A Novel Immune Algorithm for Multiparty Multiobjective Optimization

TL;DR

This paper introduces a multiparty immune algorithm (MPIA) designed to effectively solve complex multiparty multiobjective optimization problems involving multiple decision makers, outperforming existing algorithms in diverse scenarios.

Contribution

The paper proposes a novel immune algorithm with inter-party guided crossover and adaptive strategies tailored for multiparty multiobjective problems, enhancing search diversity and performance.

Findings

MPIA outperforms traditional MOEAs in synthetic tests.

MPIA achieves superior results in real-world UAV path planning.

Adaptive strategies improve population diversity across DMs.

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.