A Novel Immune Algorithm for Multiparty Multiobjective Optimization

2026-03-29

AI Summary

This work addresses the challenge of simultaneously approximating multiple Pareto fronts in multi-objective optimization problems involving multiple decision makers (MPMOPs). To this end, we propose a Multi-Party Immune Algorithm (MPIA) that innovatively integrates immune mechanisms with multi-party optimization. The algorithm features a cross-party guided crossover strategy based on multi-party non-dominated sorting and incorporates an adaptive individual activation mechanism driven by a novel Multi-party Coverage Metric (MCM) to jointly optimize conflicting objectives while preserving population diversity. Experimental results demonstrate that MPIA significantly outperforms state-of-the-art multi-objective evolutionary algorithms (MOEAs) and multi-party multi-objective evolutionary algorithms (MPMOEAs) on both synthetic MPMOP benchmarks and a real-world two-player unmanned aerial vehicle path planning scenario.

Abstract

Traditional multiobjective optimization problems (MOPs) are insufficiently equipped for scenarios involving multiple decision makers (DMs), which are prevalent in many practical applications. These scenarios are categorized as multiparty multiobjective optimization problems (MPMOPs). For MPMOPs, the goal is to find a solution set that is as close to the Pareto front of each DM as much as possible. This poses challenges for evolutionary algorithms in terms of searching and selecting. To better solve MPMOPs, this paper proposes a novel approach called the multiparty immune algorithm (MPIA). The MPIA incorporates an inter-party guided crossover strategy based on the individual's non-dominated sorting ranks from different DM perspectives and an adaptive activation strategy based on the proposed multiparty cover metric (MCM). These strategies enable MPIA to activate suitable individuals for the next operations, maintain population diversity from different DM perspectives, and enhance the algorithm's search capability. To evaluate the performance of MPIA, we compare it with ordinary multiobjective evolutionary algorithms (MOEAs) and state-of-the-art multiparty multiobjective optimization evolutionary algorithms (MPMOEAs) by solving synthetic multiparty multiobjective problems and real-world biparty multiobjective unmanned aerial vehicle path planning (BPUAV-PP) problems involving multiple DMs. Experimental results demonstrate that MPIA outperforms other algorithms.

Problem

• multiparty multiobjective optimization
• multiple decision makers
• Pareto front
• evolutionary algorithms
• solution set

Innovation

• multiparty multiobjective optimization
• immune algorithm
• inter-party guided crossover
• adaptive activation strategy
• multiparty cover metric