Dispersive Flies Optimisation

One of the main sources of inspiration for techniques applicable to complex search space and optimisation problems is nature. This paper proposes a new metaheuristic - Dispersive Flies Optimisation or DFO - whose inspiration is beckoned from the swarming behaviour of flies over food sources in nature. The simplicity of the algorithm, which is the implementation of one such paradigm for continuous optimisation, 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 well-known population based algorithms (Particle Swarm Optimisation, Differential Evolution algorithm and Genetic Algorithm). 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.