Multi-objective firefly algorithm based on compensation factor and elite learning

Li, LV; Zhao, Jia; Wang, Jiayuan; Fan, Tanghuai

doi:10.1016/j.future.2018.07.047

Cited by 44 publications

(25 citation statements)

References 22 publications

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“…To verify the performance of LVFA, 12 classic benchmark test functions are used to compare LVFA with FA [17], WSSFA [28], VSSFA [26], MFA [23], RaFA [18] and ApFA [24] in this section. The basic information of each algorithm is shown in Table 5.…”

Section: Comparison With Fa and Its Improved Algorithm 1) Comparismentioning

confidence: 99%

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Firefly Algorithm Based on Level-Based Attracting and Variable Step Size

Zhao

Chen

et al. 2020

IEEE Access

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To address the challenge of local optimization and step size factor parameter setting in the full attraction model of the firefly algorithm (FA), this paper introduces level-based attraction and variable step size to FA. In the level-based attraction model, fireflies are firstly grouped in different levels according to the brightness, and each firefly randomly selects two fireflies from a higher level to learn from. By using the variable step size strategy, the searching step size decreases with the number of iterations. Herein, the level-based attracting model can increase the diversity of individual learning and improve the ability to jump out of local optimization. Meanwhile, dynamic adjustment of step size can balance the detection and development ability of the algorithm and improve the optimization accuracy of the algorithm. To evaluate the effectiveness of the proposed algorithm, comparisons are drawn against optimized FA, particle swarm optimization algorithm, artificial bee colony algorithm and differential evolution algorithm on a variety of test function sets. INDEX TERMS Firefly algorithm, full attraction model, level-based attracting model, variable step size.

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Section: Comparison With Fa and Its Improved Algorithm 1) Comparismentioning

confidence: 99%

“…The firefly algorithm (FA) [15]- [17] is a metaheuristic searching technology proposed by Yang Xin-She in 2008, which simulates the luminous characteristics and attraction behavior of the fireflies. In this algorithm, fireflies are…”

Section: Introductionmentioning

confidence: 99%

Firefly Algorithm Based on Level-Based Attracting and Variable Step Size

Zhao

Chen

et al. 2020

IEEE Access

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“…Therefore, this paper proposes the history record matrix to solve this problem. The specific implementation method is to map the solution generated in the iteration process into a matrix and add the matrix to the history record matrix H , as shown in equations (27) and (28), then updating the history record matrix is completed, as shown in Fig. 8.…”

Section: ) History Record Matrixmentioning

confidence: 99%

“…RMMOCS and MOCS algorithms have the same algorithm framework, and the environment selection mechanism of RMMOCS is same as NSGA-II, so the comparison experiment is conducive to verify the effectiveness of the search strategy based on the record matrix. Then, to further illustrate the performance of proposed algorithm, RMMOCS algorithm will be further compared with the state of art multi-objective optimization algorithms, including multi-objective artificial bee colony algorithm (MOABC) [23], multi-objective firefly algorithm (MOFA) [27], and multi-objective ant colony optimization algorithm (MOACO) [22]. These algorithms are written in C# language.…”

Section: Numerical Experimentsmentioning

confidence: 99%

A Multi-Objective Cuckoo Search Algorithm Based on the Record Matrix for a Mixed-Model Assembly Line Car-Sequencing Problem

Wang

Sheng

et al. 2020

IEEE Access

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In the car-sequencing problem of mixed-model assembly lines, a series of cars with different model types will be put into the assembly line in a certain order considering a variety of goals and constraints. In this paper, a multi-objective cuckoo search algorithm based on the record matrix is proposed to solve this problem. In this algorithm, the factors, including the variation of parts usage rates, variation of workstation workload, idle time, overload time, and model switching cost are considered. The record matrix proposed in this paper is utilized to record the characteristic information of the optimal solutions and historical solutions. Meanwhile, two search strategies based on the record matrix are proposed to enhance the ability of local search and global search in the algorithm. The proposed algorithm is verified by a real case. The results show that the proposed model and algorithm have good results, and they have the potential to address other similar problems. INDEX TERMS Car-sequencing problem, mixed-model assembly line, multi-objective cuckoo search algorithm, record matrix.

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“…Cluster analysis has been widely used in social network analysis, 4,5 statistical analysis, 6 image pattern recognition, 7 Web search, 8 evolutionary computing, and other fields. [9][10][11][12][13][14][15][16][17][18][19][20] Rodriguez and Laio 21 proposed Clustering by fast search and find of density peaks (DPC), based on two assumptions. (1) The cluster center is surrounded by data points with lower density, and (2) the distance between the cluster centers is relatively far.…”

mentioning

confidence: 99%

Density peaks clustering based on circular partition and grid similarity

Zhao

Tang

Fan

et al. 2019

Concurrency and Computation

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Summary In density peaks clustering, its complexity for computing local density and relative distance of samples raises a scalability issue for processing large datasets. To address the issue, density peaks clustering based on circular partition and grid similarity has been proposed. The algorithm partitions the data space into circular grids, with each grid treated as a sample, for determining the number of clusters and searching for the density peaks; then, a new grid similarity is calculated to effectively assign unallocated grids. The proposed circular partition method effectively reduces the number of samples and the computational complexity. Extensive experiments have been conducted on several datasets with arbitrary shapes and scales, and the proposed method outperforms other density peaks clustering variants in terms of clustering accuracy and efficiency.

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Multi-objective firefly algorithm based on compensation factor and elite learning

Cited by 44 publications

References 22 publications

Firefly Algorithm Based on Level-Based Attracting and Variable Step Size

Firefly Algorithm Based on Level-Based Attracting and Variable Step Size

A Multi-Objective Cuckoo Search Algorithm Based on the Record Matrix for a Mixed-Model Assembly Line Car-Sequencing Problem

Density peaks clustering based on circular partition and grid similarity

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