Cloud Model-Based Differential Evolution Algorithm for Optimization Problems

Zhu, Changming; Ni, Jun

doi:10.1109/icicse.2012.57

Cited by 11 publications

(6 citation statements)

References 13 publications

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“…Cloud model-based algorithm (CMBA) was recently proposed in Wang et al (2012);Zhu and Ni (2012); Sun et al (2012);Zhang et al (2008). To implement the CMBA algorithm, the following steps need to be performed Zhu and Ni 2012):…”

Section: Fundamentals Of Cloud Model-based Algorithmmentioning

confidence: 99%

“…By setting up these variables according to the actual situation, the cloud drops of x and membership are given through Eqs. 24.18-24.20, respectively (Sun et al 2012;Zhu and Ni 2012)::18Þwhere E x indicates the cloud drops' distribution in domain, E n denotes not only the fuzziness of the concept but also the randomness and their relationships, and H e represents the coagulation of uncertainty of all points.• Cloud generator: In CMBA, two sub-generators, namely, backward cloud generator and forward cloud generator are designed based on the cloud production and direction computing mechanism. According the values of three variables (i.e., E x , E n , and H e ), the forward cloud generator can create the cloud drops x; l ð Þ, and the backward cloud generator can convert quantity values to a quality concept.…”

mentioning

confidence: 97%

“…• Cloud model and cloud drop: In CMBA, the distribution of x in domain is referred to as cloud model (or cloud in short) and each x is thus called a cloud drop which can be expresses via Eq. 24.17 (Sun et al 2012;Zhu and Ni 2012):where U stands for a quantity domain expressed with accurate numbers, x denotes a random realization of the quality concept, and l x ð Þ indicates the membership degree of x.• Cloud model's numerical characteristics: In CMBA, the variables of expectation ðE x Þ, entropy ðE n Þ, and hyper-entropy ðH e Þ are used to express the cloud model's numerical characteristics. By setting up these variables according to the actual situation, the cloud drops of x and membership are given through Eqs.…”

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Emerging Physics-based CI Algorithms

Xing

Gao²

2013

Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms

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In this chapter, a set of (more specifically 22 in total) emerging physicsbased computational intelligence (CI) algorithms are introduced. We first, in Sect. 24.1, describe the organizational structure of this chapter. Then, from Sects. 24.2 to 24.23, each section is dedicated to a specific algorithm which falls within this category. The fundamentals of each algorithm and their corresponding performances compared with other CI algorithms can be found in each associated section. Finally, the conclusions drawn in Sect. 24.24 closes this chapter. IntroductionSeveral novel physics-based algorithms were detailed in previous chapters. In particular, Chap. 18 detailed the big bang-big crunch algorithm, Chap. 19 was dedicated to central force optimization algorithm, Chap. 20 discussed the charged system search algorithm, Chap. 21 introduced the electromagnetism-like mechanism algorithm, Chap. 22 was devoted to the gravitational search algorithm, and Chap. 23 described the intelligent water drops algorithm. Apart from this quasimature physics principles inspired CI methods, there are some emerging algorithms also fall within this category. This chapter collects 22 of them that are currently scattered in the literature and organizes them as follows: The effectiveness of these newly developed algorithms are validated through the testing on a wide range of benchmark functions and engineering design problems, and also a detailed comparison with various traditional performance leading CI algorithms, such as particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE), evolutionary algorithm (EA), fuzzy system (FS), ant colony optimization (ACO), and simulated annealing (SA). Artificial Physics Optimization AlgorithmIn this section, we will introduce an emerging CI algorithm that is based on artificial physics or physicomimetics, a concept which was introduced in Spears et al. (2004a, b); Spears and Gordon (1999); Spears and Spears (2012). Fundamentals of Artificial Physics Optimization AlgorithmArtificial physics optimization (APO) algorithm was recently proposed in Xie et al. ( , b, 2010aXie et al. ( , b, 2011a, . Several APO applications and variants can also be found in the literature Popov 2012, 2013; Zeng 2009b, 2011;Mo and Zeng 2009; Wang and Zeng 2010a, b;Yang et al. 2010;Yin et al. 2010; Xie et al. 2011c, d;Wang et al. 2011). To implement the APO algorithm, the following steps need to be performed Biswas et al. 2013):• Initialization step: At this step, a swarm of individuals is randomly generated in the n-dimensional decision space. 376 24 Emerging Physics-based CI Algorithms• Force calculation step: At this step, according to the masses and distances between individual particles, the total force exerted on each particle is computed. In APO, the mass is defined via Eq. 24.1 Biswas et al. 2013):The force is then calculated through Eq. 24.2 Biswas et al. 2013):ð24:2ÞThe kth component of the total force F i;k exerted on individual i by all other individuals is acquired via Eq. 24.3...

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Section: Fundamentals Of Cloud Model-based Algorithmmentioning

confidence: 99%

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Emerging Physics-based CI Algorithms

Xing

Gao²

2013

Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms

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“…[24] is found used in different research works. One example is the application of cloud model for optimization problems [59], in which a definition of Cloud and Cloud drop is given as follows:…”

Section: ) Cloud and Cloud Dropmentioning

confidence: 99%

CCM-MADM: An approach to detect compromised Twitter accounts using Cognitive Cloud Model and Multi-Attribute Decision Making

Savyan

2023

Preprint

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Account compromising is a leading security chal-lenge to Online social networks. The compromised Online SocialNetwork accounts are used for malicious activities. The challengesposed by compromised accounts are more severe than many othersecurity attacks because because once an account is compromised,the attacker gains complete access to the account. The attackersmake use of the well established trusted network associated withthe compromised accounts. One of the challenges in detectingcompromised twitter account is the uncertainty inherent to thetweets. Another challenge is the complexity in user behaviourexhibited in various attributes related to the user account andtweets.Existing works found in literature fails to address theuncertainity in tweets. The aim of this paper is to proposesan approach to detect compromised twitter accounts usinga combined approach of machine learning and multi-criteriadecision making. In order to address the uncertaininy of tweetscognitive clound model is applied. New features are derived in thiswork to represent tweets.This proposed approach exhibits betteraccuracy compared to Weighted Sum method and WeightedProduct method comes under the multi criteria decision makingmethods and with random forest classifier.

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“…However, these algorithms usually converge prematurely and are prone to finite optimally. When approaching the optimal solution, it may also swing left and right, making the convergence slower [15]. In genetic algorithms, the crossover operators become the main operators because of its global search ability and mutation operator is to become the auxiliary operator because of its local search ability.…”

Section: Introductionmentioning

confidence: 99%

A Task Scheduling Strategy in Edge-Cloud Collaborative Scenario Based on Deadline

Wang

Pang

et al. 2020

Scientific Programming

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Task scheduling plays a critical role in the performance of the edge-cloud collaborative. Whether the task is executed in the cloud and how it is scheduled in the cloud is an important issue. On the basis of satisfying the delay, this paper will schedule tasks on edge devices or cloud and present a task scheduling algorithm for tasks that need to be transferred to the cloud based on the catastrophic genetic algorithm (CGA) to achieve global optimum. e algorithm quantifies the total task completion time and the penalty factor as a fitness function. By improving the roulette selection strategy, optimizing mutation and crossover operator, and introducing cataclysm strategy, the search scope is expanded. Furthermore, the premature problem of the evolutionary algorithm is effectively alleviated. e experimental results show that the algorithm can address the optimal local issue while significantly shortening the task completion time on the basis of satisfying tasks delays.

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Cloud Model-Based Differential Evolution Algorithm for Optimization Problems

Cited by 11 publications

References 13 publications

Emerging Physics-based CI Algorithms

Emerging Physics-based CI Algorithms

CCM-MADM: An approach to detect compromised Twitter accounts using Cognitive Cloud Model and Multi-Attribute Decision Making

A Task Scheduling Strategy in Edge-Cloud Collaborative Scenario Based on Deadline

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