Abstract:
Optimization is very important fact in terms of taking decision in mathematics, statistics,
computer science and real life problem solving or decision making application. Many different
optimization techniques have been developed for solving such functional problem. In order to
solving various problem computer Science introduce evolutionary optimization algorithm and
their hybrid. In recent years, test functions are using to validate new optimization algorithms and
to compare the performance with other existing algorithm. There are many Single Object
Optimization algorithm proposed earlier. For example: ACO, PSO, ABC. ACO is a popular
optimization technique for solving hard combination mathematical optimization problem. In this
paper, we run ACO upon five benchmark function and modified the parameter of ACO in order
to perform SBX crossover and polynomial mutation. The proposed algorithm SBXACO is tested
upon some benchmark function under both static and dynamic to evaluate performances. We
choose wide range of benchmark function and compare results with existing DE and its hybrid
DEahcSPX from other literature are also presented here.
