ar-MOEA: A Novel Preference-Based Dominance Relation for Evolutionary Multiobjective Optimization

Jiang, Yi; Bai, Junren; He, Haibo; Peng, Jun; Tang, Dedong

doi:10.1109/tevc.2018.2884133

Cited by 52 publications

(20 citation statements)

References 37 publications

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“…The basic idea of the weighted sum strategy is that the weighting coefficients act in a preferential way for the multiobjectives. In other words, through a given set of ω 1 , ω 2 , …, ω m , the optimisation process produces a single point for the problem at the Pareto front [21]. Another point at the Pareto front can be established for a different set of ω i .…”

Section: Simulated Resultsmentioning

confidence: 99%

“…Deb et al [22] and Yi et al [21] found evidence in their studies that the solutions to the problem have a combined objective, see (6), and these are the best solutions for the objectives as well, being the best Pareto sets for the original problem, as in (6).…”

Section: Simulated Resultsmentioning

confidence: 99%

“…in which ω i complies with the required condition ∑ i ω i = 1. To ensure that a more concise Pareto front can be achieved, a relatively uniform distribution must be used with random weights, ω i , as far apart as possible [21].…”

Section: Simulated Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Compact ultra‐wideband FSS optimised through fast and accurate hybrid bio‐inspired multiobjective technique

Neto

Ferreira

Araújo

et al. 2020

IET Microwaves, Antennas & Propagation

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In this study, a bioinspired computation (BIC) optimisation technique to synthesise the geometric dimensions of a lozenge‐shaped broadband frequency selective surface (FSS) for electromagnetic wave filtering in satellite communications systems, for the X (8–12 GHz) and Ku (12–18 GHz) bands, is presented. A hybrid BIC technique, that combines the general regression neural network with a multi‐objective flower pollination algorithm, is proposed. The objectives of the optimisation process consist of tuning the resonant frequency of the lozenge‐shaped FSS to 15.2 GHz and operating bandwidth to 8 GHz, characterising a broadband framework. The cutoff frequencies to control the operating frequency range of the FSS, referring to transmission coefficient in decibels (dB), were obtained at a threshold of −10 dB. The proposed lozenge‐shaped FSS, after optimisation of the geometrical dimensions of its unit cell, was fabricated on fiberglass substrate (FR4). The simulated and measured results show a good agreement.

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Section: Simulated Resultsmentioning

confidence: 99%

Section: Simulated Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Compact ultra‐wideband FSS optimised through fast and accurate hybrid bio‐inspired multiobjective technique

Neto

Ferreira

Araújo

et al. 2020

IET Microwaves, Antennas & Propagation

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show abstract

“…An improved version of the crowding distance has been proposed in [10] and shows to improve NSGA-II performances. The main differences between the various proposed approaches in this category arise in the followings search components: fitness assignment, diversity management, and elitism [18], [32], [36]. • Decomposition-based approaches: most of decomposition based algorithms in solving MOPs operate in the objective space.…”

Section: Introductionmentioning

confidence: 99%

Fractal Decomposition Approach for Continuous Multi-Objective Optimization Problems

Souquet¹,

Talbi²,

Nakib

2020

IEEE Access

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Multi-objective optimization problems (MOPs) have been widely studied during the last decades. In this paper, we present a new intrinsically parallel approach based on Fractal decomposition (FDA) to solve MOPs. The key contribution of the proposed approach is to divide recursively the decision space using hyperspheres. Two different methods were investigated: the first one is based on scalarization that has been distributed on a parallel multi-node architecture virtual environments and taking profit from the FDA's properties, while the second method is based on Pareto dominance sorting. A comparison with state of the art algorithms on different well known benchmarks shows the efficiency and the robustness of the proposed decomposition approaches.

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“…Recently, multi-objective evolutionary algorithms (MOEAs) have been presented and shown promising performance in solving different kinds of MOPs [2]- [4]. Based on the selection mechanisms, most of existing MOEAs can be classified into three main categories, i.e., Pareto-based MOEAs [5]- [9], decomposition-based MOEAs [10]- [17], and indicator-based MOEAs [20]- [23].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Multi-Objective Particle Swarm Optimization Algorithm With an Adaptive Resource Allocation Strategy

Chen

Gong

et al. 2019

IEEE Access

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Recently, there are a number of particle swarm optimization algorithms (PSOs) proposed for tackling multi-objective optimization problems (MOPs). Most of multi-objective PSOs (MOPSOs) were designed to speed up their convergence, which have been validated when tackling various kinds of MOPs. However, they may face some challenges for tackling some complicated MOPs, such as the UF test problems with complicated Pareto-optimal sets, mainly due to their neglect on the diversity. To solve the above problem, a novel hybrid MOPSO (called HMOPSO-ARA) is suggested in this paper with an adaptive resource allocation strategy, which shows a superior performance over most MOPSOs. Using the decomposition approach in HMOPSO-ARA, MOPs are transferred into a set of subproblems, each of which is accordingly optimized by one particle using a novel velocity update approach with the strengthened search capability. Then, an adaptive resource allocation strategy is employed based on the relevant improvement on the aggregated function, which can reasonably assign the computational resource to the particles according to their performance, so as to accelerate the convergence speed to the true Pareto-optimal front. Moreover, a decomposition-based clonal selection strategy is further used to enhance our performance, where the cloning process is run on the external archive based on the relevant fitness improvement. The experiments validate the superiority of HMOPSO-ARA over four competitive MOPSOs (SMPSO, CMPSO, dMOPSO and AgMOPSO) and four competitive multi-objective evolutionary algorithms (MOEA/D-ARA, MOEA/D-DE MOEA/D-GRA and EF_PD) when tackling thirty-five test problems (DTLZ1-DTLZ9, WFG1-WFG9, UF1-UF10 and F1-F9), in terms of two widely used performance indicators.

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ar-MOEA: A Novel Preference-Based Dominance Relation for Evolutionary Multiobjective Optimization

Cited by 52 publications

References 37 publications

Compact ultra‐wideband FSS optimised through fast and accurate hybrid bio‐inspired multiobjective technique

Compact ultra‐wideband FSS optimised through fast and accurate hybrid bio‐inspired multiobjective technique

Fractal Decomposition Approach for Continuous Multi-Objective Optimization Problems

A Novel Hybrid Multi-Objective Particle Swarm Optimization Algorithm With an Adaptive Resource Allocation Strategy

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