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Information 2017, 8(3), 83; doi:10.3390/info8030083

Hybridizing Adaptive Biogeography-Based Optimization with Differential Evolution for Multi-Objective Optimization Problems

1
College of Information Science&Technology, Hainan University, No. 58 Renmin Avenue, Haikou 570228, China
2
State Key Laboratory of Marine Resource Utilization in the South China Sea, Hainan University, No. 58 Renmin Avenue, Haikou 570228, China
*
Author to whom correspondence should be addressed.
Received: 22 May 2017 / Revised: 5 July 2017 / Accepted: 11 July 2017 / Published: 14 July 2017
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Abstract

In order to improve the performance of optimization, we apply a hybridization of adaptive biogeography-based optimization (BBO) algorithm and differential evolution (DE) to multi-objective optimization problems (MOPs). A model of multi-objective evolutionary algorithms (MOEAs) is established, in which the habitat suitability index (HSI) is redefined, based on the Pareto dominance relation, and density information among the habitat individuals. Then, we design a new algorithm, in which the modification probability and mutation probability are changed, according to the relation between the cost of fitness function of randomly selected habitats of last generation, and average cost of fitness function of all habitats of last generation. The mutation operators based on DE algorithm, are modified, and the migration operators based on number of iterations, are improved to achieve better convergence performance. Numerical experiments on different ZDT and DTLZ benchmark functions are performed, and the results demonstrate that the proposed MABBO algorithm has better performance on the convergence and the distribution properties comparing to the other MOEAs, and can solve more complex multi-objective optimization problems efficiently. View Full-Text
Keywords: multi-objective optimization; MABBO; adaptive biogeography-based optimization; differential evolution multi-objective optimization; MABBO; adaptive biogeography-based optimization; differential evolution
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Feng, S.; Yang, Z.; Huang, M. Hybridizing Adaptive Biogeography-Based Optimization with Differential Evolution for Multi-Objective Optimization Problems. Information 2017, 8, 83.

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