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检索条件"主题词=preference based multi-objective optimization"

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An Interactive Evolutionary multi-objective optimization Method based on Polyhedral Cones

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4th Conference on Learning and Intelligent optimization

作者： Sinha, Ankur Korhonen, Pekka Wallenius, Jyrki Deb, Kalyanmoy Aalto Univ Sch Econ Dept Business Technol Helsinki 00076 Finland

ISBN: (纸本)9783642137990

This paper suggests a preference based methodology, where the information provided by the decision maker in the intermediate runs of an evolutionary multi-objective optimization algorithm is used to construct a polyhedral cone. This polyhedral cone is used to eliminate a part of the search space and conduct a more focussed search. The domination principle is modified, to look for better solutions lying in the region of interest. The search is terminated by using a local search based termination criterion. Results have been presented on two to five objective problems and the efficacy of the procedure has been tested.

关键词： Evolutionary multi-objective optimization multiple criteria decision-making interactive multi-objective optimization sequential quadratic programming preference based multi-objective optimization

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Progressively Interactive Evolutionary multi-objective optimization Method Using Generalized Polynomial Value Functions

Progressively Interactive Evolutionary Multi-Objective Optim...

引用

2010 IEEE World Congress on Computational Intelligence

作者： Sinha, Ankur Deb, Kalyanmoy Korhonen, Pekka Wallenius, Jyrki Aalto Univ Sch Econ Dept Business Technol FIN-00101 Helsinki Finland

ISBN: (纸本)9781424481262

This paper advances and evaluates a recently proposed progressively interactive evolutionary multi-objective optimization algorithm. The algorithm uses preference information from the decision maker during the intermediate generations of the EMO and produces the most preferred solution on the Pareto-optimal front. The progress towards the Pareto-optimal front is made by approximating decision maker's value function. In this paper, a generalized polynomial value function has been proposed and the procedure to fit the value function to the decision maker's preference information has been described. The generality of the procedure of fitting a value function to the decision maker's preferences has been shown by using other existing value functions from the literature. The proposed generic polynomial value function has been incorporated in the PI-EMO-VF algorithm to efficiently approximate the decision maker's value function. The paper then evaluates the performance of the PI-EMO-VF algorithm on three and five objective test problems with constraints. It also evaluates the efficacy of the procedure in producing the most preferred solution when the decision maker is unable to provide perfect information, i.e., the decision maker finds certain pairs of solutions in the objective space to be incomparable. Results have been presented for three and five objective constrained test problems using the procedure.

关键词： Evolutionary multi-objective optimization algorithm multiple criteria decision-making interactive multi-objective optimization algorithm value/utility function preference based multi-objective optimization

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Solving High objective Problems in Fixed Interactions with the Decision Maker

Solving High Objective Problems in Fixed Interactions with t...

引用

IEEE Congress on Evolutionary Computation (CEC)

作者： Sinha, Ankur Pandey, Anmol Deb, Kalyanmoy Aalto Univ Sch Econ Dept Informat & Serv Econ Helsinki 00076 Finland

ISBN: (纸本)9781467315098

In recent advancements towards handling high objective optimization problems, it is proposed to progressively integrate the decision maker with the execution of an evolutionary multi-objective optimization algorithm. preferences from the decision maker are accepted at the intermediate steps of the algorithm and a progress towards the most preferred point is made. In this paper, we extend the work on 'progressively interactive evolutionary multi-objective optimization using value function' ( PI-EMO-VF) by allowing the optimization to be performed in a fixed number of interactions with the decision maker. In the PI-EMO-VF procedure, information is accepted from the decision maker, which is utilized by the evolutionary algorithm to perform a focused search in the region of interest. However, it is not possible to restrict the number of interactions required to handle an optimization problem. This paper contributes towards, solving the optimization problem in a pre-decided number of decision maker calls. Once the available budget of decision maker calls are known, it is optimally utilized to get close to the most preferred point on the Pareto-frontier. The paper evaluates the performance of the modified PI-EMO-VF algorithm on two, three and five objective test problems. A comparative study is performed against the previous proposal for the PI-EMO-VF procedure.

关键词： Evolutionary multi-objective optimization algorithm multiple criteria decision-making interactive multi-objective optimization algorithm value function decision maker calls preference based multi-objective optimization

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