Blended biogeography-based optimization for constrained optimization

Ma, Haiping; Simon, Dan

doi:10.1016/j.engappai.2010.08.005

Cited by 256 publications

(105 citation statements)

References 26 publications

(25 reference statements)

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“…For example, the blended-BBO which is presented in [14] can be a great addonto this modified PMB-BBO. Furthermore, even if it is required to employ the complicated models of immigration and emigration rates, such as the generalized sinusoidal migration modelin [16].The generalized sinusoidal migration model is based on an old study that was done by James A. MacMahon in 1987 [19], and was mentioned in ch.3 of [4].…”

Section: Conclusion and Suggestionsmentioning

confidence: 99%

Essential Modifications on Biogeography-Based Optimization Algorithm

Al-Roomi¹,

Albasri²,

Talaq³

2013

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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Biogeography-based optimization (BBO) is a new population-based evolutionary algorithm and is based on an old theory of island biogeography that explains the geographical distribution of biological organisms. BBO was introduced in 2008 and then a lot of modifications were employed to enhance its performance. This paper proposes two modifications; firstly, modifying the probabilistic selection process of the migration and mutation stages to give a fairly randomized selection for all the features of the islands. Secondly, the clear duplication process after the mutation stage is sized to avoid any corruption on the suitability index variables. The obtained results through wide variety range of test functions with differentdimensions and complexities proved that the BBO performance can be enhanced effectively without using any complicated form of the immigration and emigration rates. This essential modification has to be considered as an initial step for any other modification.

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Section: Conclusion and Suggestionsmentioning

confidence: 99%

Essential Modifications on Biogeography-Based Optimization Algorithm

Al-Roomi¹,

Albasri²,

Talaq³

2013

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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“…Typically, Haiping Ma [21] improved the classical BBO algorithm and analyzes the equilibrium of migration models. Haiping Ma and Dan Simon [22] discussed migration models using markov theory and blended BBO for constrained optimization. In real, BBO maps these factors as suitability index variable (SIV) and habitat suitability index (HSI) to mathematical solution space in order to find the optimal solution of a certain problem.…”

Section: Related Workmentioning

confidence: 99%

Multi-Objective Biogeography-Based Method to Optimize Virtual Machine Consolidation

Shi

Luo³

et al. 2016

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-Virtual machine consolidation (VMC) is an important issue in cloud computing, which can be used to reduce power consumption and achieve reasonable resource allocation. In this paper, an IMBBO algorithm is proposed to solve the multi-objective optimization problem of VMC through improving the classical Biogeography-Based Optimization (BBO). An improved Cosine migration model and an improved mutation model are presented to increase the efficiency of achieving the optimal solution. Meanwhile, three optimization objectives for server power consumption, load balancing and migration resource overhead are mainly addressed. Finally, several experiments are done to evaluate the performance of IMBBO by comparing with Gravitational Search Algorithm (GSA) based on the synthetic and real VM running data. The results show that the IMBBO optimizes VM consolidation with higher efficiency.

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“…The idea inspired by blended migration operator [23], the coefficient of solution H i is a constant. In our algorithm, along the number of iterations increases, solution H i is much fitter than solution H j , and solution H i is more affected by itself.…”

Section: Migration Operator For Ssd Modelmentioning

confidence: 99%

Biogeography-Based Optimization of the Portfolio Optimization Problem with Second Order Stochastic Dominance Constraints

2017

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Abstract:The portfolio optimization problem is the central problem of modern economics and decision theory; there is the Mean-Variance Model and Stochastic Dominance Model for solving this problem. In this paper, based on the second order stochastic dominance constraints, we propose the improved biogeography-based optimization algorithm to optimize the portfolio, which we called εBBO. In order to test the computing power of εBBO, we carry out two numerical experiments in several kinds of constraints. In experiment 1, comparing the Stochastic Approximation (SA) method with the Level Function (LF) algorithm and Genetic Algorithm (GA), we get a similar optimal solution by εBBO in [0, 0.6] and [0, 1] constraints with the return of 1.174% and 1.178%. In [−1, 2] constraint, we get the optimal return of 1.3043% by εBBO, while the return of SA and LF is 1.23% and 1.26%. In experiment 2, we get the optimal return of 0.1325% and 0.3197% by εBBO in [0, 0.1] and [−0.05, 0.15] constraints. As a comparison, the return of FTSE100 Index portfolio is 0.0937%. The results prove that εBBO algorithm has great potential in the field of financial decision-making, it also shows that εBBO algorithm has a better performance in optimization problem.

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Blended biogeography-based optimization for constrained optimization

Cited by 256 publications

References 26 publications

Essential Modifications on Biogeography-Based Optimization Algorithm

Essential Modifications on Biogeography-Based Optimization Algorithm

Multi-Objective Biogeography-Based Method to Optimize Virtual Machine Consolidation

Biogeography-Based Optimization of the Portfolio Optimization Problem with Second Order Stochastic Dominance Constraints

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