Changing range genetic algorithm for multimodal function optimisation
Adil Amirjanov, Константин Соболев · International Journal of Bio-Inspired Computation · 2015
The performance of a sequential changing range genetic algorithm (SCRGA) is described. This algorithm enables the transformation of an unconstrained numerical optimisation problem to a constrained problem by implementing constraints which convert the area near previously found optima to an infeasible region. This SCRGA feature is used for locating all optima of unconstrained and constrained numerical optimisation problems. Several test cases, related to unconstrained and constrained numerical optimisation problems, demonstrate the ability of this approach to reduce the computational costs, significantly improving success rates, accurately and precisely locating all optimal solutions.