Code for Computer Science

Manta Ray Foraging Optimization (MRFO) Manta ray foraging optimization (MRFO) is a new optimization approach for global optimization problems. The Manta ray optimization algorithm is developed by Zhao et al. (2020). Manta ray optimization algorithm is a bio-inspired optimization technique. Manta ray optimization algorithm is inspired by foraging strategies of manta rays. Manta ray optimization algorithm is used to solve optimization problems. Manta Ray basic structure Manta Ray foraging is often found in groups. Three main manta Rays Foraging Strategies:  Chain Foraging Cyclone Foraging Somersault Foraging Chain Foraging: More than 50 Manta Rays line up. One behind another. (the line is formed by Manta Rays). Manta Rays observe plankton’s position and swim towards it. Manta Rays form a foraging chain by line-up from head to tail. Assumed that BEST Solution is plankton with high concentration manta rays want to approach. In every generation, all individuals will update their position w

Particle Swarm Optimization (PSO) Algorithm Particle Swarm Optimization (PSO) Algorithm step-by-step explanation with Numerical Example and source code implementation. 🌞 Particle Swarm Optimization (PSO) Algorithm Matlab code. Particle Swarm Optimization Main File: main.m pso; Particle Swarm Optimization Function File: Sphere(x) function F1 = Sphere(x) F1 = sum(x.^2); end Particle Swarm Optimization File Name Save as: pso.m clear; close all; %% Fitness Function Calling FitnessFunction=@(x) Sphere(x); % Fitness Function Calling % Total Number of Decision Variables Used nVar=10; % Size of Decision Variables Matrix VarSize=[1 nVar]; % Lower Bound LowerBound =-10; % Upper Bound UpperBound = 10; %% Parameters Initialization Phase % Maximum Number of Iterations used. MaxT=100; % Total Number of Search Agents used. PopulationSize = 10; % Initialize PS

  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