An investigation on noise-induced features in robust evolutionary multi-objective optimization
Multi-objective (MO) optimization is a challenging research topic because it involves the simultaneous optimization of several complex and conflicting objectives that requires researchers to address many issues which are unique to MO problems. However multi-objectivity is only one aspect of real-world applications and there is a growing interest in the optimization of solutions that are insensitive to parametric variations as well. In order to evaluate the capability of MO evolutionary algorithms (MOEAs) to find robust solutions, it is important to employ suitable test functions. In this paper, empirical studies are conducted to examine the suitability of existing robust test functions. Results suggest that these test functions have a bias towards the region where the robust solutions lie, rendering it difficult to assess the true capability of MOEAs. Motivated by such a finding, we present a framework for the construction of robust continuous MO test functions characterized by different noise-induced features. These noise-induced features can pose different difficulties to the optimization algorithms. A fitness-inheritance scheme is also presented and incorporated into two well-known MOEAs. Empirical analysis of the proposed robust MO test functions reveals that some noise-induced features present greater challenges to robust MOEAs as compared to existing robust test functions. In addition, the vehicle routing problem with stochastic demand (VRPSD) is presented as a practical example of robust combinatorial MO optimization problems. The work presented in this paper should encourage further studies and the development of more effective algorithms for robust MO optimization.
multi-objective optimization evolutionary algorithms robust solutions robust test functions