Decomposition-Based Algorithms Using Pareto Adaptive Scalarizing Methods

Wang, Rui; Zhang, Qingfu; Zhang, Tao

doi:10.1109/tevc.2016.2521175

Cited by 231 publications

(87 citation statements)

References 58 publications

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“…Lastly, other advanced evolutionary algorithms, e.g. [27,28,29], can be employed to solve the optimal design of HRES. In another aspect, the problem itself can be formulated as a standard multiobjective problem to benchmark the performance of di erent multi-objective evolutionary algorithms.…”

Section: Resultsmentioning

confidence: 99%

Optimal design of Fractional Order Digital Integrators – An Evolutionary Approach

Mahata¹,

SAHA²,

KAR³

et al. 2017

Scientia Iranica

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Abstract. The optimal design of grid-connected Hybrid Renewable Energy Systems (HRESs) is studied by using multi-objective evolutionary algorithm in this paper. With the total system cost and fuel emissions to be minimized, a two-objective optimization model of the hybrid system is established. Then, a modi ed preference-inspired co-evolutionary algorithm is, for the rst time, applied to nd the optimal con guration of a grid-connected hybrid system. As an example, a grid-connected hybrid system, including PV panels, wind turbines, and diesel generators, has been designed and good results are obtained which show that the proposed method is e ective.

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Section: Resultsmentioning

confidence: 99%

Optimal design of Fractional Order Digital Integrators – An Evolutionary Approach

Mahata¹,

SAHA²,

KAR³

et al. 2017

Scientia Iranica

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“…Metaheuristics algorithms are effective for both single objectives [57][58][59][60] and multi-objectives [61][62][63][64]. Due to the success of metaheuristics in many numerical and combinatorial optimization problems such as those above, DE is a type of metaheuristics that is easy and powerful.…”

Section: Literature Reviewmentioning

confidence: 99%

An Improved Differential Evolution Algorithm for Crop Planning in the Northeastern Region of Thailand

Ketsripongsa

Pitakaso

Sethanan

et al. 2018

MCA

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Abstract:This research aimed to solve the economic crop planning problem, considering transportation logistics to maximize the profit from cultivated activities. Income is derived from the selling price and production rate of the plants; costs are due to operating and transportation expenses. Two solving methods are presented: (1) developing a mathematical model and solving it using Lingo v.11, and (2) using three improved Differential Evolution (DE) Algorithms-I-DE-SW, I-DE-CY, and I-DE-KV-which are DE with swap, cyclic moves (CY), and K-variables moves (KV) respectively. The algorithms were tested by 16 test instances, including this case study. The computational results showed that Lingo v.11 and all DE algorithms can find the optimal solution eight out of 16 times. Regarding the remaining test instances, Lingo v.11 was unable to find the optimal solution within 400 h. The results for the DE algorithms were compared with the best solution generated within that time. The DE solutions were 1.196-1.488% better than the best solution generated by Lingo v.11 and used 200 times less computational time. Comparing the three DE algorithms, MDE-KV was the DE that was the most flexible, with the biggest neighborhood structure, and outperformed the other DE algorithms.

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“…Amongst the state-of-the-art MOEAs, the MOEA based on decomposition (MOEA/D) [12] has gained great popularity, and thus is adopted. It decomposes an MOP into a set of subproblems by a scalarizing method with evenly distributed direction vectors, and then optimizes these subproblems in a collaborative manner [20,21].…”

Section: Multi-objective Optimization Using Moea/d-lpbimentioning

confidence: 99%

“…The test problems are constructed by performing different shape constrains in the Walking Fish Group (WFG) kit to the standard WFG4 benchmark problem [20,21]. Taking WFG45 as an example, its PF is mixed, combining the concave and the convex.…”

Section: Appendix Amentioning

confidence: 99%

Multi-Objective Optimization of Hybrid Renewable Energy System Using an Enhanced Multi-Objective Evolutionary Algorithm

et al. 2017

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Due to the scarcity of conventional energy resources and the greenhouse effect, renewable energies have gained more attention. This paper proposes methods for multi-objective optimal design of hybrid renewable energy system (HRES) in both isolated-island and grid-connected modes. In each mode, the optimal design aims to find suitable configurations of photovoltaic (PV) panels, wind turbines, batteries and diesel generators in HRES such that the system cost and the fuel emission are minimized, and the system reliability/renewable ability (corresponding to different modes) is maximized. To effectively solve this multi-objective problem (MOP), the multi-objective evolutionary algorithm based on decomposition (MOEA/D) using localized penalty-based boundary intersection (LPBI) method is proposed. The algorithm denoted as MOEA/D-LPBI is demonstrated to outperform its competitors on the HRES model as well as a set of benchmarks. Moreover, it effectively obtains a good approximation of Pareto optimal HRES configurations. By further considering a decision maker's preference, the most satisfied configuration of the HRES can be identified.Keywords: hybrid renewable energy system (HRES); power grid; multi-objective optimization; multi-objective evolutionary algorithm (MOEA); penalty-based boundary intersection method

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Decomposition-Based Algorithms Using Pareto Adaptive Scalarizing Methods

Cited by 231 publications

References 58 publications

Optimal design of Fractional Order Digital Integrators – An Evolutionary Approach

Optimal design of Fractional Order Digital Integrators – An Evolutionary Approach

An Improved Differential Evolution Algorithm for Crop Planning in the Northeastern Region of Thailand

Multi-Objective Optimization of Hybrid Renewable Energy System Using an Enhanced Multi-Objective Evolutionary Algorithm

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