NAAM‐MOEA/D‐Based Multitarget Firepower Resource Allocation Optimization in Edge Computing

Liyuan Deng,Lina Wang,Weidong Liu,Sifeng Wang,Xiumei Zhang,Ping Yang,Mohammad R Khosravi

doi:10.1155/2021/5579857

Abstract

In the edge environment, the multiobjective evolutionary algorithm based on decomposition (MOEA/D) has been widely used in the research of multitarget firepower resource allocation. However, as the MOEA/D algorithm uses a fixed neighborhood update mechanism, it is impossible to rationally allocate computing resources based on the difficulty of each subproblem optimization, which results in some problems such as reduced population evolution efficiency and poor evolution quality during the calculation process. In order to solve these problems, a decision mechanism for subproblems and population evolution stages is designed, and on this basis, a MOEA/D algorithm based on the neighborhood adaptive adjustment mechanism is proposed to adapt to the edge environment. The optimization model of multiobjective firepower resource allocation based on the maximization of damage effect and the minimization of strike cost is constructed and solved. Using the ZDT series of test functions for comparative experiments, the simulation results show that the proposed algorithm can balance the distribution and convergence of population evolution and obtain satisfactory optimization results.

Highlights

In the edge environment, due to the limited computing resources of edge clients, the allocation of firepower resources based on factors such as battlefield situation, weapon performance, and combat objectives reasonably deploying and allocating various types and quantities of weapons and equipment to obtain the best combat effect is an important part of combat planning [1]
This paper considers the impact of subproblems and the degree of population evolution on the performance of the algorithm, designs the decision mechanism for subproblems and population evolution stages, and proposes a multiobjective evolutionary algorithm based on decomposition (MOEA/D) algorithm based on the neighborhood adaptive adjustment mechanism
The main innovations of this article are summarized as follows: (1) Aiming at the defects of the traditional MOEA/D algorithm’s fixed neighborhood update mechanism in solving the multiobjective fire resource allocation problem, a MOEA/D algorithm based on the neighborhood adaptive adjustment mechanism is proposed, which greatly improves the efficiency and quality in edge

Summary

Introduction

Due to the limited computing resources of edge clients, the allocation of firepower resources based on factors such as battlefield situation, weapon performance, and combat objectives reasonably deploying and allocating various types and quantities of weapons and equipment to obtain the best combat effect is an important part of combat planning [1]. The firepower resource allocation optimization problem in edge environment usually constructs a single-objective firepower resource allocation optimization model based on the damage probability objective function, using heuristic genetic algorithm [2], simulated annealing genetic algorithm [3], particle swarm algorithm [4], and ant colony algorithm to solve the model. The decomposition-based multiobjective evolutionary algorithm decomposes the high-dimensional and Wireless Communications and Mobile Computing complex multiobjective optimization problem into multiple single-objective subproblems by referring to the decomposition strategy in mathematical programming and optimizes the subproblems separately. It has the advantages of high algorithm efficiency and simple operation [8, 9]

Methods

Findings

Discussion

Conclusion