Decomposition-based sub-problem optimal solution updating direction-guided evolutionary many-objective algorithm

Zhao, Haitong; Zhang, Bin; Duan, Peibo; Yang, Yang

doi:10.1016/j.ins.2018.03.015

Cited by 25 publications

(8 citation statements)

References 25 publications

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“…For each empty subpopulation, the associated weight vector will be replaced with a unit vector, which is randomly generated inside the range specified by the minimum and maximum objective values calculated from solutions in the current population; while the other weight vectors associated with the nonempty subpopulations remain unchanged. Likewise, some other variants replace each inactive weight vector with a new one defined by a solution least similar to the current active weight vectors where the similarity is measured by either the angle [50][51][52], cosine value [53] or perpendicular distance [54].…”

Section: Delete-and-add Methodsmentioning

confidence: 99%

“…Model-based adaptation methods T-MOEA/D [33], paλ-MOEA/D [34], apa-MOEA/D [35], DMOEA/D [36] Polynomial model MOEA/D-LTD [37,38] Parametric model MOEA/D-SOM [39], MOEA/D-GNG [40,41] Neural networks Delete-and-add MOEA/D-AWA [42], MOEA/D-URAW [43,44], AdaW [45] Less crowded or promising areas CLIA [46], MOEA/D-AM2M [47,48] RVEA* [49], [50], iRVEA [51], OD-RVEA [52], EARPEA [53], g-DBEA [54] Away from the promising areas A-NSGA-III [55], A 2 -NSGA-III [56], AMOEA/D [57], MOEA/D-TPN [58],…”

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: Delete-and-add Methodsmentioning

confidence: 99%

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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“…In the whole solution space S , the pareto solution set is a set composed of all the non‐dominated solutions 23 . The pareto frontier is defined as follows: a series of non‐dominated solutions form the Pareto solution set PS, 37 and the surface composed of their corresponding vectors is pareto frontier (PF) 38 …”

Section: Proposed Pga Algorithmmentioning

confidence: 99%

“…23 The pareto frontier is defined as follows: a series of non-dominated solutions form the Pareto solution set PS, 37 and the surface composed of their corresponding vectors is pareto frontier (PF). 38 All constraints are transformed into convex, and the optimization problem can be reformulated as…”

Section: Pareto Optimal Solution Theorymentioning

confidence: 99%

Multi‐UAV path planning utilizing the PGA algorithm for terrestrial IoT sensor network under ISAC framework

Azadur,

Pawase,

Chang

2023

Trans Emerging Tel Tech

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The integration of internet of things (IoT) technologies with unmanned aerial vehicles (UAVs) has initiated a groundbreaking revolution in data acquisition and communication systems across diverse domains. This document introduces an innovative endeavor, the integration of multi‐UAV path planning for integrated sensing and communication (ISAC) within ground‐based CAT‐M1 IoT sensor networks, accomplished through the application of the Pareto‐based genetic ant colony optimization (PGA) algorithm. The PGA algorithm is highly capable of UAV path planning due to its efficiency, adaptability, and seamless integration of domain expertise. By employing the PGA algorithm, we simultaneously minimize UAV travel distance while optimizing energy consumption, resulting in multi‐objective optimization. The synergy of ground‐based IoT sensors and seamless UAV communication, coupled with a convex optimization resource allocation algorithm, empowers real‐time data acquisition and heightens situational awareness. Our proposed UAV path planning PGA algorithm with resource allocation is crafted to maximize the efficiency of ground‐based sensor data acquisition. We stand at the forefront of advancing multi‐UAV data collection systems with this pioneering approach, promoting increased efficiency, robustness, and transformative solutions across diverse domains. The proposed system for ISAC achieves an impressive throughput of up to 95% of the system's capacity.

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“…Dhiman et al (2020) designed a novel hybrid evolutionary algorithm based on the RVEA to achieve great convergence and the hypervolume estimation algorithm (HYPE) to estimate the exact hypervolume values and preserve diversity, which employed both the RVEA and HYPE features in the full sense and delivered better performance. Zhao et al (2018) modified the original RVEA by the mutation strategy to guide the evolutionary direction toward the sub-problem optimal solution. The updating direction of the solution in each sub-problem is MOEVSSP in the automotive industry used to guide the generation of subsequent offspring and speed up the searching of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

Synergetic energy-conscious scheduling optimization of part feeding systems via a novel chaotic reference-guided policy

Zhou

et al. 2022

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PurposeThis paper aims to investigate a multi-objective electric vehicle’s (EV’s) synergetic scheduling problem in the automotive industry, where a synergetic delivery mechanism to coordinate multiple EVs is proposed to fulfill part feeding tasks.Design/methodology/approachA chaotic reference-guided multi-objective evolutionary algorithm based on self-adaptive local search (CRMSL) is constructed to deal with the problem. The proposed CRMSL benefits from the combination of reference vectors guided evolutionary algorithm (RVEA) and chaotic search. A novel directional rank sorting procedure and a self-adaptive energy-efficient local search strategy are then incorporated into the framework of the CRMSL to obtain satisfactory computational performance.FindingsThe involvement of the chaotic search and self-adaptive energy-efficient local search strategy contributes to obtaining a stronger global and local search capability. The computational results demonstrate that the CRMSL achieves better performance than the other two well-known benchmark algorithms in terms of four performance metrics, which is inspiring for future researches on energy-efficient co-scheduling topics in manufacturing industries.Originality/valueThis research fully considers the cooperation and coordination of handling devices to reduce energy consumption, and an improved multi-objective evolutionary algorithm is creatively applied to solve the proposed engineering problem.

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Decomposition-based sub-problem optimal solution updating direction-guided evolutionary many-objective algorithm

Cited by 25 publications

References 25 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

Multi‐UAV path planning utilizing the PGA algorithm for terrestrial IoT sensor network under ISAC framework

Synergetic energy-conscious scheduling optimization of part feeding systems via a novel chaotic reference-guided policy

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