# Function Optimization and Parameter Performance Analysis Based on Gravitation Search Algorithm
## 1 Introduction  
The function optimization problem is to find the optimal solution of the objective function by the iterative [1]. In general, the search objective is to optimize the function of the objective function, which is usually described by the continuous, discrete, linear, nonlinear, concave and convex of the function. For constrained optimization problems, one can adopt the specific operators to make the solutions always feasible, or use the penalty function to transform the solutions into the unconstrained problems. So, the unconstrained optimization problems are the research focus here. There has been considerable attention paid to employing metaheuristic algorithms inspired from natural processes and/or events in order to solve function optimization problems. The swarm intelligent optimization algorithm [2] is a random search algorithm simulating the evolution of biological populations. It can solve complex global optimization problems through the cooperation and competition among individuals. The representative swarm intelligence optimization algorithms include Ant Colony Optimization (ACO) algorithm [3], Genetic Algorithm (GA) [4], Particle Swarm Optimization (PSO) algorithm [5], Artificial Bee Colony (ABC) algorithm [6], _etc._  
However, not all met heuristic algorithms are bio-inspired, because their sources of inspiration often come from physics and chemistry. For the algorithms that are not bio-inspired, most have been developed by mimicking certain physical and/or chemical laws, including electrical charges, gravity, river systems, _etc._ The typical physics and chemistry inspired met heuristic algorithms include Big Bang-big Crunch optimization algorithm [7], Black hole algorithm [8], Central force optimization algorithm [9], Charged system search algorithm [10], Electro-magnetism optimization algorithm [11], Galaxy-based search algorithm [12], Harmony search algorithm [13], Intelligent waterdrop algorithm [14], River formation dynamics algorithm [15], Self-propelled particles algorithm [16], Spiral optimization algorithm [17], Water cycle algorithm [18], _etc._  
The gravitational search algorithm (GSA) was introduced by [PERSON] _et al._ in 2009 [19]. It was constructed based on the law of gravity and the notion of mass interactions. The GSA algorithm uses the theory of Newtonian physics and its searcher agents are the collection of masses. A multi-objective gravitational search algorithm (MOGSA) technique was proposed to be applied in hybrid laminates to achieve minimum weight and cost [20]. A fuzzy gravitational search algorithm was proposed for a pattern recognition application, which in addition provided a comparison with the original gravitational approach [21]. The grouping GSA (GGSA) adapted the structure of GSA for solving the data clustering problem [22]. Combining the advantages of the gravitational search algorithm (GSA) and gauss pseudo spectral method (GPM), an improved GSA (IGSA) is presented to enhance the convergence speed and the global search ability [23]. A novel modified hybrid Particle Swarm Optimization (PSO) and GSA based on fuzzy logic (FL) was proposed to control the ability to search for the global optimum and increase the performance of the hybrid PSO-GSA [24]. A binary quantum-inspired gravitational search algorithm (BQIGSA) was proposed by using the principles of QC together with the main structure of GSA to present a robust optimization tool to solve binary encoded problems [25]. Another binary version of hybrid PSOGSA called BPSOGSA was proposed to solve many kinds of optimization problems [26]. A new gravitational search algorithm was proposed to solve the unit commitment (UC) problem, which is the integrated binary gravitational search algorithm (BGSA) with the Lambda-iteration method [27].