Adaptive Mutation Particle Swarm Optimization for Dynamic Channel Assignment Problems

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.