CODEA : an agent based multi-objective optimization framework

This work presents CODEA, a COoperative DEcentralized Architecture for Multiobjective Optimization. CODEA is an objectoriented framework that aims at the creation of groups of agents to tackle complex problems by cooperative search. This cooperation is carried out without any individual controlling the cooperation nor the behaviour of the agents. Each agent works on its own to improve itself and collaborates to improve the performance of the group by sharing information

Group Leaders Optimization Algorithm

We present a new global optimization algorithm in which the influence of the leaders in social groups is used as an inspiration for the evolutionary technique which is designed into a group architecture. To demonstrate the efficiency of the method, a standard suite of single and multidimensional optimization functions along with the energies and the geometric structures of Lennard-Jones clusters are given as well as the application of the algorithm on quantum circuit design problems. We show that as an improvement over previous methods, the algorithm scales as N^2.5 for the Lennard-Jones clusters of N-particles. In addition, an efficient circuit design is shown for two qubit Grover search algorithm which is a quantum algorithm providing quadratic speed-up over the classical counterpart

Joint Cooperative Spectrum Sensing and MAC Protocol Design for Multi-channel Cognitive Radio Networks

In this paper, we propose a semi-distributed cooperative spectrum sen sing (SDCSS) and channel access framework for multi-channel cognitive radio networks (CRNs). In particular, we c onsider a SDCSS scheme where secondary users (SUs) perform sensing and exchange sensing outcomes with ea ch other to locate spectrum holes. In addition, we devise the p -persistent CSMA-based cognitive MAC protocol integrating the SDCSS to enable efficient spectrum sharing among SUs. We then perform throughput analysis and develop an algorithm to determine the spectrum sensing and access parameters to maximize the throughput for a given allocation of channel sensing sets. Moreover, we consider the spectrum sensing set optimization problem for SUs to maxim ize the overall system throughput. We present both exhaustive search and low-complexity greedy algorithms to determine the sensing sets for SUs and analyze their complexity. We also show how our design and analysis can be extended to consider reporting errors. Finally, extensive numerical results are presented to demonstrate the sig nificant performance gain of our optimized design framework with respect to non-optimized designs as well as the imp acts of different protocol parameters on the throughput performance.Comment: accepted for publication EURASIP Journal on Wireless Communications and Networking, 201