Best neighbor-guided artificial bee colony algorithm for continuous optimization problems

Peng, Hu; Deng, Changshou; Wu, Zhijian

doi:10.1007/s00500-018-3473-6

Cited by 64 publications

(34 citation statements)

References 43 publications

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“…Simulation studies are done on a computer with C++ and i7 8 GB RAM hardware. For SSEABC, ABC [21], MABC [35], NABC [36], and APABC [37], the results are obtained with 100 independent runs with 7500 function evaluations (FEs) for each sample. A Gaussian white noise signal with 100 samples is applied as input signal to both the unknown system and the IIR filter for each algorithm run.…”

Section: Simulation Resultsmentioning

confidence: 99%

Adaptive iir filter design using self-adaptive search equation based artificial beecolony algorithm

Durmuş¹,

Yavuz²,

Aydın³

2019

Turk J Elec Eng & Comp Sci

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Infinite impulse response (IIR) system identification problem is defined as an IIR filter modeling to represent an unknown system. During a modeling task, unknown system parameters are estimated by metaheuristic algorithms through the IIR filter. This work deals with the self-adaptive search-equation-based artificial bee colony (SSEABC) algorithm that is adapted to optimal IIR filter design. SSEABC algorithm is a recent and improved variant of artificial bee colony (ABC) algorithm in which appropriate search equation is determined with a self-adaptive strategy. Moreover, the success of the SSEABC algorithm enhanced with a competitive local search selection strategy was proved on benchmark functions in our previous studies. The SSEABC algorithm is utilized in filter modelings which have different cases. In order to demonstrate the performance of the SSEABC algorithm on IIR filter design, we have also used canonical ABC, modified ABC (MABC), best neighbor-guided ABC, and an ABC with an adaptive population size (APABC) algorithms as well as other algorithms in the literature for comparison. The obtained results and the analysis on performance evolution of compared algorithms on several filter design cases indicate that SSEABC outperforms all considered ABC variants and other algorithms in the literature.

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Section: Simulation Resultsmentioning

confidence: 99%

Adaptive iir filter design using self-adaptive search equation based artificial beecolony algorithm

Durmuş¹,

Yavuz²,

Aydın³

2019

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

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“…(1) Begin (2) / * Build classification model based on DMSDL-QBSA-RF * / (3) Initialize the positions of N birds using equations (16) and (17): X i (i � 1, 2, ..., N); (4) Calculated fitness: f(X i )(i � 1, 2, ..., N); set X i to be P i and find P gbest ; (5) While iter < iter max + 1 do (6) For i � 1: n tree (7) Give each tree a training set of size N by random sampling with replacement based on Bootstrap; (8) Select m try attributes randomly at each leaf node, compare the attributes, and select the best one; (9) Recursively generate each decision tree without pruning operations; (10) End For (11) Update classification accuracy of RF: Evaluate f(X i ); (12) Update gbest and P gbest ; (13) [n best , m best ] � gbest; (14) iter � iter + 1; (15) End While (16) / * Classify using RF model * / (17) For i � 1: n best (18) Give each tree a training set of size N by random sampling with replacement based on Bootstrap; (19) Select m best attributes randomly at each leaf node, compare the attributes, and select the best one; (20) Recursively generate each decision tree without pruning operations; (21) End (22) Return DMSDL-QBSA-RF classification model (23) Classify the test dataset using equation 20; (24) Calculate OOB error; (25) End ALGORITHM 2: DMSDL-QBSA-RF classification model.…”

Section: Practical Applicationmentioning

confidence: 99%

“…The simulation results have shown that the proposed approach has the best performance in some complex functions. Peng et al [ 18 ] also developed a hybrid approach, which is using the best neighbor-guided solution search strategy to search ABC algorithm. The experimental results indicate that the proposed ABC is very competitive and outperforms the other algorithms.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Multi-Swarm Differential Learning Quantum Bird Swarm Algorithm and Its Application in Random Forest Classification Model

Zhang

Xia

et al. 2020

Computational Intelligence and Neuroscience

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Bird swarm algorithm is one of the swarm intelligence algorithms proposed recently. However, the original bird swarm algorithm has some drawbacks, such as easy to fall into local optimum and slow convergence speed. To overcome these short-comings, a dynamic multi-swarm differential learning quantum bird swarm algorithm which combines three hybrid strategies was established. First, establishing a dynamic multi-swarm bird swarm algorithm and the differential evolution strategy was adopted to enhance the randomness of the foraging behavior’s movement, which can make the bird swarm algorithm have a stronger global exploration capability. Next, quantum behavior was introduced into the bird swarm algorithm for more efficient search solution space. Then, the improved bird swarm algorithm is used to optimize the number of decision trees and the number of predictor variables on the random forest classification model. In the experiment, the 18 benchmark functions, 30 CEC2014 functions, and the 8 UCI datasets are tested to show that the improved algorithm and model are very competitive and outperform the other algorithms and models. Finally, the effective random forest classification model was applied to actual oil logging prediction. As the experimental results show, the three strategies can significantly boost the performance of the bird swarm algorithm and the proposed learning scheme can guarantee a more stable random forest classification model with higher accuracy and efficiency compared to others.

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“…In summary, our scheme significantly outperforms the existing method in almost all aspects. Therefore, our scheme may be applied in many scenarios with range query, such as cloud computing [37], [38], recommendation system [39], real-time graph stream [40], ciphertext query [41], [42], privacy-preserving for location-based services [43], continuous optimization [44], energy scheduling and optimization [45], etc.…”

Section: Comparison Of Search Efficiencymentioning

confidence: 99%

A Novel Range Search Scheme Based on Frequent Computing for Edge-Cloud Collaborative Computing in CPSS

Cui

Yang

et al. 2020

IEEE Access

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Due to the rapid advances of Information and Communication Technologies (ICT), especially 5G and Artificial Intelligence (AI), the Internet of Everything is gradually becoming a reality, and human beings living environments are becoming smarter and smarter. Every day there will be generated large amounts of data in Humans-Machines-Things hybrid space, which is also called Cyber-Physical-Social Systems (CPSSs). Today, the city we live in has become a data-driven society. However, how to effectively mine valuable information from these massive data to provide proactive and personalized services for human beings is a challenging problem. Thus, top-k search remains an important topic of ongoing research. In this paper, we focus on a basic problem of geo-tagged data: find the top-k frequent terms among the geo-tagged data in a specific region from the cloud. We first construct a Region Tree Index (RTI) for geo-tagged data. Then the list storage structure is proposed to Store Sorted Terms and Weights (SSTW) in RTI. And then an efficient kTermsSearch algorithm is presented to compute top-k frequent terms in a given region. Finally, extensive experiments verify the validity of the proposed scheme. INDEX TERMS Cyber-physical-social system (CPSS), frequent computing, top-k search, edge-cloud collaborative computing.

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Best neighbor-guided artificial bee colony algorithm for continuous optimization problems

Cited by 64 publications

References 43 publications

Adaptive iir filter design using self-adaptive search equation based artificial beecolony algorithm

Adaptive iir filter design using self-adaptive search equation based artificial beecolony algorithm

Dynamic Multi-Swarm Differential Learning Quantum Bird Swarm Algorithm and Its Application in Random Forest Classification Model

A Novel Range Search Scheme Based on Frequent Computing for Edge-Cloud Collaborative Computing in CPSS

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