Optimization Techniques with FORTRAN

Grey wolf optimizer (GWO) is one of recent metaheuristics swarm intelligence methods. It has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods: it has very few parameters, and no derivation information is required in the initial search. Also it is simple, easy to use, flexible, scalable, and has a special capability to strike the right balance between the exploration and exploitation during the search which leads to favourable convergence. Therefore, the GWO has recently gained a very big research interest with tremendous audiences from several domains in a very short time. Thus, in this review paper, several research publications using GWO have been overviewed and summarized. Initially, an introductory information about GWO is provided which illustrates the natural foundation context and its related optimization conceptual framework. The main operations of GWO are procedurally discussed, and the theoretical foundation is described. Furthermore, the recent versions of GWO are discussed in detail which are categorized into modified, hybridized and paralleled versions. The main applications of GWO are also thoroughly described. The applications belong to the domains of global optimization, power engineering, bioinformatics, environmental applications, machine learning, networking and image processing, etc. The open source software of GWO is also provided. The review paper is ended by providing a summary conclusion of the main foundation of GWO and suggests several possible future directions that can be further investigated.

Grey wolf optimizer: a review of recent variants and applications

System reduction using factor division algorithm and eigen spectrum analysis

Local extrema co-occurrence pattern for color and texture image retrieval

A mixed method is proposed for finding stable reduced order models of single-input- single-output large-scale systems using Pade approximation and the clustering technique. The denominator polynomial of the reduced order model is determined by forming the clusters of the poles of the original system, and the coefficients of numerator polynomial are obtained by using the Pade approximation technique. This method guarantees stability of the reduced order model when the original high order system is stable. The methodology of the proposed method is illustrated with the help of examples from literature.

Clustering Method for Reducing Order of Linear System using Pade Approximation

Design of controllers with arbitrary convergence time

In the year 1995, Dr R.C. Eberhart, who was an electrical engineer, along with Dr. James Kennedy, a social psycologist invented a random optimization technique which a was later named as Particle Swarm Optimization. As the name itself asserts that this method draws inspiration from natural biotic life of swarms of flocks. It uses the same principle to find most optimal solution to problem in search space as birds do find their most suitable place in a flock or insects do in a swarm. The PSO algorithm is initialized with a horde of particles which are a collection of random feasible solutions. Every single particle in the swarm is initialised a random velocity and as soon as they are assigned a velocity these particles start moving in problem search space. Now from this space the algorithm draws the particle to most suited fitness which in turn pulls it to the location of best fitness achieved across the whole horde. The PSO update rule comprises of many distinguishing features which are adjusted and modified depending upon the area of application of algorithm. This paper gives a detailed description of the PSO algorithm and significance of the various parameters involved in its update rule. It also highlights the advantages and disadvantages of using PSO algorithm in any optimization problem.

Particle swarm optimization algorithm and its parameters: A review

This paper presents a novel evolutionary technique to optimize the parameters of fractional order controller for controlling two classes of systems: time-delay and higher-order system. The evolutionary technique known as grey wolf optimizer is used to tune both integer and fractional order controllers. The grey wolf optimizer searches for the optimum solution in the following manner, i.e. encircling, hunting, attacking the prey and finally search for the new prey consecutively if exists. To certify these various procedure, the performance indices like integral square error, integral absolute error, integral time-weighted square error, and integral time-weighted absolute error are minimized for the authenticity. Moreover, the proposed algorithm is validated and compared with well-established techniques.

Optimization of Fractional Order PID Controller Using Grey Wolf Optimizer

Optimized PID Controller for Magnetic Levitation System

This paper presents a novel evaluationary approach to extract color-texture features for image retrieval application namely Color Directional Local Quinary Pattern (CDLQP). The proposed descriptor extracts the individual R, G and B channel wise directional edge information between reference pixel and its surrounding neighborhoods by computing its grey-level difference based on quinary value (?2, ?1, 0, 1, 2) instead of binary and ternary value in 0°, 45°, 90°, and 135° directions of an image which are not present in literature (LBP, LTP, CS-LBP, LTrPs, DExPs, etc.). To evaluate the retrieval performance of the proposed descriptor, two experiments have been conducted on Core-5000 and MIT-Color databases respectively. The retrieval performances of the proposed descriptor show a significant improvement as compared with standard local binary pattern LBP, center-symmetric local binary pattern (CS-LBP), Directional binary pattern (DBC) and other existing transform domain techniques in IR system.

https://link.springer.com/content/pdf/10.1186%2Fs13673-014-0006-x.pdf

Shyam Krishna Nagar

Papers

Design of controllers with arbitrary convergence time

Particle swarm optimization algorithm and its parameters: A review

Optimization of Fractional Order PID Controller Using Grey Wolf Optimizer

Optimized PID Controller for Magnetic Levitation System

Color Directional Local Quinary Patterns for Content Based Indexing and Retrieval