A hybrid biogeography-based optimization algorithm to solve high-dimensional optimization problems and real-world engineering problems

Zhang, Ziyu; Gao, Yuelin; Liu, Yingchun; Zuo, Wenlu

doi:10.1016/j.asoc.2023.110514

Cited by 6 publications

(6 citation statements)

References 99 publications

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“…Four levels comprise the hierarchy: omega, alpha, beta, and delta [12]. One of the evolutionary algorithms utilized to resolve optimization problems is the GA [13]. The algorithm in question is a direct www.ijacsa.thesai.org replication of Darwin's survival of the fittest and the process of natural evolution [13].…”

Section: A Knowledge Backgroundmentioning

confidence: 99%

“…One of the evolutionary algorithms utilized to resolve optimization problems is the GA [13]. The algorithm in question is a direct www.ijacsa.thesai.org replication of Darwin's survival of the fittest and the process of natural evolution [13]. An initial population of randomly generated candidate solutions encoded as chromosomes is utilized by the algorithm.…”

Section: A Knowledge Backgroundmentioning

confidence: 99%

“…An initial population of randomly generated candidate solutions encoded as chromosomes is utilized by the algorithm. By applying the principle of survival of the fittest to generate ever-improving approximations, the solution to the inverse problem may be to gradually identify the elite individual attained through progress [13]. The BBO algorithm, introduced by Dan Simon in 2008 [14], is a novel population-based meta-heuristic algorithm that drew inspiration from the migration of species across various islands in search of a suitable habitat [14].…”

Section: A Knowledge Backgroundmentioning

confidence: 99%

“…This algorithm utilizes the habitat suitability index (HSI) to quantify the quality of the homeland (solution); a solution with a high HSI is deemed to be good, whereas one with a low HSI is deemed to be poor [14]. This optimizer has been widely utilized in different tasks [13], [15], [16], [17], [18], [19].…”

Section: A Knowledge Backgroundmentioning

confidence: 99%

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Stock Market Volatility Estimation: A Case Study of the Hang Seng Index

Wu,

Lin,

liu

2024

ijacsa

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Among the influential elements in the national economy is the stock market. The stock market is a multifaceted system that combines economics, investor psychology, and other market mechanics. The objective of the financial market investment is to maximize profits; but, due to the market's complexity and the multitude of factors that might impact it, it is challenging to predict its future behavior. The challenging process of stock price prediction requires the analysis of a wide range of social, political, and economic factors. These variables include market trends, financial statements, earnings reports, and other data. The goal of this project is to develop an accurate hybrid stock price forecasting model using Random Forest which is combined with the optimization. Random Forest is one type of machine learning that is often used in time series analysis. This study provides stock price forecasting using the Hang Seng index market, which consists of the largest and most liquid corporations that are publicly traded on the Hong Kong Stock Exchange, data from 2015 to 2023. The Dow Jones and KOSPI were evaluated as two additional indices. This study demonstrates some optimization approaches including genetic algorithm, grey wolf optimization, and biogeography-based optimization, which drew inspiration from the phenomenon of species migrating between islands in search of a suitable habitat. Biogeography-based optimization has shown the best result among these optimizations. The proposed hybrid model obtained the values 0.992, 0.997, and 0.9937 for the coefficient of determination for HSI, Dow Jones, and KOSPI markets, respectively. These results indicate the ability of the model in order to predict the stock market with a high degree of accuracy.

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Section: A Knowledge Backgroundmentioning

confidence: 99%

Section: A Knowledge Backgroundmentioning

confidence: 99%

Section: A Knowledge Backgroundmentioning

confidence: 99%

Section: A Knowledge Backgroundmentioning

confidence: 99%

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Stock Market Volatility Estimation: A Case Study of the Hang Seng Index

Wu,

Lin,

liu

2024

ijacsa

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“…Then, Sang et al [36] proposed DCGBBO by a hierarchical tissue-like P system with triggering ablation rules, making use of the evolution rule and communication rule to achieve migration and mutation, which reduces the computational complexity. Recently, to enhance the overall performance of BBO algorithm, [37] designed a novel BBO variant with hybrid migration operator and feedback differential evolution mechanism, referred to as HFBBO. It is a "living algorithm" that can self-regulate the mutation mode.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Multi-Strategy Enhanced Biogeography-Based Optimization Algorithm for High-Dimensional Optimization and Engineering Design Problems

Gao,

Li,

Gao

et al. 2024

Mathematics

Self Cite

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The biogeography-based optimization (BBO) algorithm is known for its simplicity and low computational overhead, but it often struggles with falling into local optima and slow convergence speed. Against this background, this work presents a multi-strategy enhanced BBO variant, named MSBBO. Firstly, the example chasing strategy is proposed to eliminate the destruction of the inferior solutions to superior solutions. Secondly, the heuristic crossover strategy is designed to enhance the search ability of the population. Finally, the prey search–attack strategy is used to balance the exploration and exploitation. To verify the performance of MSBBO, we compare it with standard BBO, seven BBO variants (PRBBO, BBOSB, HGBBO, FABBO, BLEHO, MPBBO and BBOIMAM) and seven meta-heuristic algorithms (GWO, WOA, SSA, ChOA, MPA, GJO and BWO) on multiple dimensions of 24 benchmark functions. It concludes that MSBBO significantly outperforms all competitors both on convergence accuracy, speed and stability, and MSBBO basically converges to the same results on 10,000 dimensions as on 1000 dimensions. Further, MSBBO is applied to six real-world engineering design problems. The experimental results show that our work is still more competitive than other latest optimization techniques (COA, EDO, OMA, SHO and SCSO) on constrained optimization problems.

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Solving numerical and engineering optimization problems using a dynamic dual-population differential evolution algorithm

Zuo,

Gao

2024

Int. J. Mach. Learn. & Cyber.

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A hybrid biogeography-based optimization algorithm to solve high-dimensional optimization problems and real-world engineering problems

Cited by 6 publications

References 99 publications

Stock Market Volatility Estimation: A Case Study of the Hang Seng Index

Stock Market Volatility Estimation: A Case Study of the Hang Seng Index

A Comprehensive Multi-Strategy Enhanced Biogeography-Based Optimization Algorithm for High-Dimensional Optimization and Engineering Design Problems

Solving numerical and engineering optimization problems using a dynamic dual-population differential evolution algorithm

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