A sensitivity analysis method aimed at enhancing the metaheuristics for continuous optimization

An efficient covering of the search space is an important issue when dealing with metaheuristics. Sensitivity analysis methods aim at evaluating the influence of each variable of a problem on a model (i.e. objective function) response. Such methods provide knowledge on the function behavior and would be suitable for guiding metaheuristics. To evaluate correctly the dimensions influences, usual sensitivity analysis methods need a lot of evaluations of the objective function or are constrained with an experimental design. In this paper, we propose a new method, with a low computational cost, which can be used into metaheuristics to improve their search process. This method is based on two global sensitivity analysis methods: the linear correlation coefficient technique and Morris’ method. We propose to transform the global study of a non linear model into a local study of quasi-linear sub-parts of the model, in order to evaluate the global influence of each input variable on the model. This sensitivity analysis method will use evaluations of the objective function done by the metaheuristic to compute a weight of each variable. Then, the metaheuristic will generate new solutions choosing dimensions to offset, according to these weights. The tests done on usual benchmark functions of sensitivity analysis and continuous optimization (CEC 2013) reveal two issues. Firstly, our sensitivity analysis method provides good results, it correctly ranks each dimension’s influence. Secondly, integrating a sensitivity analysis method into a metaheuristic (here, Differential Evolution and ABC with modification rate) improves its results.