Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization

Mirjalili, Seyedali; Saremi, Shahrzad; Mirjalili, Seyed Mohammad; Coelho, Leandro dos Santos

doi:10.1016/j.eswa.2015.10.039

Cited by 1,279 publications

(480 citation statements)

References 26 publications

Supporting

Mentioning

473

Contrasting

Unclassified

Order By: Relevance

“…It is worth mentioning that there are many methods to improve or optimize the weights of the neural networks, including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), etc. [37][38][39]. In this paper, we choose Bayesian regularization mainly because the data in our study are scarce, and the Bayesian method can improve the performance of neural networks (by reducing the training iterations) [36].…”

Section: Training Parameter Selection On the Bp Neural Networkmentioning

confidence: 99%

A Smart MCDM Framework to Evaluate the Impact of Air Pollution on City Sustainability: A Case Study from China

2017

View full text Add to dashboard Cite

Abstract:Air pollution has become one of the key environmental concerns in the urban sustainable development. It is important to evaluate the impact of air pollution on socioeconomic development since it is the prerequisite to enforce an effective prevention policy of air pollution. In this paper, we model the impact of air pollution on the urban economic development as a Multiple Criteria Decision Making (MCDM) problem. In particular, we propose a novel Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) analysis framework to evaluate multiple factors of air pollutants and economic development. Our method can overcome the drawbacks of conventional TOPSIS methods by using Bayesian regularization and the Back-Propagation (BP) neural network to optimize the weight training process. We have conducted a case study to evaluate our proposed framework.

show abstract

Section: Training Parameter Selection On the Bp Neural Networkmentioning

confidence: 99%

A Smart MCDM Framework to Evaluate the Impact of Air Pollution on City Sustainability: A Case Study from China

2017

View full text Add to dashboard Cite

show abstract

“…This algorithm is inspired by the social hierarchy and hunting strategies of grey wolves in the wild. It can be regarded as a robust swarm-based optimizer [40][41][42][43][44][45]. The following discusses its working mechanism.…”

Section: Gwo Algorithmmentioning

confidence: 99%

SAR Image Segmentation Based on Improved Grey Wolf Optimization Algorithm and Fuzzy C-Means

et al. 2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

An improved Grey Wolf Optimization (GWO) algorithm with differential evolution (DEGWO) combined with fuzzy C-means for complex synthetic aperture radar (SAR) image segmentation was proposed for the disadvantages of traditional optimization and fuzzy C-means (FCM) in image segmentation precision. In the process of image segmentation based on FCM algorithm, the number of clusters and initial centers estimation is regarded as a search procedure that searches for an appropriate value in a greyscale interval. Hence, an improved differential evolution Grey Wolf Optimization (DE-GWO) algorithm is introduced to search for the optimal initial centers; then the image segmentation approach which bases its principle on FCM algorithm will get a better result. Experimental results in this work infers that both the precision and efficiency of the proposed method are superior to those of the state of the art.

show abstract

“…One is because of cost considerations, for computing resources and computer performance have certain limitations, which requires calculation of multi-objective optimization algorithm with low complexity, small amount of calculation; the two is some application problems to have higher real-time requirements, which requires the multi-objective optimization algorithm can in a relatively short period of time are satisfactory the algorithm is also requires a shorter running time or faster convergence speed [14]. The increase of target number not only makes it difficult to obtain satisfactory results for high dimensional multi-objective optimization problems, but also has high computational complexity and long running time.…”

Section: High Dimensional Multi-objective Optimization Problem Solvinmentioning

confidence: 99%

Research on Key Problems of Heuristic Evolutionary Algorithm for Dynamic High Dimensional Multi-Objective Optimization Problem

Yang¹,

Wang²

2018

Proceedings of the 2018 8th International Conference on Social Science and Education Research (SSER 2018)

View full text Add to dashboard Cite

Abstract. The multi-objective optimization method has a strong background of engineering application. In recent years, the problem of solving multi-objective optimization based on evolutionary algorithm has become a hot topic in the international academic circle. This paper studies the problem of dynamic application of high-dimensional multi-objective optimization problem heuristic evolutionary algorithm key technology and algorithm, which constructs online target dimension reduction algorithm model, alternating between the executive, the original problem is gradually transformed into smaller problems, improve the optimization algorithm, which provides a new method for solving high dimension the multi-objective optimization problem.

show abstract

Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization

Cited by 1,279 publications

References 26 publications

A Smart MCDM Framework to Evaluate the Impact of Air Pollution on City Sustainability: A Case Study from China

A Smart MCDM Framework to Evaluate the Impact of Air Pollution on City Sustainability: A Case Study from China

SAR Image Segmentation Based on Improved Grey Wolf Optimization Algorithm and Fuzzy C-Means

Research on Key Problems of Heuristic Evolutionary Algorithm for Dynamic High Dimensional Multi-Objective Optimization Problem

Contact Info

Product

Resources

About