Enhancing social emotional optimization algorithm using local search

Guo, Zitao; Yue, Xuezhi; Yang, Hua; Liu, Kun; Liu, Xiaosheng

doi:10.1007/s00500-016-2282-z

Cited by 12 publications

(4 citation statements)

References 45 publications

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“…Figure 19 presents the optimization methods based on the evolution process presented in References 132‐135. In this section, brief descriptions of some methods are presented with their related applications for the next‐generation networks.…”

Section: Optimization Methods In Communicationsmentioning

confidence: 99%

Overview of evolutionary algorithms and neural networks for modern mobile communication

Kouhalvandi

Shayea

Özoğuz

et al. 2022

Trans Emerging Tel Tech

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The sixth generation (6G) of mobile networks must support the huge growth of mobile connections and provides various intelligent services in future mobile networks. For that, designing high-performance network architecture and communication systems (CSs) as well as finding coherent solutions for the determined problems is critical demand that needs to be achieved in 6G networks.Optimal solutions are mainly sought by using modifiable, smart, and perceptive algorithms aimed at optimizing more specific tasks. Therefore, advanced optimization methods are highly required to accommodate the requirements of CSs efficiently. That will be in various parts of the future networks such as advanced mobility management, multi communication links, efficient power consumption, ultra-lower latency, ultra-security, high speed, and reliable connectivity.Accordingly, highly accurate and smart functions must be modeled to optimize the required communication parameters in the network. This study provides a comprehensive overview of optimization methods that may need further investigations and developments to be applied in 6G networks at various parts of networks. A detailed theoretical description for each method is presented and discussed to elucidate future research directions for optimizing specific characteristics with multi-objective optimizations. Moreover, this article illustrates the conceptual and structural viewpoints of reported optimization methods. Also, the capability of various optimization methods that can offer industrial solutions in 6G is discussed. The potential applications of each method are also analyzed. Finally, this article presented the research issues and future directions of optimization technology and research gaps that need to be addressed before the standardization of 6G networks. INTRODUCTIONRecently, innovative solutions of communication systems (CSs) have been developed impressively, as data traffic is increasing exponentially by more than 50% per year. 1 Several factors will contribute for increasing data demand massively, for example, as predicted by CISCO, the video will make up 82% of the all IP traffic by 2022. 2,3 This massive increase in data traffic will cause several new issues that will facing the implementation of future mobile networks. That will include a dramatic increase for wider spectrum need, 4 the necessity for big system capacity, 5 further mobility issues, 6,7 and higher energy consumption, 8 in which cannot be handled efficiently by the current cellular networks.

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Section: Optimization Methods In Communicationsmentioning

confidence: 99%

Overview of evolutionary algorithms and neural networks for modern mobile communication

Kouhalvandi

Shayea

Özoğuz

et al. 2022

Trans Emerging Tel Tech

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“…In such behavior, an individual's physical activities and nonphysical activities, such as thinking and acting, are critical factors that affect his or her performance [1]. The most representative examples are society and civilization (SC) [32], human-inspired algorithms (HIAs) [33], social-emotional optimization (SEO) [34], brainstorm optimization (BSO) [35], teaching-learning-based optimization (TLBO) [36], harmony Search (HS) [37], and the gaining-sharing knowledgebased algorithm (GSK) [1].…”

Section: Imentioning

confidence: 99%

Crocodile optimization algorithm for solving real-world optimization problems

Yan,

Zhang,

Yang

2024

Preprint

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This paper proposes a novel nature-inspired algorithm, called the crocodile optimization algorithm (COA), which mimics the hunting strategies of crocodiles. Two important hunting processes of crocodiles are built, i.e., premeditation and waiting, during which the crocodile individuals gain and share information so that they can trace the prey; attacking and hunting, in this phase, crocodiles attacking and hunting their prey by implementing the “death roll” strategies. The search mechanisms of the proposed COA are differently compared to the existing methods inspired by the hunting behavior of crocodiles. The performance of the proposed COA is validated by utilizing twenty-nine standard test functions, including unimodal functions, multimodal functions, fixed-dimension multimodal functions, and composite functions, with qualitative and quantitative analysis, and its practical effectiveness in solving real-world problems is evaluated using five engineering optimization problems. The simulation results are compared with 2 algorithms also inspired by the hunting behavior of crocodiles and 9 other algorithms. The results and analysis suggest that COA is a competitive technique in handling unimodal, multimodal, and composite problems, and the Friedman ranking test statistical results revealed that COA is an excellent method for solving different kinds of complex problems. Finally, the outcomes of five engineering applications highlight the superiority and potential of COA in solving challenging real-world problems.

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“…Apart from the existing methods of training wavelet parameters, there are a number of optimization algorithms, including self-adaptive differential evolution [30] and a social emotional algorithm which uses local search function [31] that can be used to optimize the WNN parameters.…”

Section: Introductionmentioning

confidence: 99%

Evolving multi-dimensional wavelet neural networks for classification using Cartesian Genetic Programming

et al. 2017

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Wavelet Neural Networks (WNNs) are complex artificial neural systems and their training can be a challenge. In the past, most common training schemes for WNNs, such as gradient descent, have been restricted to training only a subset of differentiable parameters. In this paper, we propose an evolutionary method to train both differentiable and non-differentiable parameters using the concept of Cartesian Genetic Programming (CGP). The approach was evaluated on the two-spiral task and on real-world datasets for the detection of breast cancer and Parkinson's disease. In our experiments, the performance of the proposed method was comparable to several standard methods of classification. On the breast cancer dataset, the performance was better than other non-ensemble and multistep processing methods. The experimental results show how the performance of WNNs depends on the number of wavelons used. The presented case studies demonstrate that the proposed WNNs perform competitively in comparison to several other methods and results reported in literature.

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Enhancing social emotional optimization algorithm using local search

Cited by 12 publications

References 45 publications

Overview of evolutionary algorithms and neural networks for modern mobile communication

Overview of evolutionary algorithms and neural networks for modern mobile communication

Crocodile optimization algorithm for solving real-world optimization problems

Evolving multi-dimensional wavelet neural networks for classification using Cartesian Genetic Programming

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