Enhancing MOEA/D with uniform population initialization, weight vector design and adjustment using uniform design

Zhang, Ying; Yang, Rennong; Zuo, Jian; Jing, Xiaoning

doi:10.1109/jsee.2015.00110

Cited by 15 publications

(9 citation statements)

References 15 publications

(42 reference statements)

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“…The goal of these methods is to make weight vectors be distributed more uniformly in the objective spaces. Zhang et al [26] introduced a uniform design method into MOEA/D and named the resulting algorithm as MOEA/D-UD, which initializes the population and weight vectors based on the mixed uniform experiment, and could uniformly explore the region of interest (ROI) of DMs from the initial iteration. The proposed weight vector adjustment strategy makes full use of the information on neighboring individuals to identify crowding regions and sparse regions for the complex PF, and then a PF distributed uniformly is found by removing weight vectors from and adding weight vectors into two regions, respectively.…”

Section: ) Methods Based On Uniform Designmentioning

confidence: 99%

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A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition: Variants, Challenges and Future Directions

2020

IEEE Access

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Section: ) Methods Based On Uniform Designmentioning

confidence: 99%

“…The distribution of POSs obtained depends highly on the weight vector generation method. Uniform design methods like the good grid point can be used to initialize population [26], construct crossover operators [43], and generate weight vectors [65], making the solution mapping vectors be distributed more evenly in the objective space.…”

Section: Uniform Designmentioning

confidence: 99%

A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition: Variants, Challenges and Future Directions

2020

IEEE Access

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“…MOEA/D Algorithm. MOEA/D algorithm simplifies the multi-objective problem into several single-objective problems by using a decomposition strategy [11]. Compared with the traditional evolutionary algorithm, the subproblems do not need to be optimized repeatedly, and the adjacent subproblems can be optimized with each other to improve the efficiency of the algorithm [12,13].…”

Section: The Hybrid Conjugate Gradient Methods For Moea/d Algorithmmentioning

confidence: 99%

“…The Simulation parameters are shown in Table 3 . Figure 3: The x-axis represents the fitness of path and uses the "Wp" in the formula (11) to indicate. The y-axis represents the threat fitness and uses the "Wt" in the formula (12) to indicate.…”

Section: Setting Of Simulation Parametersmentioning

confidence: 99%

Multi‐Target Strike Path Planning Based on Improved Decomposition Evolutionary Algorithm

Ming

Yang

et al. 2019

Mathematical Problems in Engineering

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This study proposes a path-finding model for multi-target strike planning. The model evaluates three elements, i.e., the target value, the aircraft’s threat tolerance, and the battlefield threat, and optimizes the striking path by constraining the balance between mission execution and the combat survival. In order to improve the speed of the Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D), we use the conjugate gradient method for optimization. A Gaussian perturbation is added to the search points to make their distribution closer to the population distribution. The simulation shows that the proposed method effectively chooses its target according to the target value and the aircraft’s acceptable threat value, completes the strike on high value targets, evades threats, and verifies the feasibility and effectiveness of the multi-objective optimization model.

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“…Zhang and Li first introduce the MOEA/D algorithm in [11]. Some of these optimizers are MOEA/D with Uniform Design (MOEA/D-UD) [47], MOEA based on Hierarchical Decomposition (MOEA/HD) [48], MOEA/D with Adaptive Weight Vector Adjustment (MOEA/D-AWA [49], MOGWO/D [50], and MOPSO/D [51].…”

Section: Def 3 Pareto Optimality [25]mentioning

confidence: 99%

A New Arithmetic Optimization Algorithm for Solving Real-World Multiobjective CEC-2021 Constrained Optimization Problems: Diversity Analysis and Validations

Jangir²,

et al. 2021

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In this paper, a new Multi-Objective Arithmetic Optimization Algorithm (MOAOA) is proposed for solving Real-World constrained Multi-objective Optimization Problems (RWMOPs). Such problems can be found in different areas, including mechanical engineering, chemical engineering, process and synthesis, and power electronics systems. MOAOA is inspired by the distribution behavior of the main arithmetic operators in mathematics. The proposed multi-objective version is formulated and developed from the recently introduced single-objective Arithmetic Optimization Algorithm (AOA) through an elitist nondominance sorting and crowding distance-based mechanism. For the performance evaluation of MOAOA, a set of 35 constrained RWMOPs and five ZDT unconstrained problems are considered. For the fitness and efficiency evaluation of the proposed MOAOA, the results obtained from the MOAOA are compared with four other state-of-the-art multi-objective algorithms. In addition, five performance indicators, such as Hyper-Volume (HV), Spread (SP), Inverse Generalized Distance (IGD), Runtime (RT), and Generalized Distance (GD), are calculated for the rigorous evaluation of the performance and feasibility study of the MOAOA. The findings demonstrate the superiority of the MOAOA over other algorithms with high accuracy and coverage across all objectives. This paper also considers the Wilcoxon signed-rank test (WSRT) for the statistical investigation of the experimental study. The coverage, diversity, computational cost, and convergence behavior achieved by MOAOA show its high efficiency in solving ZDT and RWMOPs problems. INDEX TERMS Arithmetic Optimization Algorithm (AOA); CEC-2021 real-world problems; Constrained optimization; Multi-Objective Arithmetic Optimization Algorithm (MOAOA). I. INTRODUCTIONRecently, computer technology advancements have increased the quality of addressing complex problems and decreased the time and cost of producing the optimal solution. However, human input is yet needed to determine the best of different solutions. Significant efforts can be seen in the literature to produce a system that optimally solves the given problem without any human effort [1]. One of the most reliable methods to accomplish this depends on optimization techniques. In many instances, most engineering problems, such as city programming, program management, investment decision, control system design, engineering

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Enhancing MOEA/D with uniform population initialization, weight vector design and adjustment using uniform design

Cited by 15 publications

References 15 publications

A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition: Variants, Challenges and Future Directions

A Survey of Multiobjective Evolutionary Algorithms Based on Decomposition: Variants, Challenges and Future Directions

Multi‐Target Strike Path Planning Based on Improved Decomposition Evolutionary Algorithm

A New Arithmetic Optimization Algorithm for Solving Real-World Multiobjective CEC-2021 Constrained Optimization Problems: Diversity Analysis and Validations

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