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This paper presents a new approach to the multi-agent coverage path-planning problem. An efficient multi-robot coverage algorithm yields a coverage path for each robot, such that the union of all paths generates an almost full coverage of the terrain and the total coverage time is minimized. The proposed algorithm enables multiple robots with limited sensor capabilities to perform efficient coverage...
This paper presents an investigation into the development of a multi-objective optimal chemotherapy control model to reduce the number of cancer cells after a number of fixed treatment cycles with minimum side effects. A phase specific drug scheduling method using a close-loop control method with multi-objective techniques is proposed in this paper. Genetic Algorithm (GA) and particle swarm optimisation...
This paper introduces a novel adaptation scheme of mutation step size for the Artificial Bee Colony algorithm and compares its results with a number of swarm intelligence and population based optimization algorithms on complex multimodal benchmark problems. The Artificial Bee Colony (ABC) is a swarm based optimization algorithm mimicking the intelligent food foraging behavior of honey bees. The proposed...
This paper presents a novel method of chemotherapy drug scheduling for cancer treatment using feedback control and genetic algorithm (GA). The main aim of chemotherapy treatment is to eradicate the tumour, if possible, or to reduce the tumour size to a minimum level with minimum toxic side effects. A feedback control method is developed in order to maintain a predefined level of drug concentration...
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