Derivate (PID) and Proportional Integral Double Derivate (PIDD) controllers for the same model having Integral Time multiplied Absolute Error (ITAE) as an error function. Finally, sensitivity of the proposed controller is investigated over a wide variation of system parameters and loading condition. For more examination of the proposed controller is also analyzed under random step load and sinusoidal disturbances.
Keywords: Automatic Generation Control (AGC); Black Hole Optimization (BHO); Boiler dynamics; Distributed Power System; Integral Time multiplied Absolute Error (ITAE).