CBSO: a memetic brain storm optimization with chaotic local search

Yu, Yang; Gao, Shangce; Cheng, Shi; Wang, Yirui; Song, Shuangyu; Yuan, Fenggang

doi:10.1007/s12293-017-0247-0

Cited by 133 publications

(38 citation statements)

References 23 publications

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“…In this part, MDBSO is compared with BSO [7] and its variants, including CBSO [21], BSO-OS [18], RGBSO [19], GBSO [20] and ASBSO [23]. The results including mean and standard deviation (Std Dev) are listed in Tables 2 and 3.…”

Section: ) Mdbso Vs Bso Variantsmentioning

confidence: 99%

“…GBSO also adopts the global-best guidance strategy in PSO to modify its mutation mechanism. In our previous work [21], a chaotic local search method is combined with BSO (CBSO) to enhance its search ability and improve the solution quality. Besides the mentioned works, there are many other effective modifications for BSO, In [22], a self-adaptive multiobjective BSO (SMOBSO) is proposed.…”

Section: Introductionmentioning

confidence: 99%

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A Multiple Diversity-Driven Brain Storm Optimization Algorithm With Adaptive Parameters

Gao

Wang

et al. 2019

IEEE Access

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Brain storm optimization (BSO) is a swarm intelligence optimization algorithm which is proven to have practical values in various fields. During these years, many modifications have been facilitated to effectively improve BSO's search performance. So far, these modifications focus on improving the solution quality by applying different clustering methods and learning strategies, in which the population diversity is often neglected. However, in recent studies, population diversity plays a more significant role in designing optimization algorithm. A population that maintains its diversity in a high level can easily obtain better solutions than the one with low level of diversity. Therefore, this paper proposes a control method that evaluates the population diversity of BSO to improve its performance. Two diversity measures, which are known as distance-based diversity and fitness-based diversity, are implemented to realize the adaptation of algorithm parameters. The new algorithm is called multiple diversity-driven BSO (MDBSO). Its performance is verified by CEC2017 benchmark function suit and a neuron model training task. The results demonstrate the effectiveness and efficiency of MDBSO.

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Section: ) Mdbso Vs Bso Variantsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Multiple Diversity-Driven Brain Storm Optimization Algorithm With Adaptive Parameters

Gao

Wang

et al. 2019

IEEE Access

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“…Chaotic local search (CLS) makes use of its randomicity and ergodicity. By combining CLS, many traditional NMH algorithms get significant improvement in their performances, such as chaotic BSO [16], DE combined with CLS [33], [34], and chaotic GSA [35]. They demonstrate that CLS can greatly enhance their search ability and avoid trapping into premature.…”

Section: Introductionmentioning

confidence: 99%

“…Brain storm optimization (BSO) [15] is inspired by the human brainstorming process. CBSO [16] and ASBSO [17] are two kinds of hybridization of BSO with chaotic local search and memory-based selection. At present, there are plenty of researches demonstrating that hybrid methods gain the great success in resolving complex optimization problems.…”

Section: Introductionmentioning

confidence: 99%

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TDSD: A New Evolutionary Algorithm Based on Triple Distinct Search Dynamics

Cai

Wang

et al. 2020

IEEE Access

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Spherical evolution is a recently proposed nature-inspired meta-heuristic algorithm which is proven to have nontrivial efficiency and effectiveness in solving complex optimization problems. However, it has some limitations caused by its inherent scale factor and dimension factor. Hypercube search and chaotic local search are two kinds of effective search mechanisms. To construct an algorithm which has better exploration and exploitation abilities, we propose a novel algorithm which contains triple distinct search dynamics (TDSD), i.e., spherical search, hypercube search and chaotic local search. Effective control among them enhances search performance of TDSD. It is verified on thirty CEC2017 benchmark functions and three real-world optimization problems.

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New PID parameter tuning based on improved dung beetle optimization algorithm

Hu,

Wu,

Zou

2024

Can J Chem Eng

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In this paper, a proportional‐integral‐derivative (PID) controller parameter optimization method based on the improved dung beetle optimization (IDBO) algorithm is proposed, which improves the balance between the global exploration and local exploitation capabilities of the dung beetle optimization (DBO) and significantly enhances the convergence speed and optimization accuracy. Initially, the dung beetle population is initialized using piecewise linear chaotic map (PWLCM) chaotic mapping in order to increase its variety and the DBO algorithm's capacity for global exploration. Furthermore, adaptive weighting in the DBO algorithm is now balanced between the capabilities of local exploitation and global exploration with the addition of adaptive weights. After that, in order to improve the DBO algorithm's capacity for local exploitation, a triangle wandering strategy is included during the dung beetle reproductive phase. Finally, using both Lévy flying wandering and greedy strategy (GS) together make it easier to take advantage of opportunities in both local and global areas. The outcomes of the traditional benchmark function test demonstrate a significant improvement in both convergence speed and optimization accuracy when the particle swarm optimization (PSO), DBO, grey wolf optimization (GWO), and sparrow search algorithm (SSA) algorithms are compared. The performance index function incorporates an overshooting penalty term to prevent the overshooting phenomenon in the control system. Simulation experiments are carried out for the DC motor control system, and the time domain performance, frequency domain performance, and robustness performance of the closed‐loop control system with ZN‐PID, Lambda‐PID, PSO‐PID, and IDBO‐PID rectified PID controller parameters are comparatively analyzed, which verifies the validity and practicability of the IDBO algorithm.

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CBSO: a memetic brain storm optimization with chaotic local search

Cited by 133 publications

References 23 publications

A Multiple Diversity-Driven Brain Storm Optimization Algorithm With Adaptive Parameters

A Multiple Diversity-Driven Brain Storm Optimization Algorithm With Adaptive Parameters

TDSD: A New Evolutionary Algorithm Based on Triple Distinct Search Dynamics

New PID parameter tuning based on improved dung beetle optimization algorithm

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