A New Hybrid Mutation Operator for Multiobjective Optimization with Differential Evolution
Sindhya, K., Ruuska, S., Haanpää, T., & Miettinen, K. (2011). A New Hybrid Mutation Operator for Multiobjective Optimization with Differential Evolution. Soft Computing, 15(10), 2041-2055. https://doi.org/10.1007/s00500-011-0704-5
Published inSoft Computing
© Springer. This is an electronic final draft version of an article whose final and definitive form has been published in the Soft Computing published by Springer.
Differential evolution has become one of the most widely used evolution- ary algorithms in multiobjective optimization. Its linear mutation operator is a sim- ple and powerful mechanism to generate trial vectors. However, the performance of the mutation operator can be improved by including a nonlinear part. In this pa- per, we propose a new hybrid mutation operator consisting of a polynomial based operator with nonlinear curve tracking capabilities and the differential evolution’s original mutation operator, to be efficiently able to handle various interdependencies between decision variables. The resulting hybrid operator is straightforward to implement and can be used within most evolutionary algorithms. Particularly, it can be used as a replacement in all algorithms utilizing the original mutation operator of differential evolution. We demonstrate how the new hybrid operator can be used by incorporating it into MOEA/D, a winning evolutionary multiobjective algorithm in a recent competition. The usefulness of the hybrid operator is demonstrated with extensive numerical experiments showing improvements in performance compared to the previous state of the art. ...
Publication in research information system
MetadataShow full item record
Showing items with similar title or keywords.
Misitano, Giovanni; Saini, Bhupinder Singh; Afsar, Bekir; Shavazipour, Babooshka; Miettinen Kaisa (Institute of Electrical and Electronics Engineers (IEEE), 2021)Interactive multiobjective optimization methods incorporate preferences from a human decision maker in the optimization process iteratively. This allows the decision maker to focus on a subset of solutions, learn about the ...
A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms Chugh, Tinkle; Sindhya, Karthik; Hakanen, Jussi; Miettinen, Kaisa (Springer, 2019)Evolutionary algorithms are widely used for solving multiobjective optimization problems but are often criticized because of a large number of function evaluations needed. Approximations, especially function approximations, ...
Multiobjective shape design in a ventilation system with a preference-driven surrogate-assisted evolutionary algorithm Chugh, Tinkle; Kratky, Tomas; Miettinen, Kaisa; Jin, Yaochu; Makkonen, Pekka (ACM, 2019)We formulate and solve a real-world shape design optimization problem of an air intake ventilation system in a tractor cabin by using a preference-based surrogate-assisted evolutionary multiobjective optimization algorithm. ...
Miettinen, Kaisa; Hakanen, Jussi (World Scientific, 2017)Problems in chemical engineering, like most real-world optimization problems, typically, have several conflicting performance criteria or objectives and they often are computationally demanding, which sets special requirements ...
Miettinen, Kaisa; Hakanen, Jussi (World Scientific, 2009)Problems in chemical engineering, like most real-world optimization problems, typically, have several conflicting performance criteria or objectives and they often are computationally demanding, which sets special requirements ...