Collaboration of heterogenous metaheuristic agents.
ABSTRACT Collaboration of metaheuristic agents remains as a problem, which attracts challenges to overcome in order to manage a better collaboration in problem solving methodologies. Swarm intelligences techniques have been tried as collaborating methods with good records for homogeneous metaheuristic agents. On the other hand, heterogeneous methods also attracts attention of researchers for better performance in problem solving. This paper discusses the performance of particle swarm optimisation algorithms for collaborating metaheuristic agents with consideration of homogeneity in agents.
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ABSTRACT: Multiuser scheduling is an important aspect in the performance optimisation of a wireless network as it allows multiple users to efficiently access a shared channel by exploiting multiuser diversity. For example, the 3GPP cellular standard supports multiuser scheduling in the high speed downlink packet access (HSDPA) feature. To perform efficient scheduling, channel state information (CSI) for users is required, and is obtained via their respective feedback channels. Multiuser scheduling is studied assuming the availability of perfect CSI, which would require a high bandwidth overhead. A more realistic imperfect CSI feedback in the form of a finite set of channel quality indicator values is assumed, as specified in the HSDPA standard. A global optimal approach and a simulated annealing (CSA) approach are used to solve the optimisation problem. Simulation results suggest that the performances of the two approaches are very close even though the complexity of the simulated annealing (SA) approach is much lower. The performance of a simple greedy approach is found to be significantly worse.IET Communications 09/2009; · 0.64 Impact Factor
Article: Ant system for job-shop scheduling[show abstract] [hide abstract]
ABSTRACT: The study of natural processes has inspired several heuristic optimization algorithms which have proved to be very effective in combinatorial optimization. In this paper we show how a new heuristic called ant system, in which the search task is distributed over many simple, loosely interacting agents, can be successfully applied to find good solutions of job-shop scheduling problems.STATISTICS AND COMPUTER SCIENCE. 01/1994;
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ABSTRACT: In this paper we propose two multi-agent systems gathering several metaheuristics for the K-Graph Partitioning Problem(K-GPP). In the first model COSATS, two metaheuristic agents, namely Tabu Search and Simulated Annealing run simultaneously to solve the K-GPP. These agents are mutually guided during their search process by means of a new mechanism of information exchange based on statistical analysis of search space. In the second model X-COSATS, a crossover agent is added to the model in order to make a crossover between the local optima found by simulated annealing and tabu agents. COSATS and X-COSATS are tested on several large graph benchmarks. The experiments demonstrated that our models achieve partitions with significantly higher quality than those generated by simulated annealing and tabu search operating separately.Knowledge-Based Intelligent Information and Engineering Systems, 9th International Conference, KES 2005, Melbourne, Australia, September 14-16, 2005, Proceedings, Part IV; 01/2005