Hybrid biogeography-based optimization with enhanced mutation and CMA-ES for global optimization problem

Zhao, Fuqing; Du, Songlin; Zhang, Yi; Ma, Weimin; Song, Houbin

doi:10.1007/s11761-019-00284-8

Cited by 9 publications

(6 citation statements)

References 18 publications

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“…Here, the habitat will be represented by means of a permutation of integers. This is generated randomly and is inserted to it for avoiding duplicate habitats and enhancing the chances of its diversity 18 …”

Section: Methodsmentioning

confidence: 99%

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A trust‐based mechanism in multipath routing using Biogeography‐Shun Optimization

Govindaraj¹,

Arumugam²

2021

Int J Communication

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Summary In mobile ad hoc network, a group of mobile nodes interconnect with each other in an ad hoc manner without any centralized administration. Various applications are based on integrating mobile ad hoc network and cloud computing to enhance the processing and storing of mobile nodes. Cloud service providers provide mobile ad hoc network users access to applications and other mobile ad hoc network users. Implementing security mechanism for ad hoc routing can be a daunting task owing it its unique network traits. Blackhole attack found in the mobile ad hoc network takes place when malicious nodes attract data packets using a fresh destination route and drop the packets. In this work, a trust‐based routing that has a probability of a new packet forwarding is proposed to mitigate blackhole attacks. The ad hoc on‐demand multipath distance vector routing protocol is used in this work, which is based on the trust model that measures the trust of nodes and composes a decision, respectively. A novel optimization technique that has its basis in the Biogeography‐Shun Optimization algorithm is suggested for enhancing the multipath routing. Simulations demonstrate that the proposed method attains better performance by enhancing lifetime of network and packet delivery ratio and reducing end‐to‐end delay.

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Section: Methodsmentioning

confidence: 99%

“…This is generated randomly and is inserted to it for avoiding duplicate habitats and enhancing the chances of its diversity. 18…”

Section: Initializationmentioning

confidence: 99%

A trust‐based mechanism in multipath routing using Biogeography‐Shun Optimization

Govindaraj¹,

Arumugam²

2021

Int J Communication

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“…Invasive weed optimization algorithm is a random search algorithm that simulates the process of weed colonization in na- 2006; Giri et al, 2010). Compared with particle swarm algorithm, weed algorithm only needs smaller storage space when memorizing the trajectory of particles (Nguyen et al, 2020;Zhao et al, 2020;Mohammadi and Khodaygan, 2020), compared with genetic algorithm and other evolutionary algorithms, the algorithm programming is easy to implement, and it is easy to realize without genetic algorithm. In the case of operating operators, it can easily and effectively converge to the optimal solution of the problem.…”

Section: Invasive Weed Algorithmmentioning

confidence: 99%

Research on Optimization Operation Technology of Qt Oil Pipeline Based on the Heuristic Algorithm

et al. 2022

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“…As merit, the HSA (similar to the GA algorithm) possesses a genetic pool to store the solutions. More details, especially mathematical descriptions of the mentioned algorithms can be found in earlier studies (BBO [16,134,135], IWO [136][137][138], SOSA [139], SFLA [140,141], and HSA [142][143][144]).…”

Section: Mehrabian and Lucasmentioning

confidence: 99%

Developing Invasive Weed, Social Spider, Shuffled Frog Leaping, Biogeography-Based, and Harmony Search Optimization Algorithms for the Early Prediction of Residential Building’s Cooling Load Simulation

Moayedi¹,

Mosavi²

2021

Preprint

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Regarding the high efficiency of metaheuristic techniques in energy performance analysis, this paper scrutinizes and compares five novel optimizers, namely biogeography-based optimization (BBO), invasive weed optimization (IWO), social spider algorithm (SOSA), shuffled frog leaping algorithm (SFLA), and harmony search algorithm (HSA) for the early prediction of cooling load in residential buildings. The algorithms are coupled with a multi-layer perceptron (MLP) to adjust the neural parameters that connect the CL with the influential factors. The complexity of the models is optimized by means of a trial-and-error effort, and it was shown that the BBO and IWO need more crowded spaces for fulfilling the optimization. The results revealed that the internal parameters (i.e., biases and weights) suggested by the BBO generate the most reliable MLP for both analyzing and generalizing the CL pattern (with nearly 93 and 92% correlations, respectively). Followed by this, the IWO emerged as the second powerful optimizer with mean absolute errors of 1.8632 and 1.9110 in the training and testing phases. Therefore, the BBO-MLP and IWO-MLP can be reliably used for accurate analysis of the CL in future projects.

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Hybrid biogeography-based optimization with enhanced mutation and CMA-ES for global optimization problem

Cited by 9 publications

References 18 publications

A trust‐based mechanism in multipath routing using Biogeography‐Shun Optimization

A trust‐based mechanism in multipath routing using Biogeography‐Shun Optimization

Research on Optimization Operation Technology of Qt Oil Pipeline Based on the Heuristic Algorithm

Developing Invasive Weed, Social Spider, Shuffled Frog Leaping, Biogeography-Based, and Harmony Search Optimization Algorithms for the Early Prediction of Residential Building’s Cooling Load Simulation

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