Evolutionary Biparty Multiobjective UAV Path Planning: Problems and Empirical Comparisons

Kesheng Chen; Wenjian Luo; Xin Lin; Zhen Song; Yatong Chang

cs.NEMar 23, 2026

Evolutionary Biparty Multiobjective UAV Path Planning: Problems and Empirical Comparisons

Authors: Kesheng Chen, Wenjian Luo, Xin Lin, Zhen Song, Yatong Chang

Organizations: Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China · Peng Cheng Laboratory, Shenzhen 518055, China · School of Artificial Intelligence/School of Future Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China

Abstract

Unmanned aerial vehicles (UAVs) have been widely used in urban missions, and proper planning of UAV paths can improve mission efficiency while reducing the risk of potential third-party impact. Existing work has considered all efficiency and safety objectives for a single decision-maker (DM) and regarded this as a multiobjective optimization problem (MOP). However, there is usually not a single DM but two DMs, i.e., an efficiency DM and a safety DM, and the DMs are only concerned with their respective objectives. The final decision is made based on the solutions of both DMs. In this paper, for the first time, biparty multiobjective UAV path planning (BPMO-UAVPP) problems involving both efficiency and safety departments are modeled. The existing multiobjective immune algorithm with nondominated neighbor-based selection (NNIA), the hybrid evolutionary framework for the multiobjective immune algorithm (HEIA), and the adaptive immune-inspired multiobjective algorithm (AIMA) are modified for solving the BPMO-UAVPP problem, and then biparty multiobjective optimization algorithms, including the BPNNIA, BPHEIA, and BPAIMA, are proposed and comprehensively compared with traditional multiobjective evolutionary algorithms and typical multiparty multiobjective evolutionary algorithms (i.e., OptMPNDS and OptMPNDS2). The experimental results show that BPAIMA performs better than ordinary multiobjective evolutionary algorithms such as NSGA-II and multiparty multiobjective evolutionary algorithms such as OptMPNDS, OptMPNDS2, BPNNIA and BPHEIA.

Figures & tables

Fig. 1: Each solution has four objectives and solution sets from the perspectives of different DMs.

Solution	$f_{1}-f_{4}$	NSGA-II layers	OptMPNDS2 layers [ 18 ]
$X_{1}$	(1,3,3,1)	1	1
$X_{2}$	(2,2,2,2)	1	1
$X_{3}$	(3,1,3,3)	1	2
$X_{4}$	(3,3,1,3)	1	2
$X_{5}$	(4,4,4,4)	2	3

TABLE I: Differences in solutions under different algorithms

Fig. 2: The objectives and constraints of the UAV path planning problem from the perspective of the BPMOP. (a)-(d), which are marked in blue, are the objectives of the efficiency department. (e)-(h), which are marked in red, are the objectives of the safety department. (i)-(k), which are marked in black, are the constraints of the UAV.

Problems	Efficiency DM Objectives	Safety DM Objectives
Case 1	$F_{eff}=(f_{\textup{length}},f_{\textup{distance}})$	$F_{safe}=(f_{\textup{fatal}},f_{\textup{eco}})$
Case 2	$F_{eff}=(f_{\textup{length}}+f_{\textup{height}},f_{\textup{distance}})$	$F_{safe}=(f_{\textup{fatal}},f_{\textup{eco}})$
Case 3	$F_{eff}=(f_{\textup{fuel}},f_{\textup{distance}})$	$F_{safe}=(f_{\textup{fatal}},f_{\textup{eco}})$
Case 4	$F_{eff}=(f_{\textup{length}},f_{\textup{distance}})$	$F_{safe}=(f_{\textup{fatal}},f_{\textup{noise}})$
Case 5	$F_{eff}=(f_{\textup{length}}+f_{\textup{height}},f_{\textup{distance}})$	$F_{safe}=(f_{\textup{fatal}},f_{\textup{noise}})$
Case 6	$F_{eff}=(f_{\textup{fuel}},f_{\textup{distance}})$	$F_{safe}=(f_{\textup{fatal}},f_{\textup{noise}})$

TABLE II: Case design

Fig. 3: Third-party fatality risk distribution maps

Fig. 4: Building, roads, UHPs, and one of the BPAIMA solutions

Problems	Algorithms
Problems	NSGA-II	OptMPNDS	OptMPNDS2	BPNNIA	BPHEIA	BPAIMA
Case 1	0.0981( $\pm$ 0.0016)	0.1531( $\pm$ 0.0012)	0.1670( $\pm$ 0.0013)	0.1665( $\pm$ 0.0010)	0.1685( $\pm$ 0.0012)	0.1852( $\pm$ 0.0014)
Case 2	0.1073( $\pm$ 0.0022)	0.1686( $\pm$ 0.0011)	0.1787( $\pm$ 0.0013)	0.1807( $\pm$ 0.0019)	0.1761( $\pm$ 0.0015)	0.2102( $\pm$ 0.0014)
Case 3	0.0935( $\pm$ 0.0012)	0.1544( $\pm$ 0.0011)	0.1593( $\pm$ 0.0009)	0.1697( $\pm$ 0.0010)	0.1663( $\pm$ 0.0009)	0.1959( $\pm$ 0.0009)
Case 4	0.1163( $\pm$ 0.0022)	0.1882( $\pm$ 0.0008)	0.1900( $\pm$ 0.0009 )	0.1938( $\pm$ 0.0013)	0.1816( $\pm$ 0.0008 )	0.1979( $\pm$ 0.0015)
Case 5	0.0817( $\pm$ 0.0012)	0.1778( $\pm$ 0.0018)	0.1857( $\pm$ 0.0015)	0.1855( $\pm$ 0.0011)	0.1796( $\pm$ 0.0013)	0.1924( $\pm$ 0.0020)

TABLE III: meanHV metrics of different algorithms

Fig. 5: Optimal solutions obtained by MOEAs or BPMOEAs in each case

Fig. 6: Convergence performance of different algorithms on meanHV

Fig. 7: Final population of NSGA-II in solving case 1

Fig. 8: Final population of BPAIMA in solving case 1

Fig. 9: Actual time expenditure ratios in different situations

UAV Trajectory Optimization

UAV trajectory optimization plans feasible, safe flight paths that balance mission goals, energy use, sensing, and communication.

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Mar 29, 2026J/K move · Enter open · S save

A Novel Immune Algorithm for Multiparty Multiobjective Optimization

Kesheng Chen, Wenjian Luo, Qi Zhou, Yujiang liu+2

Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies, School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China. · Peng Cheng Laboratory, Shenzhen 518000, China. · School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China.+1

Multi-Party Multi-Objective Optimization Multi-Objective Optimization Multi-Objective Evolutionary Optimization

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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.

Abstract

UAV Trajectory Optimization

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