New Artificial Landscape for Single-Objective Problems and Validation of Evolutionary Algorithms

In general terms, optimization means to improve the state of something; Having a function f(x) in optimization, we want to find an argument x whose relevant cost is optimum (usually minimum). The Test Functions, which known as Artificial Landscapes in Applied Mathematic, are utilized for assessing features of optimization algorithms such: convergence speed, accuracy, robustness and their total functionality. Single-objective optimization algorithms are the underlying basis to more sophisticated algorithms such: multi-objective optimization algorithms, niching, constrained optimization algorithms, etc. These functions are also used for testing of evolutionary algorithms (generally optimization). The aim of presenting this paper is to implement a sort of Test Function for single-objective optimization problems and validating of evolutionary algorithms. Acquired results indicate that, the proposed test function is able to perform optimization operation and validating evolutionary algorithms well.

Keywords:

Test Functions , Optimization Algorithms , Cost , Convergence , Evolutionary Algorithms