Improving Algorithm Response to Preference Changes in Multiobjective Optimisation Using Archives

Using evolutionary algorithms to solve optimisation problems with multiple objectives has proven very successful over the past few decades. The ability of these methods to efficiently find sets of solutions representing trade-offs between conflicting objectives has enhanced decision making in a wide variety of fields. Increasingly though, such techniques are being adapted to incorporate end-user preferences in order to reduce search spaces and provide smaller sets of targeted solutions. Eliciting these preferences interactively during optimisation has also become popular and helps a decision maker explore and learn and the problem and its range of solutions. Interactivity also facilitates the correction of mistakes and inaccurate preferences, leading to more satisfactory solutions, faster. In order to achieve these benefits an algorithm must be able to rapidly respond to changes in preferences. This work explores the use of secondary population archives to ensure a preference-based algorithm can change its search focus efficiently and effectively. When preferences change and the search is redirected to a new region of interest, an archive of previously found solutions can be consulted and solutions close to the new region can be included in the current population. This work shows how such archives can be implemented and how they can improve responsiveness for certain problems.