Multiobjective Biogeography-Based Optimization Based on Predator-Prey Approach

Silva, Marsil A. Costa e; Coelho, Leandro dos Santos; Lebensztajn, Luiz

doi:10.1109/tmag.2011.2174205

Cited by 66 publications

(14 citation statements)

References 13 publications

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“…An efficient multi-objective optimization algorithm using the differential evolution (DE) algorithm is proposed to solve multi-objective optimal power flow (MO-OPF) problems [37]. BBO has also been modified to solve multi-objective optimization problems (MOPs) [38][39][40][41][42][43][44][45], such as, multi-objective biogeography-based optimization based on predator-prey approach [38], indoor wireless heterogeneous networks planning [39], automated warehouse scheduling [40], and community detection in social networks with node attributes [41]. Work in the literature [42] is focused on numerical comparisons of migration models for multi-objective biogeography-based optimization.…”

Section: Literature Reviewmentioning

confidence: 99%

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

2017

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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. distribution of solutions [6]. Second, there are several good results based on the hybridization of BBO with differential evolution (DE) [8,9]. Last, blend combination operators have been widely and successfully used in other population-based optimization algorithms [10].The original contributions of this paper include the following. Firstly, the model of applying BBO for multi-objective evolutionary algorithm was established, in which the habitat suitability index is redefined, based on the Pareto dominance relation and density information among the habitat individuals. Motivated by the work in [11,12], we hybridize adaptive BBO with DE to design a new algorithm for multi-objective optimization problems. In this algorithm, the mutation operator based on DE algorithm was modified, and the migration operator based on the number of iterations was re-designed to achieve better performance. Furthermore, the modification probability and mutation probability are adaptively 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. Experiments performed on ZDT and DTLZ benchmark functions show that the obtained Pareto solution set can approximate to the Pareto optimal front, and has good diversity and uniform distribution. The results also demonstrate that the proposed MABBO algorithm has better performance on the convergence and the distribution.The paper is organized as follows. Section 2 describes the related work about BBO algorithm. The basic theories of multi-objective optimization are presented in Section 3.The MABBO algorithm procedure for multi-objective optimization problems are proposed in Section 4. Section 5 shows the simulation and experiment results. ...

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Section: Literature Reviewmentioning

confidence: 99%

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

2017

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“…Several bio-inspired swarm intelligent optimization algorithms have been applied to the design of the BLDC motors, such as Bat Algorithm (BA) [6], Predator-Prey Biogeography-Based Optimization (PPBBO) [7], Multi-Objective Particle Swarm Optimization (MOPSO) [8], the modified Non-dominated Sorting Genetic Algorithm (NSGA-II), and Sequential Quadratic Programming (SQP) [9], respectively.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective pigeon-inspired optimization for brushless direct current motor parameter design

Qiu

Duan

2015

Sci. China Technol. Sci.

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Pigeon-inspired optimization (PIO) is a new swarm intelligence optimization algorithm, which is inspired by the behavior of homing pigeons. A variant of pigeon-inspired optimization named multi-objective pigeon-inspired optimization (MPIO) is proposed in this paper. It is also adopted to solve the multi-objective optimization problems in designing the parameters of brushless direct current motors, which has two objective variables, five design variables, and five constraint variables. Furthermore, comparative experimental results with the modified non-dominated sorting genetic algorithm are given to show the feasibility, validity and superiority of our proposed MIPO algorithm. brushless direct current (BLDC) motor, multi-objective pigeon-inspired optimization (MPIO), electromagnetics, multi-objective optimization Citation:Qiu H X, Duan H B. Multi-objective pigeon-inspired optimization for brushless direct current motor parameter design.

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“…BBO has demonstrated good performance on various benchmark functions and real-world optimization problems (Ma and Simon, 2011). BBO has also been modified to solve multi-objective optimization problems (MOPs) (Chutima and Wong, 2014;Costa e Silva et al, 2012;Jamuna and Swarup, 2012;Ma et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Ensemble multi-objective biogeography-based optimization with application to automated warehouse scheduling

Simon

et al. 2015

Engineering Applications of Artificial Intelligence

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a b s t r a c tThis paper proposes an ensemble multi-objective biogeography-based optimization (EMBBO) algorithm, which is inspired by ensemble learning, to solve the automated warehouse scheduling problem. First, a real-world automated warehouse scheduling problem is formulated as a constrained multi-objective optimization problem. Then EMBBO is formulated as a combination of several multi-objective biogeography-based optimization (MBBO) algorithms, including vector evaluated biogeography-based optimization (VEBBO), non-dominated sorting biogeography-based optimization (NSBBO), and niched Pareto biogeography-based optimization (NPBBO). Performance is tested on a set of 10 unconstrained multi-objective benchmark functions and 10 constrained multi-objective benchmark functions from the 2009 Congress on Evolutionary Computation (CEC), and compared with single constituent MBBO and CEC competition algorithms. Results show that EMBBO is better than its constituent algorithms, and among the best CEC competition algorithms, for the benchmark functions studied in this paper. Finally, EMBBO is successfully applied to the automated warehouse scheduling problem, and the results show that EMBBO is a competitive algorithm for automated warehouse scheduling.

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Multiobjective Biogeography-Based Optimization Based on Predator-Prey Approach

Cited by 66 publications

References 13 publications

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

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

Multi-objective pigeon-inspired optimization for brushless direct current motor parameter design

Ensemble multi-objective biogeography-based optimization with application to automated warehouse scheduling

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