Dispersive flies optimisation and medical imaging

Abstract

One of the main sources of inspiration for techniques applicable to complex search space and optimisation problems is nature. This paper introduces a new metaheuristic—Dispersive Flies Optimisation (DFO)—whose inspiration is beckoned from the swarming behaviour of flies over food sources in nature. The simplicity of the algorithm facilitates the analysis of its behaviour. A series of experimental trials confirms the promising performance of the optimiser over a set of benchmarks, as well as its competitiveness when compared against three other wellknown population based algorithms. The convergence-independent diversity of DFO algorithm makes it a potentially suitable candidate for dynamically changing environment. In addition to diversity, the performance of the newly introduced algorithm is investigated using the three performance measures of accuracy, efficiency and reliability and its outperformance is demonstrated in the paper. Then the proposed swarm intelligence algorithm is used as a tool to identify microcalcifications on the mammographs. This algorithm is adapted for this particular purpose and its performance is investigated by running the agents of the swarm intelligence algorithm on sample mammographs whose status have been determined by the experts. Two modes of the algorithms are introduced in the paper, each providing the clinicians with a different set of outputs, highlighting the areas of interest where more attention should be given by those in charge of the care of the patients.