Solving Highly Constrained Multi-Objective Decision Problems with the IMAP-IGS Preference-Guided Metaheuristic Framework

Highly constrained multi-objective design and decision problems are difficult to solve because of strict feasibility requirements and conflicting stakeholder preferences.

Evolutionary algorithms are widely used for these problems but typically separate constraint handling from preference optimisation.

Pareto-based methods provide a set of trade-off solutions but require post-processing to select a preferred option, while scalarisation can distort heterogeneous stakeholder preferences.

This paper introduces the Integrative Maximisation of Aggregated Preferences (IMAP) and its Inter-Generational Solver (IMAP-IGS), which embed preference aggregation directly into the evolutionary fitness function.

A constraint-violation preference function guides the search toward feasibility, while final scores reflect only stakeholder preferences.

The framework can be integrated into any evolutionary algorithm that uses scalar fitness evaluation.

Two implementations are presented: IMAP-BRKGA for combinatorial optimisation and IMAP-GA-II for continuous optimisation.

Testing on DAS-CMOP, MO-VRPTW, and HVASP benchmarks shows that IMAP consistently outperforms weighted-sum and Pareto-based approaches when stakeholder preferences conflict.

These advantages largely disappear when preferences align, indicating that IMAP's strength lies in resolving preference conflicts in highly constrained decision environments.