scholarly journals Computational Intelligence Based Simulated Annealing Guided Key Generation In Wireless Communication (CISAKG)

2013 ◽  
Vol 2 (4) ◽  
pp. 35-44 ◽  
Author(s):  
Arindam Sarkar ◽  
J. K Mandal
Keyword(s):  
Simulated Annealing ◽  
Wireless Communication ◽  
Computational Intelligence ◽  
Key Generation

Linkage Synthesis of a Four-Bar Mechanism for N Precision Points Using Simulated Annealing

1998 ◽  
Author(s):  
Horacio Martínez-Alfaro ◽  
Homero Valdez ◽  
Jaime Ortega
Keyword(s):  
Simulated Annealing ◽  
Computational Intelligence ◽  
Simulated Annealing Algorithm ◽  
Alternative Synthesis ◽  
Computational Intelligence Technique ◽  
Annealing Algorithm ◽  
Four Bar Linkage ◽  
Generation Problem ◽  
Intelligence Technique ◽  
Linkage Synthesis

Abstract This paper presents an alternative way of linkage synthesis by using a computational intelligence technique: Simulated Annealing. The technique allows to define n precision points of a desired path to be followed by a four-bar linkage (path generation problem). The synthesis problem is transformed into an optimization one in order to use the Simulated Annealing algorithm. With this approach, a path can be better specified since the user will be able to provide more “samples” than the usual limited number of five allowed by the classical methods. Several examples are shown to demonstrate the advantages of this alternative synthesis technique.

Computational Intelligence in Cross Docking

2014 ◽  
Vol 2 (1) ◽  
pp. 1-8
Author(s):  
Bo Xing
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Simulated Annealing ◽  
Tabu Search ◽  
Computational Intelligence ◽  
Simple Concept ◽  
Simulated Annealing Genetic Algorithm ◽  
Cross Docking ◽  
Open Questions ◽  
Key Issues

Cross docking is a practice in logistics with the main operations of goods flow directly from receiving to the shipping docks without stopping or being put away into storage. It is a simple concept to talk about, but a challenging one to implement. So far, many different approaches have been followed in order to improve the efficiency of a cross docking system. However, as the complexity increases, the use of computational intelligence (CI) in those problems is becoming a unique tool of imperative value. In this paper, different CI methods, such as Tabu search, simulated annealing, genetic algorithm, and fuzzy logic. The key issues in implementing the proposed approaches are discussed, and finally the open questions are highlighted.

scholarly journals Detection of Spoofed IP nodes using BAT Algorithm and Extreme Learning Machine

2019 ◽  
Vol 9 (2) ◽  
pp. 771-778
Keyword(s):  
Wireless Communication ◽  
Extreme Learning Machine ◽  
Computational Intelligence ◽  
Performance Metrics ◽  
Delivery Ratio ◽  
Cyber Attack ◽  
Literature Study ◽  
Network Bandwidth ◽  
Different Types ◽  
Learning Machine

IP spoofing is known as the most important cyber-attack which is the source for DoS or DDoS attacks where the attacker is hidden inside the network and makes the computer resource services unavailable to the users. The attacker once done with spoofing the IP address will start to flood the system with keeping on sending requests and make the network bandwidth slow to the extent. This paper contains the literature study of the different types of defence mechanisms from different authors used few decades before to detect and mitigate the Spoofed IP nodes at router, host level and recently some author come up with ideas of using computational intelligence methods for detecting the different types of attacks in wireless communications which results in accurate prediction. This paper provides creating a threat model of detecting the Spoofed IP nodes among 105 network wireless communication scenario using computational intelligence algorithm, the features are selected from the simulated raw data and preprocessed by using BAT optimization algorithm and features are converted to ELM readable format and then they are trained and learned using Extreme learning machine algorithm to predict the accurate detection of the Spoofed IP nodes in the wireless communication network scenario. The proposed method provides high accuracy in detection of Spoofed IP nodes with respect to some performance metrics like end to end delay, throughput, packet delivery ratio, packet drop ratio and it is compared with the KNN-SVM exiting model proved the results.

Particle Swarm Optimization-Based Session Key Generation for Wireless Communication (PSOSKG)

2015 ◽  
pp. 640-677 ◽  
Author(s):  
Arindam Sarkar ◽  
Jyotsna Kumar Mandal
Keyword(s):  
Particle Swarm Optimization ◽  
Wireless Communication ◽  
Velocity Vector ◽  
Particle Swarm ◽  
Threshold Value ◽  
Key Generation ◽  
Session Key ◽  
Swarm Optimization ◽  
Minimum Probability ◽  
Velocity Level

In this chapter, a Particle Swarm Optimization-Based Session Key Generation for wireless communication (PSOSKG) is proposed. This cryptographic technique is solely based on the behavior of the particle swarm. Here, particle and velocity vector are formed for generation of keystream by setting up the maximum dimension of each particle and velocity vector. Each particle position and probability value is evaluated. Probability value of each particle can be determined by dividing the position of a particular particle by its length. If probability value of a particle is less than minimum probability value then a velocity is applied to move each particle into a new position. After that, the probability value of the particle at the new position is calculated. A threshold value is selected to evaluate against the velocity level of each particle. The particle having the highest velocity more than predefined threshold value is selected as a keystream for encryption.

A computational intelligence based approach for optimized operation scheduling of energy plants

2020 ◽  
Vol 68 (2) ◽  
pp. 118-129
Author(s):  
Johannes Mast ◽  
Stefan Rädle ◽  
Joachim Gerlach ◽  
Oliver Bringmann
Keyword(s):  
Simulated Annealing ◽  
Power Plant ◽  
Computational Intelligence ◽  
Electricity Market ◽  
Operating Mode ◽  
Combined Heat And Power ◽  
Operation Planning ◽  
Operation Schedule ◽  
Operation Scheduling ◽  
Energy Plants

AbstractThis paper describes a methodology for optimizing the operation schedule of energy plants, which is exemplarily applied for a combined heat and power plant and a heat pump. The methodology is based on the computational intelligence algorithms Ant Colony Optimization and Simulated Annealing and allows a customized description of the optimization objective. This is demonstrated by several optimization objectives that have been considered, such as the price on the electricity market. The methodology replaces a conventional, guided operating mode of the system with an intelligent, prognostic-based operation planning. In this way, the systems can be operated more economically and/or more sustainably.

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