Problem-specific multi-objective invasive weed optimization algorithm for reconnaissance mission scheduling problem (2024)

Cai, J., Peng, Z., Ding, S., & Sun, J. (2021). Problem-specific multi-objective invasive weed optimization algorithm for reconnaissance mission scheduling problem. Computers and Industrial Engineering, 157, Article 107345. https://doi.org/10.1016/j.cie.2021.107345

Cai, Junqi ; Peng, Zhihong ; Ding, Shuxin et al. / Problem-specific multi-objective invasive weed optimization algorithm for reconnaissance mission scheduling problem. In: Computers and Industrial Engineering. 2021 ; Vol. 157.

@article{2abba4e37fad4b2d8b2f991754422b8e,

title = "Problem-specific multi-objective invasive weed optimization algorithm for reconnaissance mission scheduling problem",

abstract = "With the progress of technology, the multi-agent system is successfully applied in many applications. In this paper, we investigate the problem of multi-agent system reconnaissance mission scheduling, which is the core of the reconnaissance decision support system and can be modeled as an extension of Multi-Mode Multi-Skill Resource-Constrained Project Scheduling Problem. Three objectives are considered in this paper: (1) minimizing the reconnaissance mission's makespan, (2) minimizing the total cost of allocating reconnaissance agents, and (3) maximizing the total quality of all reconnaissance tasks. An effective problem-specific multi-objective invasive weed optimization algorithm (PS-MOIWO) is proposed for solving the problem. Firstly, a new chromosome structure guaranteeing the feasibility of solutions and an initialization method are proposed. Secondly, we propose a self-adaptive penalty-based constraint handling technique to describe the fitness of each individual and adopt a novel non-dominated sorting method to rank the population. Thirdly, by using the problem-specific knowledge, a local search procedure is developed and incorporated into the PS-MOIWO framework to enhance the exploitation ability. Based on the Taguchi method, algorithm's suitable parameter combinations are determined. Simulation results based on a set of newly generated reconnaissance instances and the comparisons with some existing algorithms demonstrate the proposed algorithm's effectiveness.",

keywords = "Invasive weed optimization, Multi-mode, Multi-skill, RCPSP, Reconnaissance",

author = "Junqi Cai and Zhihong Peng and Shuxin Ding and Jingbo Sun",

note = "Publisher Copyright: {\textcopyright} 2021",

year = "2021",

month = jul,

doi = "10.1016/j.cie.2021.107345",

language = "English",

volume = "157",

journal = "Computers and Industrial Engineering",

issn = "0360-8352",

publisher = "Elsevier Ltd.",

}

Cai, J, Peng, Z, Ding, S & Sun, J 2021, 'Problem-specific multi-objective invasive weed optimization algorithm for reconnaissance mission scheduling problem', Computers and Industrial Engineering, vol. 157, 107345. https://doi.org/10.1016/j.cie.2021.107345

Problem-specific multi-objective invasive weed optimization algorithm for reconnaissance mission scheduling problem. / Cai, Junqi; Peng, Zhihong; Ding, Shuxin et al.
In: Computers and Industrial Engineering, Vol. 157, 107345, 07.2021.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

T1 - Problem-specific multi-objective invasive weed optimization algorithm for reconnaissance mission scheduling problem

AU - Cai, Junqi

AU - Peng, Zhihong

AU - Ding, Shuxin

AU - Sun, Jingbo

N1 - Publisher Copyright:© 2021

PY - 2021/7

Y1 - 2021/7

N2 - With the progress of technology, the multi-agent system is successfully applied in many applications. In this paper, we investigate the problem of multi-agent system reconnaissance mission scheduling, which is the core of the reconnaissance decision support system and can be modeled as an extension of Multi-Mode Multi-Skill Resource-Constrained Project Scheduling Problem. Three objectives are considered in this paper: (1) minimizing the reconnaissance mission's makespan, (2) minimizing the total cost of allocating reconnaissance agents, and (3) maximizing the total quality of all reconnaissance tasks. An effective problem-specific multi-objective invasive weed optimization algorithm (PS-MOIWO) is proposed for solving the problem. Firstly, a new chromosome structure guaranteeing the feasibility of solutions and an initialization method are proposed. Secondly, we propose a self-adaptive penalty-based constraint handling technique to describe the fitness of each individual and adopt a novel non-dominated sorting method to rank the population. Thirdly, by using the problem-specific knowledge, a local search procedure is developed and incorporated into the PS-MOIWO framework to enhance the exploitation ability. Based on the Taguchi method, algorithm's suitable parameter combinations are determined. Simulation results based on a set of newly generated reconnaissance instances and the comparisons with some existing algorithms demonstrate the proposed algorithm's effectiveness.

AB - With the progress of technology, the multi-agent system is successfully applied in many applications. In this paper, we investigate the problem of multi-agent system reconnaissance mission scheduling, which is the core of the reconnaissance decision support system and can be modeled as an extension of Multi-Mode Multi-Skill Resource-Constrained Project Scheduling Problem. Three objectives are considered in this paper: (1) minimizing the reconnaissance mission's makespan, (2) minimizing the total cost of allocating reconnaissance agents, and (3) maximizing the total quality of all reconnaissance tasks. An effective problem-specific multi-objective invasive weed optimization algorithm (PS-MOIWO) is proposed for solving the problem. Firstly, a new chromosome structure guaranteeing the feasibility of solutions and an initialization method are proposed. Secondly, we propose a self-adaptive penalty-based constraint handling technique to describe the fitness of each individual and adopt a novel non-dominated sorting method to rank the population. Thirdly, by using the problem-specific knowledge, a local search procedure is developed and incorporated into the PS-MOIWO framework to enhance the exploitation ability. Based on the Taguchi method, algorithm's suitable parameter combinations are determined. Simulation results based on a set of newly generated reconnaissance instances and the comparisons with some existing algorithms demonstrate the proposed algorithm's effectiveness.

KW - Invasive weed optimization

KW - Multi-mode

KW - Multi-skill

KW - RCPSP

KW - Reconnaissance

UR - http://www.scopus.com/inward/record.url?scp=85105345578&partnerID=8YFLogxK

U2 - 10.1016/j.cie.2021.107345

DO - 10.1016/j.cie.2021.107345

M3 - Article

AN - SCOPUS:85105345578

SN - 0360-8352

VL - 157

JO - Computers and Industrial Engineering

JF - Computers and Industrial Engineering

M1 - 107345

ER -

Cai J, Peng Z, Ding S, Sun J. Problem-specific multi-objective invasive weed optimization algorithm for reconnaissance mission scheduling problem. Computers and Industrial Engineering. 2021 Jul;157:107345. doi: 10.1016/j.cie.2021.107345