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dc.contributor.authorAlratrout, Serein Abdelmonam.
dc.date.accessioned2009-09-02T10:20:51Z
dc.date.available2009-09-02T10:20:51Z
dc.date.issued2009
dc.identifier.urihttp://hdl.handle.net/2086/2409
dc.description.abstractAgent-based computing has attracted much attention as a promising technique for application domains that are distributed, complex and heterogeneous. Current research on multi-agent systems (MAS) has become mature enough to be applied as a technology for solving problems in an increasingly wide range of complex applications. The main formal architectures used to describe the relationships between agents in MAS are centralised and distributed architectures. In computational complexity theory, researchers have classified the problems into the followings categories: (i) P problems, (ii) NP problems, (iii) NP-complete problems, and (iv) NP-hard problems. A method for computing the solution to NP-hard problems, using the algorithms and computational power available nowadays in reasonable time frame remains undiscovered. And unfortunately, many practical problems belong to this very class. On the other hand, it is essential that these problems are solved, and the only possibility of doing this is to use approximation techniques. Heuristic solution techniques are an alternative. A heuristic is a strategy that is powerful in general, but not absolutely guaranteed to provide the best (i.e. optimal) solutions or even find a solution. This demands adopting some optimisation techniques such as Evolutionary Algorithms (EA). This research has been undertaken to investigate the feasibility of running computationally intensive algorithms on multi-agent architectures while preserving the ability of small agents to run on small devices, including mobile devices. To achieve this, the present work proposes a new Hybrid Multi-Agent Architecture (HMAA) that generates new heuristics for solving NP-hard problems. This architecture is hybrid because it is "semi-distributed/semi-centralised" architecture where variables and constraints are distributed among small agents exactly as in distributed architectures, but when the small agents become stuck, a centralised control becomes active where the variables are transferred to a super agent, that has a central view of the whole system, and possesses much more computational power and intensive algorithms to generate new heuristics for the small agents, which find optimal solution for the specified problem. This research comes up with the followings: (1) Hybrid Multi-Agent Architecture (HMAA) that generates new heuristic for solving many NP-hard problems. (2) Two frameworks of HMAA have been implemented; search and optimisation frameworks. (3) New SMA meeting scheduling heuristic. (4) New SMA repair strategy for the scheduling process. (5) Small Agent (SMA) that is responsible for meeting scheduling has been developed. (6) “Local Search Programming” (LSP), a new concept for evolutionary approaches, has been introduced. (7) Two types of super-agent (LGP_SUA and LSP_SUA) have been implemented in the HMAA, and two SUAs (local and global optima) have been implemented for each type. (8) A prototype for HMAA has been implemented: this prototype employs the proposed meeting scheduling heuristic with the repair strategy on SMAs, and the four extensive algorithms on SUAs. The results reveal that this architecture is applicable to many different application domains because of its simplicity and efficiency. Its performance was better than many existing meeting scheduling architectures. HMAA can be modified and altered to other types of evolutionary approaches.en
dc.language.isoenen
dc.publisherDe Montfort Universityen
dc.subjectmeeting schedulimgen
dc.subjectLocal searchen
dc.subjectgenetic programmingen
dc.subjectmulti-agent systemsen
dc.subjectheuristics for meeting schedulingen
dc.titleA Hybrid multi-agent architecture and heuristics generation for solving meeting scheduling problemen
dc.typeThesis or dissertationen
dc.publisher.departmentSoftware Technology Research Laboratory (STRL)en
dc.type.qualificationlevelDoctoralen
dc.type.qualificationnamePhDen


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