Decomposition-Based-Sorting and Angle-Based-Selection for Evolutionary Multiobjective and Many-Objective Optimization

Cai, Xinye; Yang, Zhitao; Fan, Zhun; Zhang, Qingfu

doi:10.1109/tcyb.2016.2586191

Cited by 115 publications

(35 citation statements)

References 38 publications

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“…As introduced in Section II, RVEA [40], LWS [44], MOEA/D-SAS [45], VaEA [41], MaOEA-CSS [47], and MOEA/VAN [50] utilize the information of vector angle, while SPEA2+SDE [17], CMODE+SDE [42], and SRA [39] employ the shift-based density estimation. To the best of our knowledge, AnD is the first attempt to combine both the vector angle and the shift-based density estimation together, by taking advantage of their complementary features.…”

Section: E Discussionmentioning

confidence: 99%

“…Note that the neighborhood is defined by a hypercone, whose apex angle is determined automatically in a priori. Very recently, a new variant of MOEA/D with sorting-and-selection (MOEA/D-SAS) is presented in [45]. In MOEA/D-SAS, the balance between convergence and diversity is achieved by two distinctive components, i.e., decomposition-based-sorting and angle-based-selection.…”

Section: Preliminary Knowledge a Vector Anglementioning

confidence: 99%

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AnD: A many-objective evolutionary algorithm with angle-based selection and shift-based density estimation

Liu

Wang

Huang

2020

Information Sciences

113

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Evolutionary many-objective optimization has been gaining increasing attention from the evolutionary computation research community. Much effort has been devoted to addressing this issue by improving the scalability of multiobjective evolutionary algorithms, such as Pareto-based, decomposition-based, and indicator-based approaches. Different from current work, we propose a novel algorithm in this paper called AnD, which consists of an angle-based selection strategy and a shift-based density estimation strategy. These two strategies are employed in the environmental selection to delete the poor individuals one by one. Specifically, the former is devised to find a pair of individuals with the minimum vector angle, which means that these two individuals share the most similar search direction. The latter, which takes both the diversity and convergence into account, is adopted to compare these two individuals and to delete the worse one. AnD has a simple structure, few parameters, and no complicated operators. The performance of AnD is compared with that of seven state-of-the-art many-objective evolutionary algorithms on a variety of benchmark test problems with up to 15 objectives. The experimental results suggest that AnD can achieve highly competitive performance. In addition, we also verify that AnD can be readily extended to solve constrained many-objective optimization problems.

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Section: E Discussionmentioning

confidence: 99%

Section: Preliminary Knowledge a Vector Anglementioning

confidence: 99%

AnD: A many-objective evolutionary algorithm with angle-based selection and shift-based density estimation

Liu

Wang

Huang

2020

Information Sciences

113

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“…Evolutionary Multi-objective Optimization (EMO) algorithms have demonstrated their ability for solving multi-objective optimization problems [20], [21], [22], [23], [24], [25]. They find a subset of non-dominated solutions approximating P F (the set of all Pareto optimal solutions in the objective space).…”

Section: Multi-objective Evolutionary Algorithmsmentioning

confidence: 99%

“…These options Augment an ES error object at throw time and have to be deactivated together 31,24 Disabling duktape object built-in requires to disable global built-in too 24,30 Disabling global built-in requires to disable number built-in too…”

Section: -74mentioning

confidence: 99%

MoMIT: Porting a JavaScript Interpreter on a Quarter Coin

Morales

Saborido

Guéhéneuc

2021

IIEEE Trans. Software Eng.

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The Internet of Things (IoT) is a network of physical, heterogeneous, connected devices providing services through private networks and the Internet. It connects a range of new devices to the Internet so they can communicate with Web servers and other devices around the world. Today's standard platform for communicating Web pages and Web apps is JavaScript (JS) and extending the same standard platform to connect IoT devices seems more than appropriate. However, porting JS applications to the large variety of IoT devices, specifically on System-on-a-Chip (SoCs) devices (e.g., Arduino Uno, Particle Photon), is challenging because these devices are constrained in terms of memory and storage capacity. Running JS applications adds an overhead of resources to deploy a code interpreter on the devices. Also, running JS applications may not be possible "as is" on some device missing some hardware/software capabilities. To address this problem, we propose MoMIT a multiobjective optimization approach to miniaturize JS applications to run on IoT constrained devices. To validate MoMIT, we miniaturize a JS interpreter to execute a testbed comprised of 23 applications and measure their performances before and after applying the miniaturization process. We implement MoMIT using three different search algorithms and found that it can reduce code size, memory usage, and CPU time by median values of 31%, 56%, and 36% respectively. Finally, MoMIT ported the miniaturized JS interpreters up to to 2 SoCs additional devices, in comparison of using default JS interpreter features.

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“…The most representative algorithm of this type is MOEA/D [6]. Some variants of MOEA/D include MOEA/D-DE [7], MOEA/D-M2M [8], EAG-MOEA/D [9] and MOEA/D-SAS [10]. For indicator-based MOEAs, they use a scalar metric to assist the selection, typical examples of this type are IBEA [11], SMS-EMOA [12], HypE [13] and FV-MOEA [14].…”

Section: Introductionmentioning

confidence: 99%

MOEA/D with angle-based constrained dominance principle for constrained multi-objective optimization problems

Fan

Fang

et al. 2019

Applied Soft Computing

Self Cite

111

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This paper proposes a novel constraint-handling mechanism named angle-based constrained dominance principle (ACDP) embedded in a decomposition-based multi-objective evolutionary algorithm (MOEA/D) to solve constrained multiobjective optimization problems (CMOPs). To maintain the diversity of the working population, ACDP utilizes the information of the angle of solutions to adjust the dominance relation of solutions during the evolutionary process. This paper uses 14 benchmark instances to evaluate the performance of the MOEA/D with ACDP (MOEA/D-ACDP). Additionally, an engineering optimization problem (which is I-beam optimization problem) is optimized. The proposed MOEA/D-ACDP, and four other decomposition-based CMOEAs, including C-MOEA/D, MOEA/D-CDP, MOEA/D-Epsilon and MOEA/D-SR are tested by the above benchmarks and the engineering application. The experimental results manifest that MOEA/D-ACDP is significantly better than the other four CMOEAs on these test instances and the real-world case, which indicates that ACDP is more effective for solving CMOPs.

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Decomposition-Based-Sorting and Angle-Based-Selection for Evolutionary Multiobjective and Many-Objective Optimization

Abstract: Index Terms-Angle-based-selection (ABS), decompositionbased-sorting (DBS), diversity, evolutionary multiobjective optimization, many-objective optimization.

Cited by 115 publications

References 38 publications

AnD: A many-objective evolutionary algorithm with angle-based selection and shift-based density estimation

AnD: A many-objective evolutionary algorithm with angle-based selection and shift-based density estimation

MoMIT: Porting a JavaScript Interpreter on a Quarter Coin

MOEA/D with angle-based constrained dominance principle for constrained multi-objective optimization problems

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