MOEA/D with Two Types of Weight Vectors for Handling Constraints

Zhu, Qingling; Zhang, Qingfu; Lin, Qiuzhen; Sun, Jianyong

doi:10.1109/cec.2019.8790336

Cited by 5 publications

(4 citation statements)

References 19 publications

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“…Specifically, the convergence archive aims to push the population towards the PF; while the diversity archive plays as an auxiliary to explore areas under-exploited by the convergence archive including the infeasible regions. Similar idea was thereafter explored in some follow-up works such as [192] and [193]. In [194,195], Fan et al proposed a push and pull search (PPS) framework that bears a similar principle as [191] but divides the search process into two independent search stages dubbed push and pull.…”

Section: Trade-off Among Convergence Diversity and Feasibilitymentioning

confidence: 96%

“…Feasibility-driven method CMOEA/D-DE-ATP [183,184], g-DBEA [185,186] Constraint violation C-MOEA/D and C-NSGA-III [55], C-MOEA/DD [28] Prioritize isolated regions MOEA/D-IEpsilon [187], -MOEA/D-δ [188], MOEA/D-WEO-CHT [189] -constraint Weight vector adaptation RVEA [49], g-DEBA [54], CM2M [190] Weight vector adaptation Trade-off among convergence, diversity and feasibility C-TAEA [191], MOEA/D-TW [192], IDW-M2M-CDP [193] Multi-population PPS-MOEA/D [194], PPS-M2M [195], DC-NSGA-III [196] Push and pull…”

Section: Subcategory Algorithm Name Core Techniquementioning

confidence: 99%

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Decomposition Multi-Objective Evolutionary Optimization: From State-of-the-Art to Future Opportunities

2021

Preprint

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Decomposition has been the mainstream approach in the classic mathematical programming for multi-objective optimization and multi-criterion decision-making. However, it was not properly studied in the context of evolutionary multi-objective optimization until the development of multi-objective evolutionary algorithm based on decomposition (MOEA/D). In this article, we present a comprehensive survey of the development of MOEA/D from its origin to the current state-of-the-art approaches. In order to be self-contained, we start with a step-by-step tutorial that aims to help a novice quickly get onto the working mechanism of MOEA/D. Then, selected major developments of MOEA/D are reviewed according to its core design components including weight vector settings, subproblem formulations, selection mechanisms and reproduction operators. Besides, we also overviews some further developments for constraint handling, computationally expensive objective functions, preference incorporation, and real-world applications. In the final part, we shed some lights on emerging directions for future developments.

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Section: Trade-off Among Convergence Diversity and Feasibilitymentioning

confidence: 96%

Section: Subcategory Algorithm Name Core Techniquementioning

confidence: 99%

Decomposition Multi-Objective Evolutionary Optimization: From State-of-the-Art to Future Opportunities

2021

Preprint

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“…Two populations are maintained and infeasible solutions with good objective function values are preferred in the secondary population. Zhu et al [32] employed two types of weight vectors in MOEA/D to solve CMOPs. The solutions associated with the convergence weight vectors are updated based on the aggregation function, while the solutions associated with the diversity weight vectors are renewed according to both the aggregation function and the degree of constraint violation.…”

Section: B Infeasibility-assisted Cmoeasmentioning

confidence: 99%

ATM-R: An Adaptive Tradeoff Model with Reference Points for Constrained Multiobjective Evolutionary Optimization

Wang¹,

Qin²,

Meng³

et al. 2023

Preprint

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The goal of constrained multiobjective evolutionary optimization is to obtain a set of well-converged and welldistributed feasible solutions. To complete this goal, there should be a tradeoff among feasibility, diversity, and convergence. However, it is nontrivial to balance these three elements simultaneously by using a single tradeoff model since the importance of each element varies in different evolutionary phases. As an alternative, we adapt different tradeoff models in different phases and propose a novel algorithm called ATM-R. In the infeasible phase, ATM-R takes the tradeoff between diversity and feasibility into account, aiming to move the population toward feasible regions from diverse search directions. In the semi-feasible phase, ATM-R promotes the transition from "the tradeoff between feasibility and diversity" to "the tradeoff between diversity and convergence", which can facilitate the discovering of enough feasible regions and speed up the search for the feasible Pareto optima in succession. In the feasible phase, the tradeoff between diversity and convergence is considered to attain a set of well-converged and well-distributed feasible solutions. It is worth noting that the merits of reference points are leveraged in ATM-R to accomplish these tradeoff models. Also, in ATM-R, a multiphase mating selection strategy is developed to generate promising solutions beneficial to different evolutionary phases. Systemic experiments on a wide range of benchmark test functions demonstrate that ATM-R is effective and competitive, compared against five stateof-the-art constrained multiobjective optimization evolutionary algorithms.

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“…Note that the formulated optimization problem is a non convex problem. In order to solve it, we adopt three well known evolutionary algorithms named MODPSO [14], NSGA II [15][16][17] and MOEA/D [18,19]. For each algorithm, we specially design its key operators to make them suitable for the optimization problem.…”

Section: Introductionmentioning

confidence: 99%

A Multiobjective Optimization Approach for Air Traffic Flow Management for Airspace Safety Enhancement

Cai

Ang

Alam

2022

2022 IEEE Congress on Evolutionary Computation (CEC)

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This work aims to enhance the safety of air trafc in a procedural airspace by air trafc ow management (ATFM) without compromizing air trafc demand. In civil aviation, collision risk is an important indicator for air trafc safety assessment. In this work, we propose a generic ATFM framework based on multiobjective optimization for reducing lateral collision risk of the trafc in a given procedural airspace. The proposed framework aims to optimize the ight level assignment for a given set of ight plans such that the lateral collision risk can be reduced. To achieve this goal, we formulate an optimization problem containing two partially conicting objective functions. We then adopt three well-known evolutionary algorithms, i.e., MODPSO, NSGA-II, and MOEA/D to solve the optimization problem. We specially design some of the operators of those algorithms to make them suitable for the optimization problem. We merge the solutions yielded by those three algorithms and lter out the nal Pareto solutions. We carry out a case study on the procedural airspace of Singapore ight information region (FIR) with respect to twelve daily trafc data selected from the real trafc data for December 2019. Experiment results demonstrates that the lateral occupancy which is the key contributor to lateral risk can be reduced by 10.65% to 93.05% at a strategic planning level. This research contribute to strategic ight planning by assigning ight levels that may reduce the risk of collision in procedural airspace.

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MOEA/D with Two Types of Weight Vectors for Handling Constraints

Cited by 5 publications

References 19 publications

Decomposition Multi-Objective Evolutionary Optimization: From State-of-the-Art to Future Opportunities

Decomposition Multi-Objective Evolutionary Optimization: From State-of-the-Art to Future Opportunities

ATM-R: An Adaptive Tradeoff Model with Reference Points for Constrained Multiobjective Evolutionary Optimization

A Multiobjective Optimization Approach for Air Traffic Flow Management for Airspace Safety Enhancement

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