Biogeography is the study of the geographical distribution of biological organisms. The mindset of the engineer is that we can learn from nature. Biogeography Based Optimization is a burgeoning nature inspired technique to find the optimal solution of the problem. Satellite image classification is an important task because it is the only way we can know about the land cover map of inaccessible ...

Differential Evolution (DE) is a fast and robust evolutionary algorithm for global optimization. It has been widely used in many areas. Biogeography-Based Optimization (BBO) is a new biogeography inspired algorithm. It mainly uses the biogeography-based migration operator to share the information among solutions. In this paper, we propose a hybrid DE with BBO, namely DE/BBO, for the global nume...

Recent patents and progress on scan chain balance algorithms have been reviewed. With a significant increase of the SoC (System on Chip) integration and scale, the test time of SoC increase dramatically, and this makes the test cost of SoC grow rapidly. In order to reduce test cost and expense, the paper proposes an OBBO (Opposition-based learning and Biogeography Based Optimization) algorithm ...

To obtain high-quality Pareto optimal solutions and to enhance the searchability of biogeography-based optimization (BBO) algorithm, we present an improved BBO algorithm based on hybrid migration a dual-mode mutation strategy (HDBBO). We first adopted more scientific nonlinear hyperbolic tangent mobility model instead conventional linear which can solution closer global minimum function. develo...

Biogeography-based optimization (BBO) is an evolutionary algorithm (EA) based upon the models of biogeography, which describe the relationship between habitat suitability and the migration of species across habitats. In this work, we apply BBO to the problem of tuning the fuzzy tracking controller of mobile robots. This is an extension of previous work, in which we used BBO to tune a proportion...

Hybrid evolutionary algorithms (EAs) are effective optimization methods that combine multiple EAs. We propose several hybrid EAs by combining some recently-developed EAs with a biogeography-based hybridization strategy. We test our hybrid EAs on the continuous optimization benchmarks from the 2013 Congress on Evolutionary Computation (CEC) and on some real-world traveling salesman problems. The...