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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 …
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. //// sajad.win8@gmail.com \\\\ 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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