Constriction Coefficient Based PSO and Chaotic GSA for Engineering Design Problems This is the 'Constriction Coefficient Based Particle Swarm Based Optimization and Chaotic Gravitational Search Algorithm (CPSOCGSA) Mathlab code for Solving Mechanical and Civil Engineering design Problems.
Change 'benchmark_functions.m' and 'benchmark_functions_details.m' for your own applications like solving other engineering problems and numerical optimization frameworks.
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functions:
Main.m : Main function for using Chaotic GSA algorithm.
CCPSOCGSA: Constriction Coefficient Based Particle Swarm Optimization and Chaotic Gravitational Search Algorithm
GSA.m : Gravitational Search Algorithm.
bbo.m :Biogeograpgy Based Optiumization
pso.m: Particle Swarm Optimization
DE.m: Differential Evolution
CPSOGSA: Constriction Coefficient based particle swarm optimization and Gravitational Search Algorithm
GA.m: Genetic Algorithm
ACO.m: Any Colony Optimization
crossover_continious : It is for calculating the cross_over rate of agents in successive generations
mutation_continious: It is used for changing the diversity of agents and helps in exploitation of the candidate solutions.
Geinitialization : It is utilized for exploration of the search space i.e. Diversification.
RouletteWheelSelection.m : finds optimal candidate solutions.
selection.m : Particulaily used in GA, for increasing local exploration rate.
initialization.m : initializes the position of agents in the search space, randomly.
Gfield.m : calculates the accelaration of each agent in gravitational field.
move.m : updates the velocity and position of agents.
massCalculation.m : calculates the mass of each agent.
Gconstant.m : calculates Gravitational constant.
space_bound.m : checks the search space boundaries for agents.
Scatter Plot.m: Fot getting correlation between best solutions of algorithms.
evaluateF.m : Evaluates the agents.
benchmark_functions.m : calculates the value of cost function.
benchmark_functions_details.m : gives boundaries and dimension of search space for design cost functions.