Code for Computer Science

  Software Engineering   Software Engineering: Software Engineering (S.E.) is a profession dedicated to the designs, implementation, and modification of software.  Applications of Software Engineering are:  Re-engineering of software Software Testing Software Maintainance  Software Analysis Software Design Software Implementation The objective of software engineering is to produce good quality software, on time and within budget. To obtain this objective it is very important to focus on Software Quality and Software Development Process. Software Characteristics are:  Reusability of the components. Softwares are not manufactured as hardware.  In the Software development process, there is no wear-out phase. Software is fixable. Software Life Cycle Models: Software life cycle means the time period when a software product is conceived and when the software product is no longer available for use. The software Life cycle includes different phases: Requirement Phase Design Phase Implementatio

Chemical Reaction Optimization Algorithm Chemical Reaction Optimization algorithm is a population-based metaheuristic algorithm. Chemical Reaction Optimization algorithm is inspired by Chemical reactions. Chemical Reaction Optimization algorithm is developed by Albert Y.S. Lam and Victor O.K. Li. In this algorithm, Molecular structure (sum all characteristics) is used to compute the solution. Chemical Reaction Optimization algorithm is used to solve optimization problems. Chemistry Basic Fundamentals:  Atom / Molecule / Chemical Bonding / Molecular Structure / Molecule Energy / Chemical Reations/ Elementary Reactions  Atom:   According to Dalton (in 1808), an atom is the smallest part of an element that exists as the smallest entity. 3 important fundamental particles of an atom are Proton, Electron, and Neutron. For Example Oxygen (O), Nitrogen (N), Hydrogen (H), etc. Molecule: A molecule  is composed of 2 or more atoms held together by chemical bonds. The molecule is always formed whe

  BARC Computer Science and Information Technology Previous Year Question  Solved  1.) The worst-case time complexity of Quick Sort is: O(nlogn) O(n) O(n^2) None Answer: O(n^2) Explanation : Quicksort is based on the divide and conquers paradigm. Quicksort expected average running time is O(nlogn) 2.) In C programming operator '&' is used to represent: Logical AND Bitwise AND Logical OR Bitwise OR Answer: B itwise AND Explanation : logical AND (&&), bitwise AND (&), Logical OR (||), Bitwise OR(|) 3.) When a static variable is initialized: Answer: First time when a loop is executed. Explanation : Static variables only execute once. 4.) What is the maximum height of the AVL tree with 7 nodes? Assume that height of the tree with a single node is 0. Answer:  3 . 5.) Data structure used in the recursive algorithm is: Answer:  Stack

Grasshopper Optimization Algorithm (G.O.A.)  Grasshoppers are also known as pests. They destroy fields and crop production. Grasshopper lifecycle contains Eggs, Nymph Phases, and Adult Grasshopper. Grasshopper Optimization Algorithm is a Nature-inspired swarm-based optimization algorithm. Grasshopper Optimization Algorithm (GOA) is inspired by the foraging and swarming behavior of grasshoppers in nature. The grasshopper optimization algorithm is basically inspired by the behavior of adult grasshoppers in nature. Adult grasshoppers can make sudden jumps and cover long-range as compare to nymphs. This is the mathematical model used to represent grasshopper behavior in this algorithm : 𝑥_𝑖 = 𝑆_𝑖 + 𝐺_𝑖 + 𝐴_i GrasshopperCurrentPosition = Social Interaction in the group + Force of gravity + Wind Direction. Normally distributed random values are used in the grasshopper optimization algorithm for grasshopper random behavior in nature. Grasshopper Optimization Algorithm Steps. 1.) Parame

Metaheuristic Optimization Algorithms                     Metaheuristic Algorithms Categories. 1. Single Based Metaheuristic Algorithms: Single Solution is generated at each iteration/generation. 2. Population-Based Metaheuristic Algorithms: Multiple Solutions are generated at each iteration/generation. Single Based Metaheuristic Algorithms Examples. 1. Tabu Search 2. Guided Local Search 3. Iterated Local Search 4. Variable Neighborhood Search 5. Greedy Randomized Adaptive Search Population-Based Metaheuristic Algorithms Classification. Metaheuristic Algorithm Step-by-Step with Numerical Examples. WATCH NOW: CLICK HERE 1. Nature-Inspired Metaheuristic Algorithm 2. Evolutionary Algorithms 3. Swarm Based Algorithm 4. Human-Based Algorithm 5. Physics-Based Algorithm 6. Bio-Inspired Algorithm 7. Art-Inspired Algorithm 8. Plant-Based Algorithm Evolutionary Algorithms Examples Evolutionary Algorithm Step-by-Step with Numerical Examples. WATCH NOW: CLICK HERE 1. Genetic Algorith

Artificial Bee Colony Optimization Algorithm is a Swarm Intelligence Population-Based Metaheuristic Bees are flying insects with wings. Algorithm. Artificial Bee Colony Optimization Algorithm is inspired by the behavior of bees in nature. We can use an Algorithm. Artificial Bee Colony Optimization Algorithm to solve different Engineering Optimization Problems, Numerical Problems.  Bees feed on nectar as Energy Source in their life. Algorithms Inspired by the behavior of the bees: Bees Algorithms Bee Hives Bee Colony Optimization Algorithm Artificial Bee Colony (ABC) Algorithm Marriage Bee Optimization (MBO) Algorithm Bee Algorithms are used to solve different problems.   Bee System: Genetics Problems. Bee Hive: Routing Protocols.  Honey Bee Marriage: Cluster Analysis.  Bee Colony Optimization: Travelling Salesman Problems (TSP), Vehicle Routing Problem, Ride Matching Problems, Job Scheduling Problems. Artificial Bee Colony Optimization: Engineering Problems, Numerical Optimization. Be

 Krill Herd Optimization Algorithm Numerical Example What is Krill Herd Optimization Algorithm?  Krill herd optimization algorithm is introduced in 2012 to solve the Global Optimization Function. This is a population-based Swarm Intelligence Search Algorithm based on the Herding behavior of krill.   In the Krill herd optimization algorithm, we have a  Group of Krill individuals and they are  Searching for Food.  Krill Herd Optimization Algorithm Working. In real life, Krill move through Multidimensional Space to search for Food and High-Density herd. Three main Calculations to update Krill's Position.  Movement Induced by the position of other Krill’s. Foraging Activity: Krill’s Searching for Food. Random Diffusion: Net movement of each Krill based on Density. KRILL MOTION CALCULATION The movement led by Other Krill Foraging Activity/Motion Random Physical Diffusion FOR KRILL Individual Movement is Calculated as: 𝑁_𝑖^𝑁𝑒𝑤=𝑁^𝑀𝑎𝑥 𝛼_𝑖+𝜔_𝑛 𝑁_𝑖^𝑜𝑙𝑑 Where, 𝑁^𝑀𝑎𝑥 = Ma

Harris Hawks Optimization Algorithm Harris Hawks Optimization Algorithm Introduction Harris hawks optimizer (HHO) is a nature-inspired population-based optimization algorithm. Harris Hawks Optimization algorithm is a Population-based algorithm. This algorithm mimics the Exploring, Exploiting, and Attacking strategies of Harris Hawks. Harris Hawks Optimization algorithm can be used to solve different engineering problems. About Harris Hawk Harris Hawk is a large medium bird and also known as Dusky Hawk. Harris Hawk hunt in cooperative groups and group size is 2 to 7 birds. Hawks Diet: Large insects, Birds, Lizards, and Mammals. Harris Hawks Optimization Algorithm Steps-by-Step. INPUT: Population Size (N), and a maximum number of iterations (MaxT). OUTPUT: Target Location and its Fitness Values. Step 01: Initialize the population randomly 𝑋_𝑖 ( 𝑖=1,2,3,4,…𝑁 ). Step 02: Check While ( (t ≥ max )Stopping Criteria is Matched or Not ) Step 03: Calculate fitness value for each ha

 Transmission Expansion Planning (TEP) Transmission Systems are Large and Interconnected. Transmission Systems carry large quantities of electricity (from utility-scale to low voltage lines – distributed system).   Transmission Expansion Planning (TEP) is process of identifying needed investment and expansion in transmission.  Transmission Expansion Planning (TEP) is a complex decision-making process.  TEP Process include different analysis : System Cost. Reliability and Modeling. Compute Risk and burden. Number of Generators required. The number of Equipment required. How a transmission system should develop over time?  Determine the Number of Electric Power Transmission facilities required in the future power grid. Transmission Expansion Planning (TEP): A list of types of equipment can be inserted on the grid: Cables Transformers Transmission Lines Transmission Expansion Planning (TEP) Problem: Objective Functions Investment Function Operational Cost Function Power Loss Function  App

 Whale Optimization Algorithm for association rule mining. Input: Number of Maximum Iteration and Population Size, Minsupport, minconfedence.  Step 01: Initialize the population size for n search agents.[Xi(i=1,2,3,...n)] Step 02: Initialize i, A, C, L, and p. Step 03: Compute the fitness value of each search agent/whale. Step 04: X* = the best rule Step 05: While (CurrentIteration <= MaximumIteration ) Step 06: Update a, A, C, L and p. Step 07: For all whale poplation check          if (p<0.5) if(|A|<1)    For each Item in the solution Xi.    Update Items. Else if(|A|=1)     Select a random Item in Xi. Update Items. End if        For each item in the solution Xi. If the Item is odd, it belongs to the antecedent, Otherwise, it belongs to the consequence. End for Step 08: Calculate the fitness of each search agent. Step 09: Update X* if there is a better solution. Step 11: Iteration = currentIteration + 1 Step 12:    End While Step 13:    Return X* 1

Multiverse Optimization Algorithm   Q. What is Multiverse Theory? A.  Multiverse means the group of multiple universes. According to Multiverse theory, there is more than one Big Bang, and each Big Bang causes the birth of a universe. Multiple universes collide and interact with each other.  Q. What is Big Bang Theory? A.  Our universe comes into existence (13 Billion years Ago) from a single, hot and dense point. According to the Big Bang theory, our universe starts with a massive explosion. Before this explosion, nothing exists in this world. The universe contains time, energy, planets, stars, galaxies, and matter. Q. What is Multiverse Optimization Algorithm? A.  Multiverse Optimization Algorithm (MVO) is inspired by multiverse theory. Multiverse optimization algorithm is basically inspired by three main concepts of multiverse theory: White Hole, Black Hole, and Worm Hole. According to multi-verse theory, white holes / Big Bangs are created where collisions between parallel universe

 Local Binary Pattern Introduction to Local Binary Pattern (LBP) Q. What is Digital Image? A. Digital images are collections of pixels or numbers ( range from 0 to 255).  Q. What is Pixel? A. Pixel is the smallest element of any digital image. Pixel can be categorized as Dark Pixel and Bright Pixel. Dark pixels contain low pixel values and bright pixels contain high pixel values. Q. Explain Local Binary Pattern (LBP)? A. Local binary pattern is a popular technique used for image processing. We can use the local binary pattern for face detection and face recognition. Q. What is LBP Operator? A. LBP operator is an image operator. We can transform images into arrays using the LBP operator. Q. How LBP values are computed? A. LBP works in 3x3 (it contain a 9-pixel value ). Local binary pattern looks at nine pixels at a time. Using each 3x3 window in the digital image, we can extract an LBP code. Q. How to Obtain LBP operator value?  A. LBP operator values can be obtained by using the simp

Dragonfly Optimization Algorithm (DOA) Dragonfly Algorithm is developed by Mirjalili in 2016. Dragonfly Algorithm is a metaheuristic algorithm inspired by the behavior of dragonflies in nature. There are about 5000 known species of dragonflies. Dragonfly is a symbol of Strength, Courage, and Happiness in Japan.  Dragonfly Algorithm Step-by-Step: - Step 01: Initialize Dragonfly Population Randomly (𝑋_𝑖, Where i = 1,2,3,4,…n).  Step 02: Initialize Step vector / Size for dragonfly (〖∆𝑋〗_𝑖). Step 03: While(CurrentIteration < MaximumIteration) Step 04: Computer Fitness Values for each dragonfly. Step 05: Update Food sources and enemy.  Step 06: Update parameters w, s, a, c, f, and e. Step 07: Calculate S, A, C, and F. Step 08: Update neighboring radius.  Step 09: If the dragonfly has at least one neighboring dragonfly. {     Update Velocity and Position; }  else { Update Position; } Elseif { Check and correct new position based on boundaries of variable; } Note: To Improve rand

 Q. What is Optimization?  A.  Optimization means Optimum Point Where conditions are best and most favorable. Optimization algorithms help to obtain the best solutions for complex problems. Different numerical methods for optimization are used to design better systems.  Q. Why we do Optimization? A. To Find the better/best among different possible solutions Q. Why Objective functions are used? A. Objective functions are used to Maximize or Minimize values that you are trying to Optimize. Using objective functions you can obtain a minimum or maximum value. Q. Define Meta-heuristic optimization.  A. Metaheuristic algorithms plays important role in solving real-life problems. Metaheuristic algorithms are Optimization methods used to solve complex engineering problems. A Metaheuristic is an advanced technique for finding good solutions to a complex problem.  Q. Define multi-objective optimization problems?     A. When designers want to optimize two or more two objective functions simulta

 Ant colony optimization algorithms Do you know: How the Ant Colony Optimization algorithm works?   We all know about ants. Researchers have estimated about 13800 known ant species. In an ant colony, there is Queen (i.e., female ant), fertilized male ant (also known as drone), female ant workers, and soldiers. Queen ant can live up to 30 years. Ants live in colonies (also known as ant nest). An average ant colony contains 1000 individual ants and an ant super colony can contain 300 million individual ants. Ants communicate with each other indirectly using pheromones, sound, and touch. Ant colony optimization is a Nature Inspired Population Based Metaheuristic Optimization Algorithm. Ant colony optimization (ACO) is inspired by the real ant food searching behavior. Ant colony optimization algorithm is basically inspired by the pheromone-based ant communication . Ant colony optimization is developed by Marco Dorigo in 1992.  Author developed this algorithm to solve Discrete Variable Comb