Enhancing artificial bee colony algorithm with multi-elite guidance

Zhou, Xinyu; Lu, Jiaxin; Huang, Junhong; Zhong, Maosheng; Wang, Mingwen

doi:10.1016/j.ins.2020.07.037

Cited by 57 publications

(20 citation statements)

References 38 publications

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“…The introduction of the ABC algorithm ( Zhou et al, 2021 ) improves the original BP neural network structure. The target output value of the ABC algorithm is used as the weight and threshold of the BP neural network.…”

Section: Music Emotion Recognition Based On Improved Back Propagation Networkmentioning

confidence: 99%

A Novel Music Emotion Recognition Model Using Neural Network Technology

Yang¹

2021

Front. Psychol.

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Music plays an extremely important role in people’s production and life. The amount of music is growing rapidly. At the same time, the demand for music organization, classification, and retrieval is also increasing. Paying more attention to the emotional expression of creators and the psychological characteristics of music are also indispensable personalized needs of users. The existing music emotion recognition (MER) methods have the following two challenges. First, the emotional color conveyed by the first music is constantly changing with the playback of the music, and it is difficult to accurately express the ups and downs of music emotion based on the analysis of the entire music. Second, it is difficult to analyze music emotions based on the pitch, length, and intensity of the notes, which can hardly reflect the soul and connotation of music. In this paper, an improved back propagation (BP) algorithm neural network is used to analyze music data. Because the traditional BP network tends to fall into local solutions, the selection of initial weights and thresholds directly affects the training effect. This paper introduces artificial bee colony (ABC) algorithm to improve the structure of BP neural network. The output value of the ABC algorithm is used as the weight and threshold of the BP neural network. The ABC algorithm is responsible for adjusting the weights and thresholds, and feeds back the optimal weights and thresholds to the BP neural network system. BP neural network with ABC algorithm can improve the global search ability of the BP network, while reducing the probability of the BP network falling into the local optimal solution, and the convergence speed is faster. Through experiments on public music data sets, the experimental results show that compared with other comparative models, the MER method used in this paper has better recognition effect and faster recognition speed.

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Section: Music Emotion Recognition Based On Improved Back Propagation Networkmentioning

confidence: 99%

A Novel Music Emotion Recognition Model Using Neural Network Technology

Yang¹

2021

Front. Psychol.

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“…Enhancing artificial bee colony algorithm with multi-elite guidance (MGABC) [7] Artificial bee colony algorithm with an adaptive greedy position update strategy (AABC) [8] Multi-strategy ensemble ABC algorithm (MEABC) [9] Self-adaptive heterogeneous PSO (fk-PSO) [ 10 presents the mean error function values achieved by the six algorithms on the test suite.…”

Section: Complete Experiments Of Synthesis Model Through 28 Cec2013 Test Functionmentioning

confidence: 99%

“…This paper focuses on the balance strategies of the global search ability and local search ability. Therefore, the proposed algorithm should compare with the particular algorithms which have balance strategies of the global search ability and local search ability rather than all the algorithms, such as Enhancing artificial bee colony algorithm with multi-elite guidance (MGABC) [7], Artificial bee colony algorithm with an adaptive greedy position update strategy (AABC) [8],Multi-strategy ensemble artificial bee colony (MEABC) [9], Self-adaptive heterogeneous PSO (fk-PSO) [10].…”

Section: Introductionmentioning

confidence: 99%

Research on a new optimization algorithm simulating multi- states of matter inspired by finite element analysis approach

2021

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A new optimization algorithm is proposed, since a huge problem that many algorithms faced was not being able to effectively balance the global and local search ability. Matter exists in three states: solid, liquid, and gas, which presents different motion characteristics. Inspired by multi-states of matter, individuals of optimization algorithm have different motion characteristics of matter, which could present different search ability. The Finite Element Analysis (FEA) approach can simulate multi-states of matter, which can be adopted to effectively balance the global search ability and local search ability in new optimization algorithm. The new algorithm is creative application of Finite Element Analysis at optimization algorithm field. Artificial Physics Optimization (APO) and Gravitational Search Algorithm (GSA) belongs to the algorithm types defined by force and mass. According to FEA approach, node displacement caused by force and stiffness could be equivalent to motion caused by force and mass of APO and GSA. In the new algorithm framework, stiffness replaces mass of APO and GSA algorithm. This paper performs research on two different algorithms based on APO and GSA respectively. The individuals of new optimization algorithm are divided into solid state, liquid state, and gas state. The effects of main parameters on the performance were studied through experiments of 6 static test functions. The performance is compared with PSO, basic APO, or GSA for four complex models which made up of solid individual, liquid individual, and gas individual in iterative process. The reasonable complex model can be confirmed experimentally. Based on the reasonable complex model, the article conducted complete experiments against Enhancing artificial bee colony algorithm with multi-elite guidance (MGABC), Artificial bee colony algorithm with an adaptive greedy position update strategy (AABC), Multi-strategy ensemble artificial bee colony (MEABC), Self-adaptive heterogeneous PSO (fk-PSO), and APO with 28 CEC2013 test problem. Experimental results show that the proposed method achieves a good performance in comparison to its counterparts as a consequence of its better exploration-exploitation balance. The algorithm supplies a new method to improve physics optimization algorithm.

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“…Hence, many modifications have been introduced into the basic ABC for gaining better exploitation, 25–28 and most of them share the similar idea of utilizing the global best individual or some elite individuals. There is no doubt that the reported experimental results verified the feasibility of the idea, that is, the performance of ABC can be effectively enhanced by utilizing the elite information.…”

Section: Introductionmentioning

confidence: 99%

Artificial bee colony algorithm with bi‐coordinate systems for global numerical optimization

Zhou

Huang

Tang

et al. 2022

Int J of Intelligent Sys

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As an effective global optimization technique, artificial bee colony (ABC) algorithm has become one of the hottest research topics in the fields of evolutionary algorithms. However, the solution search equation is not rotationally invariant, which causes the problem that the performance of ABC is sensitive to the coordinate system. Although many improved ABC variants have been developed, they rarely considered the problem. Hence, to solve the problem, we propose a new ABC variant with bi‐coordinate systems (BSABC), including the original coordinate system and the eigen coordinate system. The two coordinate systems own different characteristics: (1) the former one aims to maintain the population diversity, and (2) the latter one is to adapt the search to the fitness landscape of the problems. Based on the characteristics, in the BSABC, the two coordinate systems are used in the employed bee phase and onlooker bee phase, respectively. Meanwhile, two new solution search equations are designed by utilizing the elite information, and they are respectively performed in the two coordinate systems to further improve the algorithm performance. As another contribution of this study, in the scout bee phase, the multivariate Gaussian distribution is constructed to replace the original method to generate offspring, which is helpful to save the search experience. The performance of the BSABC is verified by extensive experiments on the CEC2013 test suite and one real‐world optimization problem, and four well‐established ABC variants and three other evolutionary algorithms are included in the performance comparison. The comparison results confirm that the BSABC shows competitive performance by achieving better results on the majority of test functions.

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Enhancing artificial bee colony algorithm with multi-elite guidance

Cited by 57 publications

References 38 publications

A Novel Music Emotion Recognition Model Using Neural Network Technology

A Novel Music Emotion Recognition Model Using Neural Network Technology

Research on a new optimization algorithm simulating multi- states of matter inspired by finite element analysis approach

Artificial bee colony algorithm with bi‐coordinate systems for global numerical optimization

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