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Kamal, Hanan A.
- Adaptive Mutation Particle Swarm Optimization for Dynamic Channel Assignment Problems
Authors
1 Electronics and Electrical Communication Engineering, Institute of Aviation Engineering and Technology, I.A.E.T., EG
2 Electronics and Communications Engineering, Cairo University, EG
3 Cairo University, EG
Source
Artificial Intelligent Systems and Machine Learning, Vol 5, No 1 (2013), Pagination: 30-37Abstract
Dynamic Channel Assignment (DCA) assigns the channels to the cells dynamically according to traffic demand, and hence, can provide higher capacity (or lower call blocking probability) than the fixed assignment schemes. Hybrid Channel Assignment (HCA) is a mixture of the FCA and DCA techniques. In HCA, the total number of channels available for service is divided into fixed and dynamic sets. Channel assignment problems are formulated as combinatorial optimization problems and are NP-hard problem. Genetic Algorithm, and Particle Swarm Optimization, proves effective in the solution of Fixed Channel Assignment (FCA) problems but they still require high computational time and therefore may be inefficient for DCA. This paper presents a new optimization technique based on Particle Swarm Optimization (PSO) named Adaptive Mutation Particle Swarm Optimization (AMPSO). An adaptive mutation technique is introduced to increase the diversity in the search space. The proposed AMPSO is applied to solve the Channel Assignment Problem (CAP) for different benchmark problems and different fixed to dynamic ratio. Cloud Model Based Adaptive Mutation Particle Swarm Optimization (CMPSO) technique is used to challenge the proposed technique. Results obtained show that AMPSO creates significant improvement in the blocking probability compared to the other technique. Moreover, AMPSO succeeded to reach a global solution faster than CMPSO.Keywords
Channel Assignment Problem (CAP), Dynamic Channel Assignment (DCA), Electromagnetic Compatibility (EMC), Blocking Probability, Adaptive Mutation Particle Swarm Optimization (AMPSO), Cloud Mutation Particle Swarm Optimization (CMPSO).
- Enhancing Genetic Algorithms using a Dynamic Mutation Value Approach: An Application to the Control of Flexible Robot Systems
Authors
1 Mathematics and Physics Department, Cairo University, Giza, EG
2 Communication Engineering Department, Cairo University, Giza, EG
3 Aeronautical and Aerospace Engineering Department, Cairo University, Giza, EG
Source
Artificial Intelligent Systems and Machine Learning, Vol 4, No 1 (2012), Pagination: 9-16Abstract
This paper presents an investigation into a new optimization technique based on genetic algorithm (GA). A dynamically-changed mutation value approach is introduced to increase the diversity in the search space and avoid premature convergence caused by simple genetic algorithm (SGA). The enhanced genetic algorithm (EGA) is used to tune the feedback gains of a PD controller which controls both the position and vibration of a single-link flexible arm. The dynamic model of the system is derived using Hamilton’s principle and modeled using the finite element method (FEM). A multi-objective function is defined and altered to reach a range of specified system responses and therefore it is shown to be able to satisfy different objectives. Adaptive Genetic Algorithm (AGA) and Cloud Model Based Adaptive Genetic Algorithm (CAGA) techniques are used to challenge the proposed technique. Results obtained show that EGA creates significant improvement in the speed of convergence compared to other techniques. Moreover, the obtained solutions are of higher average fitness values. EGA succeeded to consistently reach a global solution for an objective function that needs rigorous search mechanism which encourages for further application to various control problems, complex mathematical functions and real time applications.
Keywords
Adaptive Genetic Algorithm (AGA), Cloud Model Based Adaptive Genetic Algorithm (CAGA), Enhanced Genetic Algorithm (EGA), Genetic Algorithms (GA), Multi-Objective Optimization, PD Controller, Single-Link Flexible Manipulator.- Optimization of HVAC System Using Adaptive Genetic Swarm Algorithm
Authors
1 Cairo University, EG
2 Invensys Engineering & Services, EG
Source
Artificial Intelligent Systems and Machine Learning, Vol 3, No 13 (2011), Pagination: 831-838Abstract
In this paper a new approach is proposed for global energy consumption minimization of heating, ventilating and air conditioning (HVAC) systems. The objective function of global optimization and constraints is formulated based on mathematical models of the major components. A Genetically Swarm Optimization (GSO) algorithm is applied for energy minimization problem which is considered as a new application for GSO. The GSO algorithm combines the standard velocity and updated rules of the Particle Swarm Optimization (PSO) with the ideas of selection and mutation of the Genetic Algorithm (GA). In addition of solving the problem using GSO, a new adaptive mutation operator is presented which actively disperses the population preventing premature convergence. The adaptive genetic swarm optimization (AGSO) algorithm is applied for HVAC energy minimization problem. The results have been compared to the standard GA, adaptive GA, PSO, and GSO models. Results obtained showed that AGSO algorithm is faster in convergence and the obtained solutions have higher average fitness than other techniques.