Analytical and numerical comparisons of biogeography-based optimization and genetic algorithms |
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| Authors: | Dan Simon Rick Rarick Mehmet Ergezer Dawei Du |
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| Affiliation: | 1. School of Energy and Machinery Engineering, Jiangxi University of Science and Technology, Nanchang 330006, PR China;2. School of Materials Science and Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, PR China;3. Key Laboratory of Rare Earth Luminescence Materials and Devices of Jiangxi Province, Ganzhou 341000, PR China;4. Key Laboratory of Development and Application of Ionic Rare Earth Resources (Jiangxi University of Science and Technology), Ministry of Education, Ganzhou 341000, PR China;1. College of Mechanical Engineering, Hunan Institute of Science and Technology, Yueyang 414000, China;2. Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China;3. Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, China;4. State Key Laboratory of Powder Metallurgy, Central South University, Changsha 410083, China |
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| Abstract: | We show that biogeography-based optimization (BBO) is a generalization of a genetic algorithm with global uniform recombination (GA/GUR). Based on the common features of BBO and GA/GUR, we use a previously-derived BBO Markov model to obtain a GA/GUR Markov model. One BBO characteristic which makes it distinctive from GA/GUR is its migration mechanism, which affects selection pressure (i.e., the probability of retaining certain features in the population from one generation to the next). We compare the BBO and GA/GUR algorithms using results from analytical Markov models and continuous optimization benchmark problems. We show that the unique selection pressure provided by BBO generally results in better optimization results for a set of standard benchmark problems. We also present comparisons between BBO and GA/GUR for combinatorial optimization problems, include the traveling salesman, the graph coloring, and the bin packing problems. |
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