Multiobjective Optimization of Inverse Problems Using a Vector Cross Entropy Method

Ho, S. L.; Yang, Shiyou

doi:10.1109/tmag.2011.2175437

Cited by 9 publications

(5 citation statements)

References 16 publications

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“…CE not only solves rare event probability estimation problems. It can also be used to solve complex optimization problems such as combination optimization [ 46 , 47 , 48 ], function optimization [ 46 , 48 , 49 ], engineering design [ 50 ], vehicle routing problems [ 51 ], and problems from other fields [ 52 , 53 , 54 ].…”

Section: Preliminariesmentioning

confidence: 99%

A Novel Hybrid Meta-Heuristic Algorithm Based on the Cross-Entropy Method and Firefly Algorithm for Global Optimization

Liu

et al. 2019

Entropy

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Global optimization, especially on a large scale, is challenging to solve due to its nonlinearity and multimodality. In this paper, in order to enhance the global searching ability of the firefly algorithm (FA) inspired by bionics, a novel hybrid meta-heuristic algorithm is proposed by embedding the cross-entropy (CE) method into the firefly algorithm. With adaptive smoothing and co-evolution, the proposed method fully absorbs the ergodicity, adaptability and robustness of the cross-entropy method. The new hybrid algorithm achieves an effective balance between exploration and exploitation to avoid falling into a local optimum, enhance its global searching ability, and improve its convergence rate. The results of numeral experiments show that the new hybrid algorithm possesses more powerful global search capacity, higher optimization precision, and stronger robustness.

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Section: Preliminariesmentioning

confidence: 99%

A Novel Hybrid Meta-Heuristic Algorithm Based on the Cross-Entropy Method and Firefly Algorithm for Global Optimization

Liu

et al. 2019

Entropy

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“…For example, the available fitness assignment mechanisms cannot measure, quantitatively, the number of improvements in the objective functions, and the amount of improvements in a specified objective function cannot be quantified either. To consider these problems, a measure is proposed in Ho and Yang (2012b). Moreover, to eliminate the deficiency of existing vector EAs when they are applied to study an inverse problem with more than three objectives, called a many-objective optimization problem, an aggregation-based algorithm, the multiple single objective Pareto sampling algorithm, is found in the success application of antenna array designs (Liu et al, 2012).…”

Section: Vector Easmentioning

confidence: 99%

Studies on numerical methodologies for inverse problems and optimizations in China

Yang

Yao

et al. 2013

COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering

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Purpose -The purpose of this paper is to explore the potential challenges in developing numerical methodologies for inverse problems and optimizations. Design/methodology/approach -Summarizing previous research results mainly contributed by two research groups of Zhejiang University and Hong Kong Polytechnic University. Findings -Computational intelligence plays an essential role in studying inverse problems and optimizations. Originality/value -An up-to-date review on the current status of numerical methodologies, especially computational intelligences, for inverse problems and optimizations contributed by Chinese researchers.

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“…Ren et al [10] used the PSO to increase the robustness of the problem of Team problem 22. Ho and Yang [11] applied a PSO‐based algorithm to make the electromagnetic inverse problem. Frederico et al [12] used the PSO to be the optimisation method for the robust design problem of the loudspeaker.…”

Section: Introductionmentioning

confidence: 99%

Robust design for the 9‐slot 8‐pole surface‐mounted permanent magnet synchronous motor by analytical method‐based multi‐objectives particle swarm optimisation

Gao

Qian

et al. 2016

IET Electric Power Applications

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To increase the robustness of the toque ripple peak values in mass productions of surface-mounted permanent magnet synchronous motor (SPMSM), the multi-objectives robust optimisation for the 9-slot 8-pole SPMSMs is developed in this study. To increase the search ability and reduce the calculation time of the optimisation, an analytical-based particle swarm optimisation (PSO) method is proposed. Analytical exact subdomain model is used to calculate the torque ripple values and the PSO method is to find the optimal machine design. To verify the effectiveness of the method, the robustness of the performance characteristics are compared between the initial design SPMSM and the design after optimisation by both the simulations and the experiments.

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Multiobjective Optimization of Inverse Problems Using a Vector Cross Entropy Method

Cited by 9 publications

References 16 publications

A Novel Hybrid Meta-Heuristic Algorithm Based on the Cross-Entropy Method and Firefly Algorithm for Global Optimization

A Novel Hybrid Meta-Heuristic Algorithm Based on the Cross-Entropy Method and Firefly Algorithm for Global Optimization

Studies on numerical methodologies for inverse problems and optimizations in China

Robust design for the 9‐slot 8‐pole surface‐mounted permanent magnet synchronous motor by analytical method‐based multi‐objectives particle swarm optimisation

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