Applying Ant Colony Optimization to the Partitioned Scheduling Problem for Heterogeneous Multiprocessors

The problem of determining whether a set of periodic tasks can be assigned to a set of heterogeneous processors in such a way that all timing constraints are met has been shown, in general, to be NP-hard. This paper presents a new algorithm based on Ant Colony Optimization (ACO) metaheuristic for solving this problem. Experimental results show that our ACO approach can outperform the major existing methods. In addition to being able to search for a feasible assignment solution, our ACO approach can further optimize the solution to reduce its energy consumption. 1

ACODV : Ant Colony Optimisation Distance Vector routing in ad hoc networks

A mobile ad hoc network is a collection of wireless mobile devices which dynamically form a temporary network, without using any existing network infrastructure or centralised administration. Each node in the network effectively becomes a router, and forwards packets towards the packet’s destination node. Ad hoc networks are characterized by frequently changing network topology, multi-hop wireless connections and the need for dynamic, efficient routing protocols. The overarching requirement for low power consumption, as battery powered sensors may be required to operate for years without battery replacement; An emphasis on reliable communication as opposed to real-time communication, it is more important for packets to arrive reliably than to arrive quickly; and Very scarce processing and memory resources, as these sensors are often implemented on small low-power microprocessors. This work provides overviews of routing protocols in ad hoc networks, swarm intelligence, and swarm intelligence applied to ad hoc routing. Various mechanisms that are commonly encountered in ad hoc routing are experimentally evaluated under situations as close to real-life as possible. Where possible, enhancements to the mechanisms are suggested and evaluated. Finally, a routing protocol suitable for such low-power sensor networks is defined and benchmarked in various scenarios against the Ad hoc On-Demand Distance Vector (AODV) algorithm.Dissertation (MSc)--University of Pretoria, 2005.Computer ScienceUnrestricte

Construction-based metaheuristics for personnel scheduling problems

This thesis investigates the idea of balancing different constraints in order to find optimal solutions to two personnel scheduling problems, within the framework of constructive metaheuristic approaches. The two problems considered are a nurse scheduling problem, for which finding feasible solutions is known to be difficult and for which the hard and soft constraints are in direct conflict, and a medical student scheduling problem for which there is little relevant literature this second problem also has conflicting hard and soft constraints, but presents further conflict between the different soft constraints. The methods used to solve these problems are focused on two constructive metaheuristics in particular: Greedy Randomised Adaptive Search Procedures (GRASP) and Ant Colony Optimisation (ACO) and for each approach several construction heuristics are introduced and compared. Using GRASP, a number of local search neighbourhoods are established for each problem, while for ACO the suitability of three trail definitions are compared. In order to further explore the balance which may obtained between the different constraints and objectives for the two problems, hybrid constructions are investigated, incorporating exact methods which take advantage of the underlying structures of each problem with regards to feasibility. For medical student scheduling, this exact method was developed into a new type of construction mechanism providing much improved results over a standard heuristic approach. Further enhancements investigated include the use of problem-specific feedback for nurse scheduling and the use of an intelligent memory procedure for the medical student scheduling problem. For the nurse scheduling problem, the final algorithm developed was able to rival the best in the literature so far and produce optimal solutions for all available datasets. For the medical student scheduling problem, optimal solutions are not known, but the results obtained are very promising and provide a good basis for further study of the problem.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Construction-based metaheuristics for personnel scheduling problems.

This thesis investigates the idea of balancing different constraints in order to find optimal solutions to two personnel scheduling problems, within the framework of constructive metaheuristic approaches. The two problems considered are a nurse scheduling problem, for which finding feasible solutions is known to be difficult and for which the hard and soft constraints are in direct conflict, and a medical student scheduling problem for which there is little relevant literature this second problem also has conflicting hard and soft constraints, but presents further conflict between the different soft constraints. The methods used to solve these problems are focused on two constructive metaheuristics in particular: Greedy Randomised Adaptive Search Procedures (GRASP) and Ant Colony Optimisation (ACO) and for each approach several construction heuristics are introduced and compared. Using GRASP, a number of local search neighbourhoods are established for each problem, while for ACO the suitability of three trail definitions are compared. In order to further explore the balance which may obtained between the different constraints and objectives for the two problems, hybrid constructions are investigated, incorporating exact methods which take advantage of the underlying structures of each problem with regards to feasibility. For medical student scheduling, this exact method was developed into a new type of construction mechanism providing much improved results over a standard heuristic approach. Further enhancements investigated include the use of problem-specific feedback for nurse scheduling and the use of an intelligent memory procedure for the medical student scheduling problem. For the nurse scheduling problem, the final algorithm developed was able to rival the best in the literature so far and produce optimal solutions for all available datasets. For the medical student scheduling problem, optimal solutions are not known, but the results obtained are very promising and provide a good basis for further study of the problem