Localized Weighted Sum Method for Many-Objective Optimization

Wang, Rui; Zhou, Zhongbao; Ishibuchi, Hisao; Liao, Tianjun; Zhang, Tao

doi:10.1109/tevc.2016.2611642

Cited by 248 publications

(97 citation statements)

References 85 publications

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“…In terms of future studies, the first optimal design of HRES in view of many-objective optimization [26,28] and/or interactive decision making [31] will be investigated. Second, regarding the algorithm design, we would like to examine the effect of reference point z * in decomposition based algorithms [32], and the performance of LPBI on convex PFs.…”

Section: Resultsmentioning

confidence: 99%

“…In lines 7-11, the parent set Q of x i is probabilistically selected from the neighbours B(x i ) or the whole population S, and is used to generate an offspring x new i by the differential evolution (DE) and polynomial mutation (PM) operators [26]. Lines 12-14 merge the current population and the newly generated individuals, denoted as JointS, and obtain the angle matrix φ sw .…”

Section: Endmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

Section: Endmentioning

confidence: 99%

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

et al. 2017

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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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“…One important research issue is the specification of a scalarizing function [11,21,24]. The specification of weight vectors [8] and their adjustments [20] is also important.…”

Section: Introductionmentioning

confidence: 99%

On the effect of normalization in MOEA/D for multi-objective and many-objective optimization

Ishibuchi

Doi

Nojima

2017

Complex Intell. Syst.

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The frequently used basic version of MOEA/D (multi-objective evolutionary algorithm based on decomposition) has no normalization mechanism of the objective space, whereas the normalization was discussed in the original MOEA/D paper. As a result, MOEA/D shows difficulties in finding a set of uniformly distributed solutions over the entire Pareto front when each objective has a totally different range of objective values. Recent variants of MOEA/D have normalization mechanisms for handling such a scaling issue. In this paper, we examine the effect of the normalization of the objective space on the performance of MOEA/D through computational experiments. A simple normalization mechanism is used to examine the performance of MOEA/D with and without normalization. These two types of MOEA/D are also compared with recently proposed many-objective algorithms: NSGA-III, MOEA/DD, and θ -DEA. In addition to the frequently used many-objective test problems DTLZ and WFG, we use their minus versions. We also propose two variants of the DTLZ test problems for examining the effect of the normalization in MOEA/D. where the range of each objective is the same on the Pareto front but totally different over the entire feasible region. Computational experiments on those test problems clearly show the necessity of the normalization. It is also shown that the normalization has both positive and negative effects on the performance of MOEA/D. These observations suggest that the influence of the normalization is strongly problem dependent.

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Localized Weighted Sum Method for Many-Objective Optimization

Cited by 248 publications

References 85 publications

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

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

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

On the effect of normalization in MOEA/D for multi-objective and many-objective optimization

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