A new resource allocation strategy based on the relationship between subproblems for MOEA/D

Wang, Peng; Zhu, Wen; Liu, Haihua; Liao, Bo; Cai, Liang; Wei, Xiaohui; Ren, Siqi; Yang, Jialiang

doi:10.1016/j.ins.2019.06.001

Cited by 23 publications

(5 citation statements)

References 36 publications

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“…The MOEA/D framework has been studied and used for dealing with a number of multi-objective problems [21][22][23]. It decomposes a multi-objective optimization problem into a number of single-objective optimization sub-problems, then solves these sub-problems simultaneously by evolving a population of solutions.…”

Section: Moea/d-sam For Mo-tapmentioning

confidence: 99%

Multi-objective Robust Simultaneous Arrival Matching for Aerial Swarms

Fang

et al. 2022

Preprint

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In this paper, we present an efficient approach to solving the simultaneous arrival matching problem for aerial swarm robots under complex constraints. By leveraging Pontryagin’sminimum principle, we derive the assignment cost for any robot-target pair, which takes into account the motion planning for the simultaneous arrival of each aerial robot. Then we explicitly formulate the simultaneous arrival matching problem as a multi-objective optimization problem and present a multi-objective evolutionary algorithm based on decomposition(MOEA/D) with problem-specific improvement for simultaneous arrival matching (SAM),called MOEA/D-SAM. Simulation results show that the MOEA/D-SAM outperforms significantly general-purpose MOEA/D, and an off-the-shelf multi-objective algorithm, i.e., NSGA-II. The advantages of the MOEA/D-SAM over the single-objective optimization algorithm are also shown.

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Section: Moea/d-sam For Mo-tapmentioning

confidence: 99%

Multi-objective Robust Simultaneous Arrival Matching for Aerial Swarms

Fang

et al. 2022

Preprint

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“…The reason for always selecting the boundaries is because they have an impact on the coordinates of the reference point z * used by the scalarizing function (Wang et al (2019)). Algorithm 1 details the pseudocode of the MOEA/D-DE (Zhang et al (2009)), using the Partial Update Strategy (MOEA/D-PS).…”

Section: The Partial Update Strategymentioning

confidence: 99%

“…To address this issue, several works have investigated methods to allocate different amounts of computational effort to sub-problems (Zhang et al (2009); Zhou and Zhang (2016); Lavinas et al (2019a,b); Wang et al (2019); Pruvost et al (2020); Lavinas et al (2020)). The general approach is to use different indicators (priority functions) to determine how many fitness evaluations are allocated to each sub-problem.…”

Section: Introductionmentioning

confidence: 99%

Faster Convergence in Multi-Objective Optimization Algorithms Based on Decomposition

Lavinas¹,

Ladeira²,

Aranha³

2021

Preprint

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The Resource Allocation approach (RA) improves the performance of MOEA/D by maintaining a big population and updating few solutions each generation. However, most of the studies on RA generally focused on the properties of different Resource Allocation metrics. Thus, it is still uncertain what the main factors are that lead to increments in performance of MOEA/D with RA. This study investigates the effects of MOEA/D with the Partial Update Strategy in an extensive set of MOPs to generate insights into correspondences of MOEA/D with the Partial Update and MOEA/D with small population size and big population size. Our work undertakes an in-depth analysis of the populational dynamics behaviour considering their final approximation Pareto sets, anytime hypervolume performance, attained regions and number of unique non-dominated solutions. Our results indicate that MOEA/D with Partial Update progresses with the search as fast as MOEA/D with small population size and explores the search space as MOEA/D with big population size. MOEA/D with Partial Update can mitigate common problems related to population size choice with better convergence speed in most MOPs, as shown by the results of hypervolume and number of unique non-dominated solutions, the anytime performance and Empirical Attainment Function indicates.

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“…Li et al 19 adopt the online agglomerative clustering based self‐adaptive mating restriction strategy for conventional MOEA/D to extract the neighborhood information in the solution space. Wang et al 20 propose a resource allocation strategy for conventional MOEA/D, in which the relationship between different subproblems can establish a probability vector to further guide the selection of sub‐problems for optimization. Ji et al 21 propose an improved MOEA/D, which makes use of a balance factor to unified diverse scales between different objectives.…”

Section: Introductionmentioning

confidence: 99%

An improved multiobjective evolutionary algorithm based on decomposition approach and its application in antenna array beam pattern synthesis

Liang

Fang

et al. 2021

Int J Numerical Modelling

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Antenna arrays can enhance the performance and reduce the overhead of the wireless communication systems. However, the beam pattern synthesis of antenna arrays are difficult problems since the optimization properties are usually trade‐offs that affect each other. In this paper, we formulate a multiobjective beam pattern optimization problem (MBPOP) to simultaneously reduce the maximum sidelobe level (SLL) and achieve the nulls of the antenna array beam pattern. The multiobjective evolutionary algorithm based on decomposition (MOEA/D) is a general and effective algorithm to solve the MOPs. However, it may be easy to lose population diversity and converge to local optimum. To overcome the issues above, we propose an improved MOEA/D (IMOEA/D) to deal with the formulated MBPOP. IMOEA/D introduces the normal distribution crossover operator (NDX), Lévy flight strategy and Euclidean distance‐based solution selection mechanism to enhance the performance of conventional MOEA/D to make it more suitable to solve the formulated MBPOP. Experiments are conducted and the results indicate that the proposed IMOEA/D has a better performance in terms of the convergence rate and population diversity compared to other algorithms for solving the formulated MBPOP.

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A new resource allocation strategy based on the relationship between subproblems for MOEA/D

Cited by 23 publications

References 36 publications

Multi-objective Robust Simultaneous Arrival Matching for Aerial Swarms

Multi-objective Robust Simultaneous Arrival Matching for Aerial Swarms

Faster Convergence in Multi-Objective Optimization Algorithms Based on Decomposition

An improved multiobjective evolutionary algorithm based on decomposition approach and its application in antenna array beam pattern synthesis

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