Investigating the equivalence between PBI and AASF scalarization for multi-objective optimization

Singh, Hemant Kumar; Deb, Kalyanmoy

doi:10.1016/j.swevo.2019.100630

Cited by 8 publications

(6 citation statements)

References 41 publications

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“…APS strives to facilitate convergence at an earlier stage of the evolution and then gradually focuses on diversity later in the evolution process. Schemes based on APS include MOEA/D-PaP [84], NSGA-III-AASF, and NSGA-III-EPBI [85].…”

Section: Variants Of the Pbi Approachmentioning

confidence: 99%

Sizing of Hybrid PV/Battery/Wind/Diesel Microgrid System Using an Improved Decomposition Multi-Objective Evolutionary Algorithm Considering Uncertainties and Battery Degradation

et al. 2023

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In this paper, a small-scale PV/Wind/Diesel Hybrid Microgrid System (HMS) for the city of Yanbu, Saudi Arabia is optimally designed, considering the uncertainties of renewable energy resources and battery degradation. The optimization problem is formulated as a multi-objective one with two objective functions: the Loss of Power Supply Probability (LPSP) and the Cost of Electricity (COE). An Improved Decomposition Multi-Objective Evolutionary Algorithm (IMOEAD) is proposed and applied to solve this problem. In this approach, different decomposition schemes are combined effectively to achieve better results than the classical MOEA/D approach. Twelve case studies are investigated based on different scenarios and different numbers of houses (5 and 10 houses). Each time, the suggested approach produced a set of solutions that formed a Pareto front (PF). Considering a variety of parameters, the optimal compromise option can be selected by the designer from the PF.

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Section: Variants Of the Pbi Approachmentioning

confidence: 99%

Sizing of Hybrid PV/Battery/Wind/Diesel Microgrid System Using an Improved Decomposition Multi-Objective Evolutionary Algorithm Considering Uncertainties and Battery Degradation

et al. 2023

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“…A prominent example is the use of scalarization functions, a way of combining multiple objectives into a scalar function, optimizing which will produce one solution to the original MOP [ 41 ], this being the method used in Equation ( 6 ) to highlight the importance of each of the objectives. Gain

and

control the importance between the time objective and the photometric objective.…”

Section: Proposed Frameworkmentioning

confidence: 99%

A Framework for Coverage Path Planning Optimization Based on Point Cloud for Structural Inspection

Biundini

Pinto

Melo

et al. 2021

Sensors

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Different practical applications have emerged in the last few years, requiring periodic and detailed inspections to verify possible structural changes. Inspections using Unmanned Aerial Vehicles (UAVs) should minimize flight time due to battery time restrictions and identify the terrain’s topographic features. In this sense, Coverage Path Planning (CPP) aims at finding the best path to coverage of a determined area respecting the operation’s restrictions. Photometric information from the terrain is used to create routes or even refine paths already created. Therefore, this research’s main contribution is developing a methodology that uses a metaheuristic algorithm based on point cloud data to inspect slope and dams structures. The technique was applied in a simulated and real scenario to verify its effectiveness. The results showed an increasing 3D reconstructions’ quality observing optimizing photometric and mission time criteria.

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“…Algorithm Name Core Technique TCH variants AASF [11], MOEA/AD [76] Adapt the weighted metric parameter MOEA/D-par [77], MOEA/D-PaS [78], MSF and PSF [79] PBI variants NSGA-III-AASF and NSGA-III-EPBI [80], MOEA/D-PaP [81] Adapt the penalty parameter MOEA/D-APS and MOEA/D-SPS [82] MOEA/D-IPBI [83,84] Inverted PBI method MOEA/D-LTD [38] Augmented multiple distance metrics Constrained Decomposition MOEA/D-ACD [85], MOEA/D-LWS [86] Reduce the improvement region MOEA/D-M2M [87,88], MOEA/D-AM2M [48] 0 they heavily depend on the effectiveness of the evolution. It is anticipated that the weight vector adaptation can be misled by a poorly converged population that is either trapped by local optima or some highly deceptively region(s) of the PF.…”

Section: Subproblem Formulationmentioning

confidence: 99%

“…6(a), it becomes the WS approach when θ = 0.0 and the TCH approach when θ = 1.0. Its improvement region becomes smaller with the increase of θ. Singh and Deb [80] derived some theoretical underpins between the PBI approach with various θ values and the AASF approach. Therefore, one typical idea of the PBI variants is largely about the adaptation of θ with respect to the shape of the PF [81,82].…”

Section: Pbi Variantsmentioning

confidence: 99%

“…For example, [305] theoretically investigated the geometric properties of the TCH subproblem formulation and found some asymptotically equivalence between the TCH function and the Hypervolume indicator. Singh and Deb [80] investigated the equivalence between the PBI and the AASF subproblem formulations under different hyper-parameter settings. An open research direction is to mathematically prove the convergence property of the population-based meta-heuristics under the framework of MOEA/D.…”

Section: Emerging Topics and Future Research Directionsmentioning

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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Investigating the equivalence between PBI and AASF scalarization for multi-objective optimization

Cited by 8 publications

References 41 publications

Sizing of Hybrid PV/Battery/Wind/Diesel Microgrid System Using an Improved Decomposition Multi-Objective Evolutionary Algorithm Considering Uncertainties and Battery Degradation

Sizing of Hybrid PV/Battery/Wind/Diesel Microgrid System Using an Improved Decomposition Multi-Objective Evolutionary Algorithm Considering Uncertainties and Battery Degradation

A Framework for Coverage Path Planning Optimization Based on Point Cloud for Structural Inspection

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

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