Hafiz Selamat

Analysis on the performance of mobile agents for query retrieval

The factors that affect the performance of mobile agents in retrieving information from the Internet are the number of agents and the total of routing time taken by the participated agents to complete the assigned tasks. Fewer numbers of mobile agents used to execute the tasks will cause lower network traffic and consume less bandwidth, and the total time taken to retrieve the query results can be minimized. In this paper, the performance of mobile agents for query retrieval is analyzed. Specifically, the performance of mobile agent in obtaining a query result from the remote hosts using an extended hierarchical query retrieval (EHQR) approach is proposed. It is based on a hierarchical and a parallel dispatching of mobile agents to the remote servers in order to retrieve the query results. Experimental results show that the proposed approach reduces the number of mobile agents and also improve the total time taken to retrieve the query results compared with other approaches.

A fast path planning algorithm for route guidance system

Path planning is applied in a variety of ways, including transportation, telecommunications, etc. Path planning to direct vehicles to their destination in a dynamic traffic situation, with the aim of reducing the motoring time and to ensure and efficient use of available road resources is the main challenge in route guidance system. In this paper we propose a fast path algorithm for finding the best shortest paths in the road network. This is poised to minimize costs between the origin and destination nodes. The proposed algorithm was compared with the Dijkstra algorithm in order to find the best and shortest paths using a sample of Tehran city road network. Three cases were tested through simulation using the proposed algorithm. The results show that the efficiency of proposed algorithm and could reduce the cost of vehicle routing on the path planning problems.

Route guidance system using multi-agent reinforcement learning

Nowadays, the problems of urban traffic in most big cities are more complex. Increasing population and road requirements has caused the complexity in traffic management systems. The main challenge for network traffic is to direct vehicles to their destination with the aim of reducing travel times and efficient use of available network capacity. This paper proposes a new agent model and algorithm based on multi-agent reinforcement learning to find a best and shortest path between the origin and destination nodes. Furthermore, the proposed algorithm is compared with Dijkstra algorithm to find optimal solution using some simple real sample of Kuala Lumpur (KL) road network map. Experimental results affirmed the same results to find the optimal solutions.

An Artificial Immune System for recommending relevant information through political weblog

These days, when we want to get the relevant and useful information on political issues from the web, it is important for the Internet users to understand the current political situations in the country. Most of Internet users are using applications like web mining system in order to help them in finding all relevant information available in the political weblog. Based on this problem, we have developed a web mining system called WMAIS (web mining using Artificial Immune System). The objective of the WMAIS is to recommend the relevant and interesting information on political issues in Malaysia through political weblogs to the users. From the experiment that have been done using statistic tool called Student T-test, it shows that by using the term frequency scheme in WMAIS, it has managed to recommend relevant and interesting information through political weblogs to the user.

Route guidance system based on self adaptive multiagent algorithm

Nowadays, self-adaptive multi-agent systems are applied in a variety of areas, including transportation, telecommunications, etc. The main challenge in route guidance system is to direct vehicles to their destination in a dynamic traffic situation, with the aim of reducing the traveling time and to ensure and efficient use of available road networks capacity. In this paper we propose a self-adaptive multi-agent algorithm for managing the shortest path routes that will improve the acceptability of the costs between the origin and destination nodes. The proposed algorithms have been compared with Dijkstra algorithm in order to find the best and shortest paths using a sample of Tehran road network map. Two cases have been tested on the simulation using the proposed algorithm. The experimental results demonstrate that the proposed algorithm could reduce the cost of vehicle routing problem.

Route Guidance System Based on Self-Adaptive Algorithm

Self-adaptive systems are applied in a variety of ways, including transportation, telecommunications, etc. The main challenge in route guidance system is to direct vehicles to their destination in a dynamic traffic situation, with the aim of reducing the motoring time and to ensure an efficient use of available road resources. In this paper, we propose a self-adaptive algorithm for managing the shortest paths in route guidance system. This is poised to minimize costs between the origin and destination nodes. The proposed algorithm was compared with the Dijkstra algorithm in order to find the best and shortest paths using a sample simplified real sample of Kuala-Lumpur (KL) road network map. Four cases were tested to verify the efficiency of our approach through simulation using the proposed algorithm. The results show that the proposed algorithm could reduce the cost of vehicle routing and associated problems.