A multi-objective lion swarm optimization based on multi-agent

Zhongqiang Wu; Zongkui Xie

doi:10.3934/jimo.2022001

Article Contents

2023, Volume 19, Issue 2: 1447-1458. Doi: 10.3934/jimo.2022001

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A multi-objective lion swarm optimization based on multi-agent

Zhongqiang Wu^, and
Zongkui Xie

Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao, Hebei, 066004, China

^*Corresponding author: Zhongqiang Wu
^*Corresponding author: Zhongqiang Wu

Received: August 31, 2020

Revised: July 31, 2021

Early access: January 2022

Published: February 2023

The first author is supported by the Natural Science Foundation of Hebei Province under Grant F2020203014

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Abstract

This paper proposes a multi-objective lion swarm optimization based on multi-agent (MOMALSO) for solving the increasingly complex multi-objective optimization problem in engineering practice. First, the Multi-agent system is introduced into the lion swarm optimization (LSO) algorithm. The optimization mechanism of LSO and the information exchange between the agents are integrated to enhance the local search and global search ability of the algorithm, and the self-learning operation can accelerate the approximate Pareto front obtained by the algorithm near to real front. Besides, the external archive is introduced for extending the LSO into a multi-objective algorithm. Finally, the simulations compared with other three algorithms are performed, and the results show that MOMALSO has significant advantages in both convergence and coverage, which verifies the superiority and effectiveness of the algorithm in multi-objective optimization.

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Mathematics Subject Classification: Primary: 90C26, 90C59; Secondary: 30E1.

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Figure 1. The topology structure of the MAS

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Figure 2. Flow chart of update and maintenance of external archives

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Figure 3. Obtained Pareto optimal solutions by 4 algorithms

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Figure 4. IGD convergence curves of four algorithms

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Table 1. Statistical results for IGD on UF1-UF10

Fun.	MOMALSO		MOGWO		MOALO		MOMVO
Fun.	Avg.	Std.	Avg.	Std.	Avg.	Std.	Avg.	Std.
UF1	5.94e-3	2.02e-4	1.38e-2	1.27e-3	1.97e-2	2.50e-3	1.29e-2	1.24e-3
UF2	7.14e-3	1.01e-3	9.26e-3	5.47e-4	2.03e-2	2.79e-3	9.70e-3	1.32e-3
UF3	1.88e-2	2.10e-3	3.33e-2	1.16e-3	3.86e-2	4.84e-3	4.89e-2	5.28e-3
UF4	5.16e-3	3.07e-4	7.45e-3	5.92e-4	8.81e-3	3.45e-4	1.03e-2	1.54e-4
UF5	1.53e-1	4.26e-2	1.71e-1	3.35e-2	3.12e-1	2.76e-2	2.13e-1	4.86e-2
UF6	3.65e-2	1.41e-3	3.64e-2	4.92e-4	7.87e-2	8.99e-3	4.17e-2	6.37e-3
UF7	5.53e-3	2.78e-3	1.09e-2	3.34e-2	2.82e-2	4.12e-3	4.57e-2	2.02e-2
UF8	3.29e-2	4.64e-3	3.67e-2	1.18e-2	8.59e-2	2.46e-2	6.17e-2	1.30e-2
UF9	2.69e-2	4.64e-3	2.54e-2	3.16e-3	8.65e-2	1.04e-2	3.93e-2	2.72e-3
UF10	2.67e-2	1.01e-2	7.49e-2	3.01e-2	4.21e-1	4.44e-2	1.21e-1	6.33e-2

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