This paper describes a new Preference-based Interactive Evolutionary (PIE) algorithm for multi-objective optimization which exploits the advantages of both evolutionary algorithms and multiple criteria decision making approaches. Our algorithm uses achievement scalarizing functions and the potential of population based evolutionary algorithms to help the decision maker to direct the search towards the desired Pareto optimal solution. Starting from an approximated nadir point, the PIE algorithm improves progressively the objective function values of a solution by finding a better solution at each iteration that improves the previous one. The decision maker decides from which solution, in which direction, and at what distance from the Pareto front to find the next solution. Thus, the PIE algorithm is guided interactively by the decision maker. A flexible approach is obtained with the use of archive sets to store all the solutions generated during an evolutionary algorithm's run, as it allows the decision maker to freely navigate and inspect previous solutions if needed. The PIE algorithm is demonstrated using a pollution monitoring station problem and shown to be effective in helping the decision maker to find a solution that satisfies her/his preferences.
['Karthik Sindhya', 'Ana Belen Ruiz', 'Kaisa Miettinen']
A preference based interactive evolutionary algorithm for multi-objective optimization: PIE