An electronic transition-based bare bones particle swarm optimization algorithm for high dimensional optimization problems

Tian, Hao; Guo, Jia; Xiao, Heng; Ke, Yan; Sato, Yuji

doi:10.1371/journal.pone.0271925

Cited by 12 publications

(7 citation statements)

References 32 publications

(35 reference statements)

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“…The highest dimension of 100 recommended in the CEC2017 benchmark function is chosen to show the reasonableness of the experiment. Five well-known parameter-free mate-heuristics, BBPSO 35 , PBBPSO 36 , DLSBBPSO 37 , TBBPSO 38 and ETBBPSO 39 are used as comparison groups. To reduce the impact of chance errors on the experimental results, all the trials are attempted 37 times.…”

Section: Resultsmentioning

confidence: 99%

A novel hermit crab optimization algorithm

Guo

Zhou

Ke³

et al. 2023

Sci Rep

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High-dimensional optimization has numerous potential applications in both academia and industry. It is a major challenge for optimization algorithms to generate very accurate solutions in high-dimensional search spaces. However, traditional search tools are prone to dimensional catastrophes and local optima, thus failing to provide high-precision results. To solve these problems, a novel hermit crab optimization algorithm (the HCOA) is introduced in this paper. Inspired by the group behaviour of hermit crabs, the HCOA combines the optimal search and historical path search to balance the depth and breadth searches. In the experimental section of the paper, the HCOA competes with 5 well-known metaheuristic algorithms in the CEC2017 benchmark functions, which contain 29 functions, with 23 of these ranking first. The state of work BPSO-CM is also chosen to compare with the HCOA, and the competition shows that the HCOA has a better performance in the 100-dimensional test of the CEC2017 benchmark functions. All the experimental results demonstrate that the HCOA presents highly accurate and robust results for high-dimensional optimization problems.

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Section: Resultsmentioning

confidence: 99%

A novel hermit crab optimization algorithm

Guo

Zhou

Ke³

et al. 2023

Sci Rep

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“…The CEC2017 [22] benchmark function is selected for simulation experiments. Five well-known mateheuristics BBPSO [23], PBBPSO [24], DLSBBPSO [25], TBBPSO [26], and ETBBPSO [27] are used as comparison groups. In order to reduce the impact of chance errors on experimental results, all trials are attempted 37 times.…”

Section: Experimental Methodsmentioning

confidence: 99%

A Novel Hermit Crab optimization algorithm

Guo

Zhou

et al. 2023

Preprint

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The minimax problem in continuous high-dimensional spaces has been a challenge in optimization. Traditional optimization algorithms cannot balance well between depth search and breadth search in high dimensional search spaces. A new hermit crab optimization algorithm (HCOA) is introduced in this paper to address these problems. Inspired by the population behavior of hermit crabs, the hermit crab optimization algorithm introduces the optimal crab memory and the backtracking search around the memory. Compared with other metaheuristic algorithms, the hermit crab optimization algorithm does not require advanced training or parameter correction and thus can be more quickly employed for different optimization problems. To explore the capabilities of HCOA, the simulation experiment selected CEC2017 as the test function and five well-known optimization algorithms as the control group. Among the 29 benchmark features in the CEC2017, HCAO ranks first in the number of features with 23, and second, third and fifth with two each. Experimental results demonstrate that HCOA present highly accurate and robust results for high-dimensional optimization problems.

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“…Also, new strategies for bare-bones PSO are presented [27], [28]. Tian [29] proposes a new population based method by simulating the behavior of electronics. To facilitate the further development of the algorithm, Guo [30] introduces the strategy of dynamical grouping into the BBPSO in 2017, whose name is DLS-BBPSO.…”

Section: Introductionmentioning

confidence: 99%

A Twinning Memory Bare-Bones Particle Swarm Optimization Algorithm for No-Linear Functions

et al. 2023

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Been trapped by local minimums is an important problem in no-linear optimization problems, which is blocking evolutionary algorithms to find the global optimum. Normally, to increase the optimization accuracy, evolutionary algorithms implement search around the best individual. However, overuse of information from a single individual can lead to a rapid diversity losing of the population, and thus reduce the search ability. To overcome this problem, a twinning memory bare-bones particle swarm optimization (TMBPSO) algorithm is presented in this work. The TMBPSO contains a twining memory storage mechanism (TMSM) and a multiple memory retrieval strategy (MMRS). The TMSM enables an extra storage space to extend the search ability of the particle swarm and the MMRS enhances the local minimum escaping ability of the particle swarm. The particle swarm is endowed with the ability of selfrectification by the cooperation of the TMSM and the MMRS. To verify the search ability of the TMBPSO, the CEC2017 benchmark functions and five state-of-the-art population-based optimization algorithms are selected in experiments. Finally, experimental results confirmed that the TMBPSO can obtain high accurate results for no-linear functions.

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An electronic transition-based bare bones particle swarm optimization algorithm for high dimensional optimization problems

Cited by 12 publications

References 32 publications

A novel hermit crab optimization algorithm

A novel hermit crab optimization algorithm

A Novel Hermit Crab optimization algorithm

A Twinning Memory Bare-Bones Particle Swarm Optimization Algorithm for No-Linear Functions

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