Improved evolutionary algorithms for solving constrained optimization problems with tiny feasible space

The quality of individuals in the initial population influences the performance of evolutionary algorithms, especially when the feasible region of the constrained optimization problems is very tiny in comparison to the entire search space. Too much diversity of the population may cost huge processing time; on the other hand the algorithms may trap into local optima for lack of diversity. This paper proposes a simple method to improve the quality of randomly generated initial solutions by sacrificing very little in diversity of the population. We introduce the method of search space reduction technique (SSRT) which is tested using four different existing EAs by solving a number of state-of-the-art test problems and a real world case problem. The experimental results show SSRT improves the solution qualities as well as speeding up the performance of the algorithm.