Meta-heuristic Genetic Algorithm Research Articles

Potential game for joint channel and power allocation in cognitive radio networks

Presented is a game theoretic solution for joint channel and power allocation in cognitive radio networks. This issue has been previously addressed, but the proposed games assume a global knowledge of the players' strategies, which make the solution not scalable. In contrast, here the problem is formulated as a potential game only requiring local information. The obtained results are compared with a metaheuristic genetic algorithm to show the correctness of the proposal.

Combining metaheuristics and ILP for solving the car sequencing problem

In this paper we propose two forms of hybridization between a metaheuristic (Ant Colony Optimization or Genetic Algorithm) and an exact method (ILP) for the solution of the car sequencing problem. We examine whether such hybridizations can improve solution quality over what can be obtained from the alternative approach of using local search within one of these metaheuristics. We present a computational experiment based on the CSPLib benchmarks. The results presented show that the use of hybrid approaches provides a significant improvement and that the performance of the heuristic or metaheuristic that the hybridization is based on greatly influences the quality of the results, however, computations time are long. We conclude that diversification in a population-based metaheuristic plays an important role in the hybridization.

RESOURCE MANAGEMENT IN WIDEBAND CDMA SYSTEMS USING GENETIC ALGORITHMS

In this paper, we study the resource management problem in Direct Sequence-Wideband Code Division Multiple Access (DS-WCDMA) systems. The control variables, transmission power, and transmission rate for resource management are considered. Three meta-heuristic techniques, Genetic Algorithms (GA), Simulated Annealing (SA), and Tabu Search (TS), are utilized to solve this optimization problem. Also, a nonlinear programming technique, Generalized Reduced Gradient (GRG) method, is adopted to compare with the three meta-heuristic techniques. Two approaches, the single objective approach and the multiobjective approach, are used in the simulation. The results obtained by GA, SA, TS, and GRG are compared in a single objective approach. In a multiobjective approach, Multiobjective Genetic Algorithm (MOGA) is employed to compare with the other two well-known multiobjective evolutionary algorithms (MOEAs), Pareto Archived Evolution Strategy (PAES) and Micro-Genetic Algorithms (MICRO-GA). Two scenarios, scenario (a): 25 users and scenario (b): 50 users, are considered for both approaches. The simulation results of a single objective approach show that GA outperforms SA, TS, and GRG in the two scenarios. Also, the simulation results of a multiobjective approach show that MOGA outperforms PAES and MICRO-GA, and obtains nondominated trade-off solutions with better convergence and diversity between trade minimization of total power and maximization of total rate in two scenarios.