Abstract
Swift solving of geoacoustic inverse problems strongly depends on the application of a global optimization scheme. Given a particular inverse problem, this work aims to answer the questions how to select an appropriate metaheuristic search strategy, and how to configure it for optimal performance. Four state-of-the-art metaheuristics have been selected for this study; these are simulated annealing, genetic algorithms, ant colony optimization, and differential evolution. To make a careful comparison, each of these metaheuristic optimizers has been configured for two real-world geoacoustic inverse problems. The influence and sensitivity of specific performance parameters have been studied by analysis of repeated problem-solving. It is concluded that a proper configuration and tuning is just as important as selection of the best optimization scheme. The application in this work is geoacoustic inversion, but the argumentation on selecting and configuring an appropriate metaheuristic has potential for any indirect inverse problem.
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