International Journal of Swarm Intelligence (17 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.
Accelerated Grey Wolf Optimizer for Continuous Optimization Problems
by Shubham Gupta, Kusum Deep, Seyedali Mirjalili
Abstract: Grey Wolf Optimizer (GWO) is a relatively simple and efficient nature-inspired optimization algorithm which has shown its competitive performance compared to other population-based meta-heuristics. The leadership hierarchy is the main characteristic of GWO algorithm. This algorithm drives the solutions towards some of the best solutions obtained so far using a unique mathematical model, which is inspired from leadership hierarchy of grey wolves in nature. This is beneficial in improving the accuracy of the current population (exploration), yet it might lead to premature convergence. To combat this issue, an enhanced version of GWO is proposed in this paper for continuous optimization problems. The proposed algorithm is named Accelerated Grey Wolf Optimizer (A-GWO). In A-GWO, novel modified search equations are developed that enhances the exploratory behaviour of wolves at later generations, and the exploitation of search space is also improved in the whole search process. To validate the performance of the proposed algorithm, a set of 23 well-known classical benchmark problems of different categories (unimodal and multimodal) with diverse dimensions (30, 50 and 100) are used. Statistical, convergence and Performance Index (PI) analysis ensures the better performance of the proposed algorithm. The A-GWO algorithm is also tested on five engineering optimization problems to evaluate the performance of the proposed technique. The results show the superiority and reliability of the proposed algorithm.
Keywords: Optimization; Swarm Intelligence; Grey Wolf Optimizer; Engineering optimization test problems.
A Self-Tuning Algorithm to Approximate Roots of Systems of Nonlinear Equations Based on The Firefly Algorithm
by Anuradha Ariyaratne, Gishantha Fernando, Sunethra Weerakoon
Abstract: The most acquainted methods to find root approximations of nonlinear equations and systems; numerical methods possess disadvantages such as necessity of acceptable initial guesses and the differentiability of the functions. Even having such qualities, for some univariate nonlinear equations and systems, approximation of roots is not possible with numerical methods. Research are geared towards finding alternate approaches, which are successful where numerical methods fail. One of the most disadvantageous property in such approaches is inability of finding more than one approximation at a time. On the other hand these methods are incorporated with algorithm specific parameters which should be set properly in order to achieve good results. We present a modified firefly algorithm handling the problem as an optimization problem, which is capable of giving multiple root approximations simultaneously within a reasonable state space while tuning the parameters of the proposed algorithm by itself, using a self-tuning framework. Differentiability and the continuity of the functions and the close initial guesses are needless to solve nonlinear systems using the proposed approach. Benchmark systems found in the literature were used to test the new algorithm. The root approximations and the tuned parameters obtained along with the statistical analysis illustrate the viability of the method.
Keywords: Nature inspired algorithms; Firefly algorithm; Systems of nonlinear equations; parameter tuning.
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).
Spider Monkey Optimization : State-of-the Art and Advances
by Janmenjoy Nayak, Kanithi Vakula, Paidi Dinesh, Bighnaraj Naik
Abstract: Algorithm simulated by the social behavior of understandable agents has become prominent amid the researchers in modern years. Researchers have advanced profuse algorithms by replicating the swarming behavior of different creatures. Spider Monkey Optimization (SMO) algorithm (inspired by the social behavior of spider monkeys) is a novel swarm intelligence based optimization which is a replica of spider monkeys foraging behavior. Spider monkeys have been classified as animals with fusion-fission social structure, where they pursued to split themselves from huge to lesser hordes and vice-versa depends upon the insufficiency or accessibility of food. SMO and its alternatives have been unbeaten and are successful in dealing with difficult authentic world optimization problems due to its elevated effectiveness. Researchers of optimization committee show fervent interest to apply SMO in various domains such as constraint and unconstraint optimization etc. Particular concern has been paid towards the usage level of SMO by the researchers in swarm intelligence related research and applications such as pattern recognition, data mining, image processing etc. This paper depicts a powerful analysis of SMO, its variants, applications, advancements, usage levels and performance issues in various popular yet trending domains with a deep perspective. The key motto behind this analytical point of view is to inspire the practitioners and researchers to innovate new solutions.
Keywords: Swarm intelligence; SMO; Data mining; Classification; Spider monkey.
Special Issue on: Advanced Nature-Inspired Optimisation Techniques for Engineering Applications
Modelling of nature inspired modified Fourier elimination technique for quadratic optimization.
by Sanjay Jain, Adarsh Mangal, Sangeeta .
Abstract: In many decision problems, object occurs in nonlinear nature and expressed as in product form and corresponding optimization problems are quadratic programs of product of two linear functions type. A new technique has been developed to find the solution of quadratic programming problem (QPP) by modelling of modified Fourier elimination technique of inequalities and concept of bounds. The technique is quite useful because the calculations are simple and takes least time then earlier existing methods. The technique has been illustrated by a numerical example also.
Keywords: quadratic programming problem; nature inspired elimination technique; inequalities; nonlinear objective function; constraints.
Application and Development of Improved Meta-Heuristic for making Profitable Bidding Strategy in a Day-Ahead Energy Market under Step-Wise Bidding Scenario
by Pooja Jain, Akash Saxena, Rajesh Kumar
Abstract: An unprecedented concept of restructuring has shaken the structure of electricity industry during the past decades. To sell the produced energy,many generating companies are now forced to prepare and submit daily offers to an electricity market under uncertainty in bid prices submitted by their competitors. To calculate the bid prices optimally and for maximizing the profit of generating company, this paper presents an optimal bidding strategy for a generating company. The company is operating in a day ahead market for single and multi-hourly uniform price multi-unit auctions.In this work, a hybrid model of Whale Optimization Algorithm(WOA) and Sine Cosine Algorithm (SCA), i.e., HWOASCA is proposed to give monte-carlo simulation based solution for strategic bidding problem. By using position update equations of SCA, the feeding characteristics of the whales are improved. First HWOASCA algorithm is validated on 22 standard benchmark test functions then it is applied to bidding problem of 7 GENCO's participating in a dynamically changing electricity market. By suggested approach, the optimal solution for Market Clearing Price (MCP), load dispatch and bid cost under five different capacity and price blocks are calculated. After meaningful comparison from other meta-heuristic techniques, it is observed that the profit obtained by the proposed approach is significantly higher for single-hourly and multi-hourly trading trends.The mathematical and experimental results confirm the supremacy of newly proposed hybrid version of parent algorithms which is highly useful for framing the bidding strategies for a generation company
Keywords: Strategic Bidding; HWOASCA; WOA; SCA ; MCP.
Teaching Learning based Optimization Algorithm: A Survey
by Ruchi Mishra, Nirmala Sharma, Harish Sharma
Abstract: In recent years swarm intelligence (SI) based techniquesrnhave proven their importance for finding the solution ofrnglobal optimization problems. In SI based algorithms agents actrnin a group and learn from each other for food foraging survivingrnetc. Teaching-learning based optimization algorithm (TLBOA) isrnan efficient approach of dealing with linear, nonlinear and multidimensionalrnoptimization problems established by Dr. R.VenkatarnRao in 2011. Since its inception, a lot of research has been carriedrnout to make TLBOA more proficient and to apply it to differentrntypes of optimization problems. This paper presents a review ofrnTLBOA developments, applications, comparative-performance,rnand future research perspectives.
Keywords: Teaching Learning Based Optimization; Nature-rnInspired algorithm; Swarm Intelligence based algorithm; Optimization.
A Novel Control Approach of DC Motor Drive with Optimization Techniques
by Nagendra Swarnkar, Rizwana Khokhar, Mahendra Lalwani
Abstract: Any machine either AC or DC is a backbone of the industry according to their scientific applications or to complete a particular task in industry. Sometimes, industries face the problem due to speed, position, flexibility, reliability and higher cost. To overcome these problems, numbers of controllers are available in industries such as PI controller (Proportional Integral), PD controllers (Proportional Derivatives) and PID controller (Proportional Integral Derivatives). This paper presents PID controller which is simply tuned with the nature-inspired algorithm that gives better dynamic and static performance with high accuracy. The main aim of this paper is to obtain better performance of a DC machine by using PID controller which is tuned with particle swarm optimization (PSO) and ant colony optimization techniques (ACO) algorithms with different error functions such as integral time-weighted absolute error (ITAE), integral square error (ISE) and integral absolute error (IAE).
Keywords: DC motor; proportional integral derivative (PID) controller; performance indices.
Special Issue on: Design, Analysis and Applications of Recent Swarm Intelligence-based Optimisation Algorithms
Landmark operator inspired artificial bee colony algorithm for optimal vector control of induction motor
by Fani Bhushan Sharma, Shashi Raj Kapoor
Abstract: In recent years, soft computing strategies have played a vital role to solve optimization problems associated with the real world. In this paper, an efficient soft computing strategy namely, artificial bee colony algorithm (ABC_algo) is modified with incorporating landmark operator. The proposed modified ABC algorithm is named as land mark inspired ABC (LMABC). The performance of LMABC is evaluated on benchmark functions. Further, the proposed LMABC is applied for vector control of induction motor (IM) and subsequently to improve its efficiency. The vector control of IM includes control of magnitude and phase of each phase current and voltage. In this research paper, the field orientated control, a digital implementation which demonstrates the capability of performing direct torque control, of handling system limitations and of achieving higher power conversion efficiency is considered. The obtained outcomes are significantly better than other state-of-art algorithms available in the literature.
Keywords: Swarm intelligence; Landmark; Induction motor; Metaheuristics; Real world optimization.