Efficient and merged biogeography-based optimization algorithm for global optimization problems

Zhang, Xinming; Tu, Qiang; Cheng, Jinfeng; Wang, Xia

doi:10.1007/s00500-018-3113-1

Cited by 30 publications

(28 citation statements)

References 54 publications

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“…Divide the monarch butterfly population into two subpopulations, i.e., Subpopulation1 and Subpopulation2. [29,36,53,54] to test the performance of our OPMBO algorithm, which can be rigorous to verify the effectiveness of all of the compared algorithms. The information for 12 benchmark functions is shown in Table 1, where the F is the minimum value (ideal optimal value) of the function.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…If the greedy selection method is adopted in MBO, the population at each generation is just sorted once. Thus, the elitist strategy can be replaced by the greedy selection method in the SIO algorithms [53]. Hence, during each generation, the new generated monarch butterflies are compared with the corresponding old ones, and the better one is selected.…”

Section: Random Local Perturbation-based Migration Operatormentioning

confidence: 99%

“…The two state-ofthe-art algorithms, the MBO [29] and the GCMBO [36], are selected as the comparison algorithms to evaluate the effectiveness of the OPMBO. Following the experimental techniques developed by Wang et al [36] and Zhang et al [53], the three dimensions of the functions are set as follows: the low-dimensional (20 dimensions), the mediumdimensional (50 dimensions), and the high-dimensional (100 dimensions). As the dimension increases, the difficulty of the problem increases, which verifies that OPMBO has the ability to handle the complex optimization problems.…”

Section: Comparison Of Opmbo With Mbo and Gcmbo Onmentioning

confidence: 99%

“…The experimental results for different dimensions (20,50, and 100 dimensions) on 12 benchmark functions are listed in Tables 3-5, respectively, where the best values are in bold font. Following the experimental techniques designed in [29,36,53,54,60,61], the optimal value as Best, the worst value as Worst, the mean value as Mean, and the standard deviation as Std of the fitness values of benchmark functions for the 30 independent experimental results are employed to test the MBO, GCMBO, and OPMBO algorithms. Here, Zhang et al [53] and Wang et al [36] declared that the lower Mean and Std values indicate a better algorithm with respect to search ability and stability in their experimental analysis.…”

Section: Comparison Of Opmbo With Mbo and Gcmbo Onmentioning

confidence: 99%

“…Sarkhel et al [52] applied OBL to the harmony search algorithm to overcome slow convergence to the globally optimal solutions. Zhang et al [53] merged the OBL into the biogeography-based optimization algorithm to prevent the algorithm from falling into the local optima. Zhang et al [54] added the OBL to the GWO (OGWO) to prevent the algorithm from falling into the local optima.…”

Section: Introductionmentioning

confidence: 99%

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Improved Monarch Butterfly Optimization Algorithm Based on Opposition‐Based Learning and Random Local Perturbation

Sun

Chen

et al. 2019

Complexity

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Many optimization problems have become increasingly complex, which promotes researches on the improvement of different optimization algorithms. The monarch butterfly optimization (MBO) algorithm has proven to be an effective tool to solve various kinds of optimization problems. However, in the basic MBO algorithm, the search strategy easily falls into local optima, causing premature convergence and poor performance on many complex optimization problems. To solve the issues, this paper develops a novel MBO algorithm based on opposition-based learning (OBL) and random local perturbation (RLP). Firstly, the OBL method is introduced to generate the opposition-based population coming from the original population. By comparing the opposition-based population with the original population, the better individuals are selected and pass to the next generation, and then this process can efficiently prevent the MBO from falling into a local optimum. Secondly, a new RLP is defined and introduced to improve the migration operator. This operation shares the information of excellent individuals and is helpful for guiding some poor individuals toward the optimal solution. A greedy strategy is employed to replace the elitist strategy to eliminate setting the elitist parameter in the basic MBO, and it can reduce a sorting operation and enhance the computational efficiency. Finally, an OBL and RLP-based improved MBO (OPMBO) algorithm with its complexity analysis is developed, following on which many experiments on a series of different dimensional benchmark functions are performed and the OPMBO is applied to clustering optimization on several public data sets. Experimental results demonstrate that the proposed algorithm can achieve the great optimization performance compared with a few state-of-the-art algorithms in most of the test cases.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Section: Random Local Perturbation-based Migration Operatormentioning

confidence: 99%

Section: Comparison Of Opmbo With Mbo and Gcmbo Onmentioning

confidence: 99%

Section: Comparison Of Opmbo With Mbo and Gcmbo Onmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improved Monarch Butterfly Optimization Algorithm Based on Opposition‐Based Learning and Random Local Perturbation

Sun

Chen

et al. 2019

Complexity

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Ant Lion Optimizer Based on Spatial Information

Weng,

Liu,

Zheng

et al. 2024

IEEJ Transactions Elec Engng

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Optimization problems widely exist in different real‐world applications. Ant lion optimizer (ALO) is an effective optimization algorithm that simulates the hunting behavior of antlions, but it will easily fall into local optima. To address this issue, this paper proposes a modified version of ALO called spatial ant lion optimizer (SALO). SALO makes full use of spatial informations of solutions to promote its searchability. It utilizes a distance‐based indicator instead of objective fitness to choose ant lions for updating solutions. Besides, it maintains a candidate enhanced archive by a distance‐based updating approach and implements a hybrid‐enhanced strategy in the archive to search for better solutions. Exhaust experiments are carried out through six classic benchmark functions and seven state‐of‐the‐art compared algorithms, and the results validate the superior performance of SALO. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

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Improved GWO for large-scale function optimization and MLP optimization in cancer identification

Zhang

Wang

Chen

et al. 2019

Neural Comput & Applic

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Efficient and merged biogeography-based optimization algorithm for global optimization problems

Cited by 30 publications

References 54 publications

Improved Monarch Butterfly Optimization Algorithm Based on Opposition‐Based Learning and Random Local Perturbation

Improved Monarch Butterfly Optimization Algorithm Based on Opposition‐Based Learning and Random Local Perturbation

Ant Lion Optimizer Based on Spatial Information

Improved GWO for large-scale function optimization and MLP optimization in cancer identification

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