Proposition of the Interactive Pareto Iterated Local Search Procedure - Elements and Initial Experiments

TL;DR

This paper introduces an interactive local search method for multi-objective optimization that incorporates decision maker preferences, demonstrated through portfolio optimization and benchmark tests, showing promising results.

Contribution

It proposes a novel interactive Pareto local search procedure that integrates partial preferences to efficiently find solutions in multi-objective problems.

Findings

Effective in solving biobjective portfolio optimization

Shows promising results on benchmark instances

Demonstrates applicability to real-world problems

Abstract

The article presents an approach to interactively solve multi-objective optimization problems. While the identification of efficient solutions is supported by computational intelligence techniques on the basis of local search, the search is directed by partial preference information obtained from the decision maker. An application of the approach to biobjective portfolio optimization, modeled as the well-known knapsack problem, is reported, and experimental results are reported for benchmark instances taken from the literature. In brief, we obtain encouraging results that show the applicability of the approach to the described problem.