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All nature-inspired algorithms involve two processes namely exploration and exploitation. For getting optimal performance, there should be a proper balance between these processes. Further, the majority of the optimization algorithms suffer from local minima entrapment problem and slow convergence speed. To alleviate these problems, researchers …

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SajadAHMAD1/Chaotic-GSA-for-Engineering-Design-Problems

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This is the 'Chaotic Gravitational Search Algorithm' Mathlab code for Solving  Engineering design Benchmarks.

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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sajad.win8@gmail.com
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functions:

Main.m : Main function for using Chaotic GSA algorithm.

CHGSA: Chaotic Gravitational Search Algorithm 

GSA.m : Gravitational Search Algorithm.

bbo.m  :Biogeograpgy Based Optiumization

pso.m: Particle Swarm Optimization

DE.m: Differential Evolution

GWO.m: Grey Wolf Optimizer

SCA.m: Sine Cosine Algorithm

SSA.m: Salp Swarm Algorithm

PSOGSA.m: Particle Swarm Optimization and Gravittaional Search Algorithm

CPSOGSA: Constriction Coefficient based particle swarm optimization and Gravitational Search Algorithm

GA.m: Genetic Algorithm

ACO.m: Any Colony Optimization

chaos.m : For getting graphs of ten chaotic maps

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.

initializationGWO: Randomized initialization of GWO searcher agents.

initializationSCA: Initialization of SCA agents.

initializationSSA: Initialization of SSA optimization algorithm searcher agents for randomization.

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.

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All nature-inspired algorithms involve two processes namely exploration and exploitation. For getting optimal performance, there should be a proper balance between these processes. Further, the majority of the optimization algorithms suffer from local minima entrapment problem and slow convergence speed. To alleviate these problems, researchers …

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